Warehouse performance diagnosis and evaluation method and system

Abstract

The invention belongs to the field of warehouse diagnosis, and particularly relates to a warehouse performance diagnosis evaluation method and system, and the method comprises the steps: obtaining a distributed three-dimensional virtual warehouse through employing a three-dimensional modeling algorithm based on different warehouse attributes, and then combining a reinforced screening scheduling model with an evaluation association index library with a built-in layered association index space, the method comprises the following steps: obtaining a difference association evaluation index set, then obtaining a distributed evaluation result grade cluster by using the index set and an association evaluation model configured by each three-dimensional virtual warehouse, and finally mapping an evaluation result to a corresponding three-dimensional virtual warehouse region in a real-time visualization manner according to preset difference region evaluation result mapping. And an evaluation solution synchronous mapping is generated through an integrated reasoning model, so that effective diagnosis and visual mapping of performance of different warehouses and different areas in the warehouses are realized, and rapid evaluation and solution recommendation of a large-scale warehouse group are realized.

Description

Technical Field

[0001] The present invention belongs to the field of warehouse diagnosis, and in particular relates to a warehouse performance diagnosis and evaluation method and system. Background Art

[0002] In the modern logistics system, warehouses are key links, and their operational efficiency, accuracy, and space utilization have an increasingly significant impact on corporate competitiveness. With the rapid development of industries such as e-commerce, the scale of warehouses and business complexity are constantly increasing, and traditional performance evaluation methods can no longer meet the needs of enterprises. Existing methods often focus on a single indicator, such as only focusing on efficiency or accuracy, ignoring the relationship between various indicators, and cannot fully reflect the warehouse operation status. At the same time, the existing evaluation methods lack a clear and well-structured evaluation indicator system, and cannot strictly evaluate the operating efficiency of each large warehouse cluster, resulting in the inability to detect the specific defects and related defect configurations corresponding to each warehouse, resulting in a decline in efficiency. For example, a performance evaluation method for a smart warehouse for measuring instruments disclosed in a Chinese patent application with publication number CN111915129A uses principal component analysis to reduce dimensions. Although it simplifies indicators, it over-relies on linear correlation assumptions, resulting in loss of information on nonlinear correlation indicators and failure to reflect the hidden costs in smart warehouses. A warehousing performance evaluation method based on a cloud model disclosed in a Chinese patent application with publication number CN116644991A is based on cloud model evaluation, but because the weights are determined by expert experience, it is difficult to capture the dynamic coupling relationship between indicators, resulting in sorting bottlenecks during big promotions. In addition, existing methods focus more on single-warehouse evaluation and are unable to locate cross-warehouse correlation defects in warehouse clusters. Its essence is limited to static mapping and lacks a full-factor indicator network and dynamic causal reasoning capabilities. To this end, the present invention provides a warehouse performance diagnosis and evaluation method and system. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention proposes a warehouse performance diagnosis and evaluation method and system, including: using a three-dimensional modeling algorithm based on different warehouse attributes to obtain a distributed three-dimensional virtual warehouse, then combining an enhanced screening and scheduling model with an evaluation-related indicator library of a built-in hierarchical associated indicator space to obtain a differential associated evaluation indicator set, and then using the indicator set and the associated evaluation model configured for each three-dimensional virtual warehouse to obtain a distributed evaluation result level cluster, and finally, based on a preset differential area evaluation result mapping, the evaluation results are visually mapped to the corresponding three-dimensional virtual warehouse area in real time, and an evaluation solution is generated through an integrated reasoning model for synchronous mapping, thereby achieving effective diagnosis and visual mapping of the performance of different warehouses and different areas in the warehouse, and realizing rapid evaluation and solution recommendation for large warehouse groups.

