CN119919061B - Warehouse performance diagnosis and evaluation method and system - Google Patents
Warehouse performance diagnosis and evaluation method and system Download PDFInfo
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Abstract
The invention belongs to the field of warehouse diagnosis, and particularly relates to a warehouse performance diagnosis evaluation method and system, comprising the steps of acquiring a distributed three-dimensional virtual warehouse by using a three-dimensional modeling algorithm based on different warehouse attributes, combining an enhanced screening scheduling model with an evaluation association index library of a built-in hierarchical association index space to obtain a differential association evaluation index set, obtaining a distributed evaluation result grade cluster by using the association evaluation model configured by the index set and each three-dimensional virtual warehouse, finally performing real-time visual mapping on an evaluation result to a corresponding three-dimensional virtual warehouse area according to a preset differential area evaluation result mapping, generating an evaluation solution synchronous mapping by an integrated reasoning model, and realizing effective diagnosis and visual mapping of performance of different warehouses and different areas in the warehouses.
Description
Technical Field
The invention belongs to the field of warehouse diagnosis, and particularly relates to a warehouse performance diagnosis and evaluation method and system.
Background
In a modern logistics system, a warehouse is used as a key link, and the operation efficiency, accuracy and space utilization condition of the warehouse have increasingly obvious influence on the competitiveness of enterprises. With the rapid development of industries such as electronic commerce and the like, warehouse scale and business complexity are continuously improved, and the traditional performance evaluation mode is difficult to meet enterprise requirements. The existing method is often focused on a single index, if only efficiency or accuracy is concerned, the association among the indexes is ignored, and the warehouse operation condition cannot be comprehensively reflected. Meanwhile, the existing evaluation method lacks a clear and structurally-clear evaluation index system, and cannot strictly evaluate the operation efficiency of each large warehouse cluster, so that specific defects and associated defect configuration corresponding to each warehouse cannot be detected, and the benefit is reduced. For example, the intelligent warehouse performance evaluation method of the metering appliance disclosed in the Chinese patent application with the publication number of CN111915129A utilizes a principal component analysis method to reduce the dimension, but the simplified index is excessively dependent on linear correlation assumption, so that nonlinear correlation index information is lost, and the hidden cost in the intelligent warehouse cannot be embodied, while the storage performance evaluation method based on the cloud model disclosed in the Chinese patent application with the publication number of CN116644991A is based on cloud model evaluation, and is difficult to capture the dynamic coupling relation among indexes due to the fact that the weight is determined by expert experience, so that sorting bottleneck appears in a great deal of time. In addition, the existing method adopts multi-focus single-bin evaluation, and the cross-bin association defect in the warehouse cluster cannot be positioned. The method is essentially limited to static mapping, lacks a full-element index network and dynamic causal reasoning capability, and therefore, the invention provides a warehouse performance diagnosis and evaluation method and system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a warehouse performance diagnosis and evaluation method and system, comprising the steps of acquiring a distributed three-dimensional virtual warehouse by using a three-dimensional modeling algorithm based on different warehouse attributes, combining an enhanced screening scheduling model with an evaluation association index library of a built-in hierarchical association index space to obtain a differential association evaluation index set, obtaining a distributed evaluation result grade cluster by using the association evaluation model configured by the index set and each three-dimensional virtual warehouse, finally performing real-time visual mapping of an evaluation result to a corresponding three-dimensional virtual warehouse area according to preset differential area evaluation result mapping, generating evaluation solution synchronous mapping by an integrated reasoning model, and realizing effective diagnosis and visual mapping of performance of different warehouses and different areas in the warehouses, thereby realizing rapid evaluation and solution recommendation of a large warehouse group.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A warehouse performance diagnostic evaluation method, comprising:
acquiring a distributed three-dimensional virtual warehouse based on different warehouse attributes in combination with a three-dimensional modeling algorithm;
Acquiring a differential association evaluation index set based on the combination of the distributed three-dimensional virtual warehouse, the reinforced screening scheduling model and the evaluation association index library;
the evaluation associated index library is internally provided with a layered associated index space, and the layered associated index space is constructed by a topology algorithm and a graph algorithm according to a three-level evaluation index set;
Based on the difference association evaluation index set, combining the association evaluation model configured by each three-dimensional virtual warehouse to obtain a distributed evaluation result grade cluster;
Based on the distributed evaluation result level cluster and the preset differential area evaluation result mapping, the evaluation result is visualized and mapped to the corresponding three-dimensional virtual warehouse area in real time, and meanwhile, an evaluation solution is generated and synchronously mapped to the visualization area corresponding to the three-dimensional virtual warehouse through the configured integrated reasoning model.