[0004] To achieve the above object, the present invention provides the following technical solutions: A warehouse performance diagnosis and evaluation method, comprising: Based on different warehouse attributes combined with 3D modeling algorithms, a distributed 3D virtual warehouse is obtained; Based on the distributed three-dimensional virtual warehouse combined with the enhanced screening scheduling model and the evaluation correlation index library, a differential correlation evaluation index set is obtained; The evaluation association index library has a built-in hierarchical association index space, which is constructed based on the three-level evaluation index set through topological algorithm and graph algorithm; Based on the difference-based association evaluation index set combined with the association evaluation model of each three-dimensional virtual warehouse configuration, a distributed evaluation result level cluster is obtained; Based on the distributed evaluation result level clustering combined with the preset differential area evaluation result mapping, the evaluation results are visualized and mapped to the corresponding three-dimensional virtual warehouse area in real time. At the same time, through the configured integrated reasoning model, the evaluation solution is generated and synchronously mapped to the corresponding visualization area of ​​the three-dimensional virtual warehouse.

[0005] Specifically, the three-level evaluation index set includes a first evaluation index set, a second evaluation index set, and a third evaluation index; Specifically, the order of the first evaluation index set, the second evaluation index set and the third evaluation index is: the first evaluation index set> the second evaluation index set> the third evaluation index set; the first evaluation index set includes an efficiency index, an accuracy index and a space utilization index; Specifically, the second evaluation index set includes receipt time efficiency, shelf time efficiency, shelf delay rate, inbound throughput, picking time efficiency, outbound throughput, order processing time efficiency, per capita picking efficiency, equipment utilization, inventory accuracy, picking accuracy, shelf accuracy, storage capacity utilization and shelf storage density; The efficiency indicators include receiving time, putting on shelves time, putting on shelves delay rate, inbound throughput, picking time, outbound throughput, order processing time, per capita picking efficiency, and equipment utilization rate; The accuracy index includes inventory accuracy, picking accuracy and shelving accuracy; the space utilization index includes storage capacity utilization and shelf storage density.

[0006] Specifically, the receiving time efficiency includes the acceptance completion time, arrival time, and total number of incoming batches in the third evaluation index set; the shelving time efficiency includes the total number of shelving items in a single day and the total working hours of shelving personnel in the third evaluation index set; the shelving delay rate includes the number of orders that have timed out and not been shelved and the total number of shelving orders in the third evaluation index set; the incoming throughput includes the total number of incoming items in a single day and the warehouse operation time in the third evaluation index set; the picking time efficiency includes the picking start time, order creation time, and the total number of orders in the third evaluation index set; the outbound throughput includes the total number of outbound items in a single day and the warehouse operation time in the third evaluation index set; the order processing time efficiency includes the order completion time, order creation time, and the total number of orders in the third evaluation index set; the per capita picking efficiency includes the total number of picking items and the picking personnel hours in the third evaluation index set; the equipment utilization rate includes the actual operating time and planned available time of the equipment in the third evaluation index set; The inventory accuracy rate includes the number of correctly counted SKUs and the total number of counted SKUs in the third evaluation index set; the picking accuracy rate includes the number of error-free order lines and the total number of shipped order lines in the third evaluation index set; the shelf accuracy rate includes the number of correctly put on shelf positions and the total number of put on shelf positions in the third evaluation index set; The storage capacity utilization rate includes the occupied storage volume and the total storage volume of the warehouse in the third evaluation index set; the shelf storage density includes the actual storage capacity of the SKU and the theoretical maximum storage capacity of the shelf in the third evaluation index set.

[0007] Specifically, the steps for constructing the hierarchical association indicator space include: Constructing a first indicator node layer in an evaluation-related indicator library based on the indicator variables in the first evaluation indicator set, constructing a second indicator node layer in an evaluation-related indicator library based on the indicator variables in the second evaluation indicator set, and constructing a third indicator node layer in an evaluation-related indicator library based on the indicator variables in the third evaluation indicator set; Constructing a first inter-layer index connection and a second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer based on the indicator variable inclusion relationship corresponding to the first evaluation indicator set, the second evaluation indicator set and the third evaluation indicator; Obtain historical evaluation indicator combinations of different types of warehouses, the index frequency of each indicator variable, the combined index frequency between different indicator variables in the second evaluation indicator set and the third evaluation indicator set, and the maximum efficiency indicator, accuracy indicator, and space utilization indicator results in the first evaluation indicator set corresponding to the historical evaluation indicator combination, and construct an indicator association data set; Based on the indicator association data set, the association degree between each indicator variable in the second evaluation indicator set and the association degree between each indicator variable in the third evaluation indicator set are obtained through an association algorithm.