Specifically, the three-level evaluation index set includes a first evaluation index set, a second evaluation index set, and a third evaluation index set;
The method comprises the steps that the grade sequence corresponding to a first evaluation index set, a second evaluation index set and a third evaluation index set is that the first evaluation index set is greater than the second evaluation index set and greater than the third evaluation index set, wherein the first evaluation index set comprises an efficiency index, an accuracy index and a space utilization index;
Specifically, the second evaluation index set comprises goods receiving time efficiency, shelf loading delay rate, warehouse-in throughput, goods picking time efficiency, warehouse-out throughput, order processing time efficiency, personnel goods picking efficiency, equipment utilization rate, inventory accuracy rate, goods picking accuracy rate, shelf loading accuracy rate, storage capacity utilization rate and shelf storage density;
the efficiency indexes comprise goods receiving aging, shelf loading delay rate, warehouse-in throughput, goods picking aging, warehouse-out throughput, order processing aging, average goods picking efficiency and equipment utilization rate;
The accuracy index comprises inventory accuracy, picking accuracy and shelf loading accuracy, and the space utilization index comprises storage capacity utilization and shelf storage density.
Specifically, the receiving aging comprises the checking completion time, the arrival time and the total warehouse-in batch number in the third evaluation index set; the stock-up time comprises the total number of the single-day stock-up in the third evaluation index set and the total working hours of the stock-up personnel, the stock-up delay rate comprises the total number of overtime non-stock-up orders in the third evaluation index set and the total stock-up order number, the stock-in throughput comprises the total number of the single-day stock-in the third evaluation index set and the warehouse operation time, the stock-out time comprises the stock-out start time, the order creation time and the total order number in the third evaluation index set, the stock-out throughput comprises the total number of the single-day stock-out in the third evaluation index set and the warehouse operation time, the order processing time comprises the order completion time, the order creation time and the total order number in the third evaluation index set, the per-person stock-out efficiency comprises the total stock-out number in the third evaluation index set and the stock-out personnel working hours, and the equipment utilization rate comprises the actual running time and the planning available time of the equipment in the third evaluation index set;
the inventory accuracy comprises the correct inventory SKU number and the total inventory SKU number in the third evaluation index set, the picking accuracy comprises the error-free order line number and the total shipping order line number in the third evaluation index set, and the racking accuracy comprises the correct racking goods number and the total racking goods position number in the third evaluation index set;
the storage capacity utilization rate comprises occupied storage volume in a third evaluation index set and total storage volume of a warehouse, and the storage density of the goods shelf comprises the SKU actual storage capacity and the goods shelf theoretical maximum storage capacity in the third evaluation index set.
Specifically, the step of constructing the hierarchical association index space includes:
Constructing a first index node layer in an evaluation association index library based on index variables in the first evaluation index set, constructing a second index node layer in the evaluation association index library based on index variables in the second evaluation index set, and constructing a third index node layer in the evaluation association index library based on index variables in the third evaluation index set;
Constructing a first interlayer index connection and a second interlayer index connection corresponding to the first index node layer and the second index node layer and the third index node layer based on index variable inclusion relations corresponding to the first evaluation index set, the second evaluation index set and the third evaluation index;
Acquiring historical evaluation index combinations of different types of warehouses, index frequency of each index variable, combined index frequency among different index variables in a second evaluation index set and a third evaluation index set, and maximum efficiency index, accuracy index and space utilization index results in a first evaluation index set corresponding to the historical evaluation index combinations, so as to construct an index association data set;
and acquiring the association degree between the index variables in the second evaluation index set and the association degree between the index variables in the third evaluation index set through an association algorithm based on the index association data set.
Specifically, the step of constructing the hierarchical association index space further includes:
constructing a second relation connection set among nodes in a second index node layer based on the association degree among all index variables in the second evaluation index set;
Constructing a third relation connection set among nodes in a third index node layer based on the association degree among all index variables in the third evaluation index set;
based on interlayer index connection, a second relation connection set, a third relation connection set, a first index node layer, a second index node layer and a third index node layer, constructing and obtaining a layered association index space through a topological space algorithm;
based on the hierarchical association index space and the association degree on the corresponding connection relation, respectively carrying out cyclic community clustering on index variables in the second index node layer and the third index node layer by combining a density function in a density clustering algorithm through a graph algorithm;
when the modularity corresponding to each clustered community after community clustering in the second index node layer and the third index node layer and the density of adjacent nodes corresponding to each node in the corresponding communities are a fixed value, stopping circulating community clustering to obtain a second community clustered node group and a third community clustered node group respectively corresponding to the second index node layer and the third index node layer;
and taking each community clustering node group in the second community clustering node group and the third community clustering node group as a super node, and constructing a layered super node association space through a hypergraph algorithm.
Specifically, the step of constructing the hierarchical association index space further includes:
based on index variables among layers in the hierarchical association index space, a visual contribution map is set, specifically:
When the current moment is combined with the warehouse attribute configuration to be evaluated at the current moment through the reinforced screening scheduling model, screening all index variables from a first evaluation index set, screening N variable indexes from a second evaluation index set, screening M index variables from a third evaluation index set, and constructing a warehouse evaluation variable index set to be evaluated;
Inputting the variable index set to be evaluated into a hierarchical evaluation model constructed by combining an integrated fuzzy evaluation algorithm with an autocorrelation function, obtaining evaluation scores corresponding to each evaluation index in a first evaluation index set, and constructing an inter-layer contribution degree space of second autocorrelation contribution degree space and third autocorrelation contribution degree space corresponding to N variable indexes and M variable indexes in a second evaluation index set and a third evaluation index set, and inter-layer contribution degree of third evaluation index corresponding to the second evaluation index and inter-layer contribution degree space of second evaluation index corresponding to the first evaluation index.