[0008] Specifically, the steps of constructing the hierarchical association indicator space also include: Based on the correlation between the indicator variables in the second evaluation indicator set, construct a second relationship connection set between the nodes in the second indicator node layer; Based on the correlation between the indicator variables in the third evaluation indicator set, a third relationship connection set between the nodes in the third indicator node layer is constructed; Based on the inter-layer index connection, the second relationship connection set, the third relationship connection set and the first indicator node layer, the second indicator node layer and the third indicator node layer, a hierarchical association indicator space is constructed by a topological space algorithm; Based on the hierarchical association indicator space and the association degree on the corresponding connection relationship, the indicator variables in the second indicator node layer and the third indicator node layer are respectively subjected to cyclic community clustering by combining the graph algorithm with the density function in the density clustering algorithm; When the modularity corresponding to each clustering community after community clustering in the second indicator node layer and the third indicator node layer and the density of neighboring nodes corresponding to each node in the corresponding community are a fixed value, the cyclic community clustering is stopped to obtain the second community clustering node group and the third community clustering node group corresponding to the second indicator node layer and the third indicator node layer respectively; Each community clustering node group in the second community clustering node group and the third community clustering node group is taken as a super node, and a hierarchical super node association space is constructed through a hypergraph algorithm.

[0009] Specifically, the steps of constructing the hierarchical association indicator space also include: Based on the indicator variables between each layer in the hierarchical correlation indicator space, a visualization contribution mapping is set, specifically: At the current moment, the enhanced screening scheduling model is combined with the attribute configuration of the warehouse to be evaluated at the current moment, all indicator variables are screened from the first evaluation indicator set, N variable indicators are screened from the second evaluation indicator set, and M indicator variables are screened from the third evaluation indicator set to construct an evaluation variable indicator set for the warehouse to be evaluated; The evaluation variable index set of the warehouse to be evaluated is input into the hierarchical evaluation model constructed by the comprehensive fuzzy evaluation algorithm combined with the autocorrelation function to obtain the evaluation score corresponding to each evaluation index in the first evaluation index set, and the inter-layer contribution space is constructed by the second autocorrelation contribution space and the third autocorrelation contribution space corresponding to the N and M variable indexes in the screened second and third evaluation index sets, and the inter-layer contribution space corresponding to the third evaluation index to the second evaluation index and the inter-layer contribution of the second evaluation index to the first evaluation index.

[0010] Specifically, the steps of constructing the hierarchical association indicator space also include: Based on the magnitude and positive and negative of the autocorrelation contribution between two adjacent indicator variables in the third autocorrelation contribution space combined with the preset color type and color gradient, construct an intra-layer visual contribution map between two adjacent indicator variables; The visualization contribution map within the layer is built into the third relationship connection set corresponding to the third indicator node layer to obtain a visualization third indicator node layer. Similarly, the visualization second indicator node layer is obtained by using the second autocorrelation contribution space; Based on the preset color type and color gradient and the size and positive and negative of the inter-layer contribution of the third evaluation indicator to the second evaluation indicator in the inter-layer contribution space, a second inter-layer visualization mapping between the second indicator node layer and the third indicator node layer is obtained. Similarly, a first inter-layer visualization mapping is obtained through the inter-layer contribution of the second evaluation indicator to the first evaluation indicator. The first inter-layer visualization mapping and the second inter-layer visualization mapping are embedded into the first inter-layer index connection and the second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer, so as to obtain the visualization hierarchical association indicator space corresponding to the warehouse to be evaluated; When the corresponding evaluation and solution push of the warehouse to be evaluated are completed, the inter-layer index connection and the visualization mapping on the corresponding layer intra-layer connection in the visualization second indicator node layer and the visualization third indicator node layer are automatically eliminated, and the visualization mapping corresponding to the current warehouse to be evaluated is marked and the historical evaluation is traced; The above-mentioned evaluation visualization process of the current warehouse to be evaluated is mapped to all warehouses evaluated by distributed control, and the visualization evaluation mapping of the corresponding warehouse is performed.