Specifically, the step of constructing the hierarchical association index space further includes:
based on the magnitude of the autocorrelation contribution degree between two adjacent index variables in the third autocorrelation contribution degree space and a preset color type and color gradient combined by positive and negative, an intra-layer visual contribution map between the two adjacent index variables is constructed;
The in-layer visual contribution map is built in a third relation connection set corresponding to the third index node layer, a visual third index node layer is obtained, and a visual second index node layer is obtained by using a second autocorrelation contribution space in the same way;
Based on the preset color type and color gradient and the interlayer contribution degree and positive and negative of a third evaluation index in the interlayer contribution degree space to a second evaluation index, a second interlayer visual mapping between a second index node layer and the third index node layer is obtained, and the first interlayer visual mapping is obtained through the interlayer contribution degree of the second evaluation index to the first evaluation index in the same way;
The method comprises the steps of embedding a first interlayer visual map and a second interlayer visual map into a first interlayer index connection and a second interlayer index connection corresponding to a first index node layer and a second index node layer and a third index node layer, and obtaining a visual layering association index space corresponding to a warehouse to be evaluated;
when the corresponding evaluation of the warehouse to be evaluated and the pushing of the corresponding solution are completed, automatically eliminating the visual mapping between the interlayer index connection and the visual connection in the corresponding layers of the second index node layer and the visual third index node layer, and carrying out maintenance marking and historical evaluation tracing on the visual mapping corresponding to the warehouse to be evaluated at present;
And mapping the current warehouse evaluation visualization process to all warehouses with distributed control evaluation, and performing visualization evaluation mapping of the corresponding warehouses.
Specifically, the construction process of the differential area evaluation result mapping includes:
taking the space three-dimensional coordinates corresponding to the three-dimensional virtual warehouse corresponding to the warehouse to be evaluated at present as pixel projection coordinates;
Constructing a differential area evaluation result mapping by combining space index variables in space utilization index with a dynamic LOD algorithm according to colors and color gradients corresponding to connection relations in a visual hierarchical association index space corresponding to the warehouse to be evaluated at present;
And mapping the colors and the color gradients corresponding to the connection relations in the visual hierarchical association index space corresponding to the warehouse to be evaluated at present into the pixel projection coordinates corresponding to the warehouse to be evaluated at present through the mapping of the differential area evaluation results, so as to obtain the three-dimensional virtual warehouse with visual area.
The reinforced screening scheduling model is constructed by G reinforced screening scheduling sub-models, wherein the G reinforced screening scheduling sub-models are integrated into the corresponding edge control nodes of the corresponding Q warehouses;
Specifically, the screening process of the enhanced screening scheduling sub-model includes:
establishing a frequent index variable set through a corresponding to-be-evaluated warehouse evaluation variable index set after each warehouse evaluation;
establishing frequent index connection between the variable set of the frequent index and the corresponding reinforced screening scheduling sub-model, and embedding the frequent index connection into the corresponding reinforced screening scheduling sub-model;
When the warehouse corresponding to the current strengthening screening scheduling sub-module is evaluated, the attribute configuration of the warehouse to be evaluated is combined through frequent index connection, the index set index of the variable index set to be evaluated of the warehouse to be evaluated is carried out from the evaluation association index library, the index frequency and the joint index frequency of the corresponding index variable are fed back to the layering association index space, and the association degree of the corresponding connection relation labels between the layers in the layer is adjusted in real time;
When the current index to-be-evaluated warehouse evaluation variable index set does not belong to any community clustering node group in the hierarchical association index space, constructing a new community clustering node group through the current index to-be-evaluated warehouse evaluation variable index set and performing index variable storage and frequent index connection construction as a new supernode in the hierarchical supernode association space.
A warehouse performance diagnosis and evaluation system comprises a three-dimensional simulation module, an index screening module, an evaluation module and a mapping and recommending module;
the three-dimensional simulation module is used for acquiring a distributed three-dimensional virtual warehouse based on different warehouse attributes and combining with a three-dimensional modeling algorithm;
The index screening module is used for acquiring a differential association evaluation index set based on the combination of the distributed three-dimensional virtual warehouse, the reinforced screening scheduling model and the evaluation association index library;
the evaluation module is used for obtaining a distributed evaluation result grade cluster by combining the differential association evaluation index set with the association evaluation model configured by each three-dimensional virtual warehouse;
The mapping and recommending module is used for carrying out real-time visual mapping on the evaluation result to the corresponding three-dimensional virtual warehouse area based on the distributed evaluation result level cluster and combining with the preset differential area evaluation result mapping, and simultaneously generating an evaluation solution and synchronously mapping to the visual area corresponding to the three-dimensional virtual warehouse through the configured integrated reasoning model.
Compared with the prior art, the invention has the beneficial effects that:
Aiming at the defects of the prior art, the invention acquires a differential association evaluation index set by constructing a distributed three-dimensional virtual warehouse and combining a reinforced screening scheduling model with an evaluation association index library, comprehensively considers various indexes of warehouse operation, wherein the evaluation association index library is constructed through a layered association index space, considers the inclusion relation and association degree among all indexes based on the three-level evaluation index set, makes up the defects of the existing method that the single index is emphasized and the association of the indexes is ignored, can comprehensively reflect the warehouse operation condition, and secondly, presents complex index association and corresponding existing warehouse operation defects in an intuitive mode through visual contribution mapping, accurately finds out specific defects and associated defect configuration of the warehouse, generates a more accurate solution, effectively avoids the reduction of benefits and improves the warehouse operation management level.
Drawings
Fig. 1 is a flowchart of a warehouse performance diagnosis and evaluation method according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a hierarchical association index space construction according to embodiment 1 of the present invention;
fig. 3 is a block diagram of a warehouse performance diagnosis and evaluation system according to embodiment 2 of the present invention.
Detailed Description
Example 1
Referring to fig. 1, in one embodiment of the present invention, a warehouse performance diagnosis and evaluation method includes the steps of:
s1, acquiring a distributed three-dimensional virtual warehouse based on different warehouse attributes in combination with a three-dimensional modeling algorithm;
Further, in this embodiment, the texture mapping algorithm is specifically combined with the three-dimensional modeling algorithm and different warehouse actual attribute configurations to perform synchronous virtual three-dimensional model construction and operation process simulation, and the whole evaluation process is circularly corrected.
S2, acquiring a differential association evaluation index set based on a distributed three-dimensional virtual warehouse and combining the reinforced screening scheduling model with an evaluation association index library;
further, the differential association evaluation index set in the embodiment is constructed by the evaluation variable index set corresponding to each warehouse.
Further, the reinforced screening scheduling model in the embodiment is constructed by G reinforced screening scheduling sub-models in a federal integration mode;
Further, in this embodiment, the G reinforced screening scheduling sub-models are integrated into the edge control nodes corresponding to the corresponding Q warehouses;
Further, in this embodiment, G and Q are equal in value, and as the number of warehouses evaluated increases, the corresponding enhanced screening scheduling sub-model increases synchronously.
Further, all the reinforced screening scheduling sub-models in the embodiment are constructed and obtained through a SAC algorithm, and simulation training is carried out in the distributed three-dimensional virtual warehouse to obtain a trained reinforced screening scheduling model;
the evaluation associated index library is internally provided with a layered associated index space, and the layered associated index space is constructed by a topology algorithm and a graph algorithm according to a three-level evaluation index set;
further, the three-level evaluation index set in the present embodiment includes a first evaluation index set, a second evaluation index set, and a third evaluation index set;
The rank order corresponding to the first evaluation index set, the second evaluation index set and the third evaluation index set is that the first evaluation index set is greater than the second evaluation index set and greater than the third evaluation index set, wherein the first evaluation index set comprises an efficiency index, an accuracy index and a space utilization index;
The second evaluation index set comprises goods receiving time efficiency, shelf loading delay rate, warehouse-in throughput, goods picking time efficiency, warehouse-out throughput, order processing time efficiency, personnel goods picking efficiency, equipment utilization rate, inventory accuracy rate, goods picking accuracy rate, shelf loading accuracy rate, storage capacity utilization rate and shelf storage density;
the efficiency indexes comprise goods receiving aging, shelf loading delay rate, warehouse-in throughput, goods picking aging, warehouse-out throughput, order processing aging, average goods picking efficiency and equipment utilization rate;
The accuracy index comprises inventory accuracy, picking accuracy and shelf loading accuracy, and the space utilization index comprises storage capacity utilization and shelf storage density.
The goods receiving time comprises checking completion time, arrival time and total warehouse-in batch number in a third evaluation index set, the shelf-in time comprises a single-day shelf total number and shelf personnel total time in the third evaluation index set, the shelf delay rate comprises a overtime non-shelf order number and total shelf order number in the third evaluation index set, the warehouse-in throughput comprises a single-day warehouse-in total number and warehouse operation time in the third evaluation index set, the goods picking time comprises a goods picking start time, an order creation time and a total order number in the third evaluation index set, the warehouse-out throughput comprises a single-day warehouse-out total number and warehouse operation time in the third evaluation index set, the order processing time comprises a single-day order completion time, an order creation time and a total order number in the third evaluation index set, the average goods picking efficiency comprises a total picking number and a total order personnel time in the third evaluation index set, and the equipment utilization rate comprises an actual running time and a planned availability time in the third evaluation index set;
the inventory accuracy comprises the correct inventory SKU number and the total inventory SKU number in the third evaluation index set, the picking accuracy comprises the error-free order line number and the total shipping order line number in the third evaluation index set, and the racking accuracy comprises the correct racking goods number and the total racking goods position number in the third evaluation index set;
the storage capacity utilization rate comprises occupied storage volume in a third evaluation index set and total storage volume of a warehouse, and the storage density of the goods shelf comprises the SKU actual storage capacity and the goods shelf theoretical maximum storage capacity in the third evaluation index set.