[0011] Specifically, the construction process of the difference area assessment result map includes: The spatial three-dimensional coordinates corresponding to the three-dimensional virtual warehouse corresponding to the warehouse to be evaluated are used as pixel projection coordinates; The color and color gradient corresponding to the connection relationship in the visual hierarchical association indicator space corresponding to the warehouse to be evaluated are used to construct a differential area evaluation result mapping through the spatial indicator variable in the space utilization index combined with the dynamic LOD algorithm; The color and color gradient corresponding to the connection relationship in the visualization hierarchical association indicator space corresponding to the current warehouse to be evaluated are mapped to the pixel projection coordinates corresponding to the current warehouse to be evaluated through the difference area evaluation result mapping to obtain a three-dimensional virtual warehouse with regional visualization.

[0012] Specifically, the enhanced screening scheduling model is constructed by G enhanced screening scheduling sub-models; the G enhanced screening scheduling sub-models are integrated into the edge control nodes corresponding to the corresponding Q warehouses; Specifically, the screening process of the enhanced screening scheduling sub-model includes: Establish a frequent indicator variable set through the evaluation variable indicator set of the warehouse to be evaluated corresponding to each evaluation of each warehouse; Establishing a frequent index connection between a frequent indicator variable set and a corresponding enhanced screening scheduling sub-model, and embedding the frequent index connection into the corresponding enhanced screening scheduling sub-model; When evaluating the warehouse corresponding to the current enhanced screening scheduling sub-module, the evaluation variable indicator set of the warehouse to be evaluated is indexed from the evaluation association indicator library through frequent index connection combined with the current warehouse attribute configuration to be evaluated, and the index frequency and joint index frequency of the corresponding indicator variable are fed back to the hierarchical association indicator space, and the correlation degree of the corresponding connection relationship annotation within and between layers is adjusted in real time; When the current index needs to evaluate the warehouse evaluation variable indicator set does not belong to any community clustering node group in the hierarchical association indicator space, a new community clustering node group is constructed through the current index needs to evaluate the warehouse evaluation variable indicator set and used as a new supernode in the hierarchical supernode association space to store indicator variables and construct frequent index connections.

[0013] A warehouse performance diagnosis and evaluation system, comprising: a three-dimensional simulation module, an indicator screening module, an evaluation module and a mapping and recommendation module; The three-dimensional simulation module obtains a distributed three-dimensional virtual warehouse based on different warehouse attributes combined with a three-dimensional modeling algorithm; The indicator screening module obtains a set of differential correlation evaluation indicators based on a distributed three-dimensional virtual warehouse combined with an enhanced screening scheduling model and an evaluation correlation indicator library; The evaluation module obtains a distributed evaluation result grade cluster based on the difference association evaluation indicator set combined with the association evaluation model of each three-dimensional virtual warehouse configuration; The mapping and recommendation module maps the evaluation results to the corresponding three-dimensional virtual warehouse area in real time based on the distributed evaluation result level cluster combined with the preset difference area evaluation result mapping. At the same time, through the configured integrated reasoning model, it generates an evaluation solution and synchronously maps it to the corresponding visualization area of ​​the three-dimensional virtual warehouse.