Further, in order to better illustrate the data sources corresponding to the index variables in the first, second and third evaluation index sets, the relationships between the second and third evaluation index sets, and the actual business meanings corresponding to the corresponding indexes in the embodiment, the relationships between the first, second and third evaluation index sets are specifically shown by the following tables 1, 2 and 3;
TABLE 1 efficiency index
TABLE 2 accuracy index
TABLE 3 space utilization index
Further, in this embodiment, the meanings corresponding to the english abbreviations in the above tables 1, 2 and 3 are:
SKU-Stock Keeping Unit, the stock keeping unit, is a code used in inventory management to uniquely identify an item or product. In warehouse management, different SKUs represent different commodity types, specifications, models and the like, for example, shampoos of different brands and different capacities can correspond to different SKUs.
RFID Radio Frequency Identification, radio frequency identification, is a wireless communication technology that can identify a specific object by radio signals and read and write related data without the need for mechanical or optical contact between the identification system and the specific object. In a warehouse scene, the RFID tag is commonly used for tracking and checking goods, for example, the RFID tag is attached to the goods, and the information of the goods can be rapidly acquired through a reader-writer.
PDA Personal DIGITAL ASSISTANT, a palm computer, is commonly referred to as a hand-held terminal device in warehouse management. The staff can utilize the PDA to perform operations such as goods scanning, data input, task confirmation and the like, for example, when goods are put on shelf, the PDA is used for scanning the bar codes of goods and goods positions, and the information of putting on shelf is recorded.
IoT Internet of Things, i.e. internet of things, is to collect any object or process needing to be monitored, connected and interacted in real time through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners, etc., and to realize ubiquitous connection of objects and people through various possible network accesses, thereby realizing intelligent perception, identification and management of objects and processes. IoT device sensors in a warehouse may be used to monitor warehouse environments (e.g., temperature and humidity), device status, etc.
Automated Guided Vehicle, namely an automatic guided vehicle, is a conveying device capable of automatically running along a preset path, is commonly used for carrying cargoes in a warehouse, can improve carrying efficiency and accuracy, and reduces labor cost.
The WMS Warehouse MANAGEMENT SYSTEM, namely a warehouse management system, is an information system for managing and controlling resources such as materials, personnel, equipment and the like in a warehouse, can realize functions such as warehouse management, ex-warehouse management, inventory management, goods space management and the like, and can be used for checking the warehouse goods.
Further, referring to fig. 2, the step of constructing the hierarchical association index space in this embodiment includes:
Constructing a first index node layer in an evaluation association index library based on index variables in the first evaluation index set, constructing a second index node layer in the evaluation association index library based on index variables in the second evaluation index set, and constructing a third index node layer in the evaluation association index library based on index variables in the third evaluation index set;
Constructing a first interlayer index connection and a second interlayer index connection corresponding to the first index node layer and the second index node layer and the third index node layer based on index variable inclusion relations corresponding to the first evaluation index set, the second evaluation index set and the third evaluation index;
Acquiring historical evaluation index combinations of different types of warehouses, index frequency of each index variable, combined index frequency among different index variables in a second evaluation index set and a third evaluation index set, and maximum efficiency index, accuracy index and space utilization index results in a first evaluation index set corresponding to the historical evaluation index combinations, so as to construct an index association data set;
and acquiring the association degree between the index variables in the second evaluation index set and the association degree between the index variables in the third evaluation index set through an association algorithm based on the index association data set.
Constructing a second relation connection set among nodes in a second index node layer based on the association degree among all index variables in the second evaluation index set;
Constructing a third relation connection set among nodes in a third index node layer based on the association degree among all index variables in the third evaluation index set;
based on interlayer index connection, a second relation connection set, a third relation connection set, a first index node layer, a second index node layer and a third index node layer, constructing and obtaining a layered association index space through a topological space algorithm;
based on the hierarchical association index space and the association degree on the corresponding connection relation, respectively carrying out cyclic community clustering on index variables in the second index node layer and the third index node layer by combining a density function in a density clustering algorithm through a graph algorithm;
when the modularity corresponding to each clustered community after community clustering in the second index node layer and the third index node layer and the density of adjacent nodes corresponding to each node in the corresponding communities are a fixed value, stopping circulating community clustering to obtain a second community clustered node group and a third community clustered node group respectively corresponding to the second index node layer and the third index node layer;
and taking each community clustering node group in the second community clustering node group and the third community clustering node group as a super node, and constructing a layered super node association space through a hypergraph algorithm.
Based on index variables among layers in the hierarchical association index space, a visual contribution map is set, specifically:
When the current moment is combined with the warehouse attribute configuration to be evaluated at the current moment through the reinforced screening scheduling model, screening all index variables from a first evaluation index set, screening N variable indexes from a second evaluation index set, screening M index variables from a third evaluation index set, and constructing a warehouse evaluation variable index set to be evaluated;
Inputting the variable index set to be evaluated into a hierarchical evaluation model constructed by combining an integrated fuzzy evaluation algorithm with an autocorrelation function, obtaining evaluation scores corresponding to each evaluation index in a first evaluation index set, and constructing inter-layer contribution degree spaces of second autocorrelation contribution degree spaces and third autocorrelation contribution degree spaces corresponding to N variable indexes and M variable indexes in a second evaluation index set and a third evaluation index set, inter-layer contribution degree of third evaluation indexes corresponding to the second evaluation index and inter-layer contribution degree of the second evaluation index corresponding to the first evaluation index;
based on the magnitude of the autocorrelation contribution degree between two adjacent index variables in the third autocorrelation contribution degree space and a preset color type and color gradient combined by positive and negative, an intra-layer visual contribution map between the two adjacent index variables is constructed;
The in-layer visual contribution map is built in a third relation connection set corresponding to the third index node layer, a visual third index node layer is obtained, and a visual second index node layer is obtained by using a second autocorrelation contribution space in the same way;
Based on the preset color type and color gradient and the interlayer contribution degree and positive and negative of a third evaluation index in the interlayer contribution degree space to a second evaluation index, a second interlayer visual mapping between a second index node layer and the third index node layer is obtained, and the first interlayer visual mapping is obtained through the interlayer contribution degree of the second evaluation index to the first evaluation index in the same way;
The method comprises the steps of embedding a first interlayer visual map and a second interlayer visual map into a first interlayer index connection and a second interlayer index connection corresponding to a first index node layer and a second index node layer and a third index node layer, and obtaining a visual layering association index space corresponding to a warehouse to be evaluated;
when the corresponding evaluation of the warehouse to be evaluated and the pushing of the corresponding solution are completed, automatically eliminating the visual mapping between the interlayer index connection and the visual connection in the corresponding layers of the second index node layer and the visual third index node layer, and carrying out maintenance marking and historical evaluation tracing on the visual mapping corresponding to the warehouse to be evaluated at present;
And mapping the current warehouse evaluation visualization process to all warehouses with distributed control evaluation, and performing visualization evaluation mapping of the corresponding warehouses.
The process utilizes a first evaluation index set containing efficiency indexes, accuracy indexes and space utilization indexes, a second evaluation index set containing receiving aging, loading aging and the like and a third more refined evaluation index set to comprehensively evaluate warehouse operation from multiple dimensions by constructing a distributed three-dimensional virtual warehouse and combining a reinforced screening scheduling model with an evaluation association index library. For example, the efficiency index covers the timeliness and throughput index of each link from receiving goods to delivering goods, can comprehensively reflect the operation efficiency of the warehouse, the accuracy index ensures the accuracy of the inventory and the operation, the space utilization index focuses on the utilization condition of the warehouse space resources, avoids the limitation of focusing on only a single index, and comprehensively reflects the operation condition of the warehouse. The method comprises the steps of establishing a hierarchical association index space by establishing three-level evaluation index sets, establishing each level node based on index variables, establishing interlayer index connection according to the index inclusion relationship, and simultaneously establishing an intra-layer relationship connection set by considering the association degree among the index variables, so that the evaluation system structure is clear, each operation efficiency of a large warehouse cluster can be strictly evaluated, specific defects and association defect configuration of each warehouse can be accurately found out, for example, when the on-shelf delay rate is analyzed, multiple factors influencing the on-shelf delay, such as goods backlog influence on-shelf aging caused by slow receiving speed can be found out by association analysis of indexes such as receiving aging, and the reduction of benefits is avoided.
The method comprises the steps of obtaining a construction index association data set of different types of warehouse historical evaluation index combinations, index variable index frequencies and the like, obtaining the association degree among index variables through an association algorithm based on the construction index association data set, providing data support for warehouse operation decisions, formulating a more reasonable operation strategy based on the historical data and the index association relation, for example, finding out strong association between warehouse-in throughput and equipment utilization rate, evaluating the equipment utilization rate in advance and taking corresponding measures when the warehouse-in throughput is planned to be improved, such as reasonably arranging equipment maintenance time, improving equipment available time to meet service growth requirements, setting visual contribution mapping, presenting an evaluation process and results in an intuitive mode, constructing a layered evaluation model by combining an integrated fuzzy evaluation algorithm with an autocorrelation function, obtaining contribution degree spaces among each index evaluation score and different indexes, and obtaining visual layered association index spaces based on the construction layer and interlayer visual mapping, so that warehouse management staff can quickly understand complex index relations and evaluation results, and make more accurate decisions. For example, a manager can intuitively see which indexes have a great influence on the overall efficiency through a visual interface, and positive and negative association relations among different indexes and positive and negative contribution relations to a final index, so that an operation strategy is timely adjusted. Meanwhile, the visual mapping after the assessment is completed keeps marks and historical assessment traceability, subsequent duplication and analysis are facilitated, warehouse operation management is continuously optimized, in addition, the process builds an assessment variable index set according to the attribute configuration of the warehouse to be assessed, index variables are screened from different assessment index sets, dynamic assessment of different warehouses is achieved, the flexibility enables the assessment method to adapt to the characteristics and requirements of various warehouses, and effective performance diagnosis and assessment can be carried out no matter the size of the warehouse and the difference of business types, and the universality and practicality of the method are improved.
S3, combining the association evaluation model configured by each three-dimensional virtual warehouse based on the differential association evaluation index set to obtain a distributed evaluation result grade cluster;
further, in this embodiment, each warehouse is evaluated according to the differential association evaluation index set by using an association evaluation model, so as to obtain a corresponding evaluation result, and meanwhile, association evaluation is performed on different warehouses belonging to the same user by combining an association algorithm, so as to obtain association influence between corresponding results between different warehouses corresponding to the same user, and cross scheme adjustment is performed on all warehouses by using the association influence.
S4, based on the distributed evaluation result level cluster and combining with preset differential area evaluation result mapping, the evaluation result is visualized and mapped to a corresponding three-dimensional virtual warehouse area in real time, and meanwhile, an evaluation solution is generated and synchronously mapped to the visualized warehouse area corresponding to the three-dimensional virtual through a configured integrated reasoning model.
Further, in this embodiment, based on the evaluation result and the contribution degree corresponding to the single index variable, a personalized and operable optimization proposal and decision scheme are generated by using an integrated reasoning model (such as a depth reasoning algorithm). For example, according to the stock shortage risk and the order priority, the order scheduling and replenishment strategy is automatically generated, the preventive maintenance plan is formulated for the equipment fault risk, and further, the evaluation in the embodiment focuses not only on the final result but also on the influence degree of the intermediate index variable, and the corresponding solution with problems in each flow can be generated in more detail, so that each process of warehouse operation can be well diagnosed and improved.
Further, the construction process of the differential area assessment result map in this embodiment includes:
taking the space three-dimensional coordinates corresponding to the three-dimensional virtual warehouse corresponding to the warehouse to be evaluated at present as pixel projection coordinates;
further, in the embodiment, the space three-dimensional coordinates of the three-dimensional virtual warehouse accurately define the geometric information of each position in the warehouse, and the evaluation result can be accurately mapped to a specific position of the warehouse by taking the geometric information as the pixel projection coordinates;
For example, in a large stereoscopic warehouse, the positions of different shelves, the directions of channels and the like can be accurately represented by three-dimensional coordinates, so that an accurate position basis is provided for subsequent visual mapping.
Further, in this embodiment, the color and color gradient corresponding to the connection relationship in the visual hierarchical association index space corresponding to the warehouse to be evaluated currently are combined with a dynamic LOD (Level of Detail) algorithm to construct a differential area evaluation result map, where the connection relationship color and color gradient in the visual hierarchical association index space represents the information such as the association degree and contribution degree between different indexes, and the space index variables in the space index, such as the storage capacity utilization rate, the shelf storage density, and the like, reflect the use condition of the warehouse space. The dynamic LOD algorithm can dynamically adjust the detail degree of the model according to different requirements and scenes, and can differentially represent different areas according to the value condition of the space index variable and the combination of the color and the color gradient when the mapping is constructed. For example, for a region with high storage capacity utilization rate, a brighter color can be used for representing the region with low storage capacity utilization rate, and for a region with low utilization rate, a darker color can be used for representing the region with low storage capacity utilization rate, meanwhile, according to a dynamic LOD algorithm, the detail degree of a model can be reduced when the region is observed at a long distance, the rendering efficiency is improved, and when the region is observed at a short distance, the detail is increased, so that the display is clearer;
mapping the colors and the color gradients corresponding to the connection relations in the visual hierarchical association index space corresponding to the warehouse to be evaluated at present into the pixel projection coordinates corresponding to the warehouse to be evaluated at present through the mapping of the differential area evaluation results, and obtaining the three-dimensional virtual warehouse with visual area;
In this embodiment, through this step, the abstract evaluation result and the index association information are converted into an intuitive three-dimensional visual effect, and the warehouse manager can quickly know which areas in the warehouse are well operated and which areas have problems through observing the three-dimensional virtual warehouse with the visual area.
Further, the screening process of the enhanced screening scheduling sub-model in this embodiment includes:
establishing a frequent index variable set through a corresponding to-be-evaluated warehouse evaluation variable index set after each warehouse evaluation;
In this embodiment, when evaluating the warehouse, a large amount of evaluation variable index data is generated, and the frequent index variable set is established by performing statistical analysis on the data to find out the index variable with higher occurrence frequency in multiple evaluations. For example, in multiple evaluations of multiple warehouses, index variables such as shipping time, inventory accuracy, etc., are found to occur frequently and may be included in a frequent index variable set. These frequent index variables are typically key indexes that have a greater impact on warehouse operating conditions, their attention and analysis contributing to a more accurate assessment of warehouse performance;
establishing frequent index connection between the variable set of the frequent index and the corresponding reinforced screening scheduling sub-model, and embedding the frequent index connection into the corresponding reinforced screening scheduling sub-model;
In this embodiment, the frequent index connection is established for fast locating and acquiring information related to frequent index variables. By incorporating frequent index connections into the enhanced screening scheduling sub-model, the sub-model is enabled to more efficiently access and process the data of these key index variables when screening and scheduling operations are performed. For example, when a warehouse needs to be evaluated, the enhanced screening scheduling sub-model can quickly find frequent index variables and data thereof related to the warehouse through frequent index connection, so that the evaluation efficiency and accuracy are improved.
When the warehouse corresponding to the current reinforced screening scheduling sub-model is evaluated, the attribute configuration of the warehouse to be evaluated is combined through frequent index connection, the index set index of the variable index set to be evaluated of the warehouse to be evaluated is carried out from the evaluation association index library, the index frequency and the joint index frequency of the corresponding index variable are fed back to the layering association index space, and the association degree of the intra-layer and inter-layer corresponding connection relation label is adjusted in real time;
In the evaluation process, according to the specific attribute configuration of the warehouse, such as the type of the warehouse (such as a refrigeration warehouse, a common warehouse, etc.), a business mode (such as wholesale, retail, etc.), etc., the corresponding evaluation variable index set is obtained from the evaluation association index library through frequent index connection. At the same time, index frequency and joint index frequency of index variables are recorded, and the frequency information reflects the association tightness degree between the index variables. And after the information is fed back to the layering association index space, the association degree of the interlayer-layer corresponding connection relation labels in the layers is adjusted in real time according to the set rule. For example, if it is found that a certain index variable is higher in index frequency in the evaluation of the current warehouse and the joint index frequency with other index variables is also higher, the association degree of the connection relationship between the index variable and other related index variables can be appropriately increased so as to more accurately reflect the actual relationship therebetween.
When the current index to-be-evaluated warehouse evaluation variable index set does not belong to any community clustering node group in the hierarchical association index space, constructing a new community clustering node group through the current index to-be-evaluated warehouse evaluation variable index set and performing index variable storage and frequent index connection construction as a new supernode in the hierarchical supernode association space.
In the evaluation process of the embodiment, some new index variable combinations or special cases may occur, and the index set of the warehouse variable to be evaluated corresponding to the cases may not belong to the existing community cluster node group. At this time, in order to effectively manage and analyze these new situations, a new community cluster node group is constructed according to these index sets, and is used as a new supernode in the hierarchical supernode association space. In the new supernode, relevant index variables are saved, and corresponding frequent index connection is established so that subsequent evaluation and analysis work can be smoothly performed. Therefore, the hierarchical association index space can be ensured to continuously adapt to new conditions and requirements, and the flexibility and adaptability of the whole evaluation system are improved.
The process combines the relevance evaluation model with the difference relevance evaluation index set, not only can accurately evaluate a single warehouse, but also can evaluate the relevance influence among different warehouses of the same user by using a relevance algorithm and adjust a cross scheme, so that the whole optimization of warehouse operation is realized, isolated evaluation is avoided, and the comprehensive benefit is improved. The distributed evaluation result level clusters are visualized by combining with a preset map, three-dimensional coordinates are used as pixel projection coordinates, a map is constructed by combining with a space index variable and a dynamic LOD algorithm, abstract evaluation can be converted into visual three-dimensional effects, the color difference can also display the effect of efficiency indexes, a manager can conveniently and rapidly locate a problem area, and the warehouse operation process is optimized. The reinforced screening scheduling sub-model can rapidly acquire key index data by establishing a frequent index variable set and index connection, and improves evaluation efficiency and accuracy. The association degree is adjusted in real time, so that the index relationship is more fit with the actual situation, and the evaluation scientificity is enhanced. The new community clustering node group can be constructed to adapt to new conditions, and the flexibility and adaptability of the evaluation system are improved.
Example 2
Referring to fig. 3, another embodiment of the present invention provides a warehouse performance diagnosis and evaluation system, including a three-dimensional simulation module, an index screening module, an evaluation module, and a mapping and recommendation module;
The three-dimensional simulation module is used for acquiring a distributed three-dimensional virtual warehouse based on different warehouse attributes and combining a three-dimensional modeling algorithm;
The index screening module is used for acquiring a differential association evaluation index set based on the combination of the distributed three-dimensional virtual warehouse, the reinforced screening scheduling model and the evaluation association index library;
the evaluation module is used for obtaining a distributed evaluation result grade cluster by combining the differential association evaluation index set with the association evaluation model configured by each three-dimensional virtual warehouse;
The mapping and recommending module is used for visually mapping the evaluation result to the corresponding three-dimensional virtual warehouse area in real time based on the distributed evaluation result level cluster and combining with the preset differential area evaluation result mapping, and meanwhile, an evaluation solution is generated and synchronously mapped to the visual area corresponding to the three-dimensional virtual warehouse through the configured integrated reasoning model.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and variations, modifications, substitutions and alterations of the above-described embodiments may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the protection of the claims, which are all within the protection of the present invention.
If the technical scheme of the disclosure relates to personal information, the product applying the technical scheme of the disclosure clearly informs the personal information processing rule before processing the personal information, and obtains personal autonomous consent. If the technical scheme of the disclosure relates to sensitive personal information, the product applying the technical scheme of the disclosure obtains individual consent before processing the sensitive personal information, and simultaneously meets the requirement of 'explicit consent'. For example, a clear and obvious mark is set at a personal information acquisition device such as a camera to inform that the personal information acquisition range is entered, personal information is acquired, if the personal voluntarily enters the acquisition range, the personal information is considered as consent to acquire the personal information, or if a clear mark/information is used on a personal information processing device to inform that the personal information processing rule is used, personal authorization is obtained through popup information or a mode of requesting the personal information to upload the personal information by the personal, wherein the personal information processing rule can comprise information such as a personal information processor, a personal information processing purpose, a processing mode, a processed personal information type and the like.
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