[0014] Compared with the prior art, the present invention has the following beneficial effects: In view of the deficiencies in the prior art, the present invention constructs a distributed three-dimensional virtual warehouse in combination with an enhanced screening and scheduling model and an evaluation-related indicator library to obtain a differential correlation evaluation indicator set, and comprehensively considers various indicators of warehouse operation. Among them, the evaluation-related indicator library is constructed through a hierarchical correlation indicator space. Based on a three-level evaluation indicator set, the inclusion relationship and correlation between each indicator are considered, which makes up for the shortcomings of the existing methods that focus on a single indicator and ignore the correlation between indicators, and can comprehensively reflect the warehouse operation status. Secondly, through visual contribution mapping, the complex indicator correlation and the corresponding warehouse operation defects are presented in an intuitive way, and the specific defects of the warehouse and the related defect configurations are accurately found, and more accurate solutions are generated, which effectively avoids the decline in efficiency and improves the level of warehouse operation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a warehouse performance diagnosis and evaluation method according to Embodiment 1 of the present invention; Figure 2 This is an architecture diagram of a hierarchical association index space constructed in Example 1 of the present invention; Figure 3 This is a module diagram of a warehouse performance diagnosis and evaluation system according to Example 2 of the present invention. DETAILED DESCRIPTION

[0016] Example 1

[0017] See also Figure 1 , an embodiment provided by the present invention: a warehouse performance diagnosis and evaluation method, the steps comprising: S1, based on different warehouse attributes combined with 3D modeling algorithms, obtain a distributed 3D virtual warehouse; Furthermore, in this embodiment, a texture mapping algorithm is specifically combined with a three-dimensional modeling algorithm and actual property configurations of different warehouses to perform synchronous virtual three-dimensional model construction and operation process simulation, and to perform cyclic corrections on the overall evaluation process.

[0018] S2, based on the distributed three-dimensional virtual warehouse combined with the enhanced screening scheduling model and the evaluation correlation index library, obtain the difference correlation evaluation index set; Furthermore, in this embodiment, the difference correlation evaluation index set is constructed by the evaluation variable index set corresponding to each warehouse.

[0019] Furthermore, the enhanced screening scheduling model in this embodiment is constructed by G enhanced screening scheduling sub-models through federated integration; Furthermore, in this embodiment, G enhanced screening scheduling sub-models are integrated into the edge control nodes corresponding to the corresponding Q warehouses; Furthermore, in this embodiment, G and Q are numerically equal, and when the number of evaluated warehouses increases, the corresponding enhanced screening scheduling sub-model increases synchronously.

[0020] Furthermore, all enhanced screening scheduling sub-models in this embodiment are constructed by the SAC algorithm, and simulated and trained in the above-mentioned distributed three-dimensional virtual warehouse to obtain a trained enhanced screening scheduling model; The evaluation association index library has a built-in hierarchical association index space, which is constructed based on the three-level evaluation index set through topological algorithm and graph algorithm; Further, the three-level evaluation index set in this embodiment includes a first evaluation index set, a second evaluation index set, and a third evaluation index set; The order of the first evaluation index set, the second evaluation index set and the third evaluation index set is: the first evaluation index set> the second evaluation index set> the third evaluation index set; the first evaluation index set includes an efficiency index, an accuracy index and a space utilization index; The second evaluation index set includes receiving time efficiency, putting on shelves time efficiency, putting on shelves delay rate, inbound throughput, picking time efficiency, outbound throughput, order processing time efficiency, per capita picking efficiency, equipment utilization rate, inventory accuracy rate, picking accuracy rate, putting on shelves accuracy rate, storage capacity utilization rate and shelf storage density; The efficiency indicators include receiving time, putting on shelves time, putting on shelves delay rate, inbound throughput, picking time, outbound throughput, order processing time, per capita picking efficiency, and equipment utilization rate; The accuracy index includes inventory accuracy, picking accuracy and shelving accuracy; the space utilization index includes storage capacity utilization and shelf storage density.

[0021] The receiving time efficiency includes the acceptance completion time, arrival time, and total number of incoming batches in the third evaluation index set; the shelving time efficiency includes the total number of shelving items in a single day and the total working hours of shelving personnel in the third evaluation index set; the shelving delay rate includes the number of orders that have timed out and not been shelved and the total number of shelving orders in the third evaluation index set; the incoming throughput includes the total number of incoming items in a single day and the warehouse operation time in the third evaluation index set; the picking time efficiency includes the picking start time, order creation time, and the total number of orders in the third evaluation index set; the outbound throughput includes the total number of outbound items in a single day and the warehouse operation time in the third evaluation index set; the order processing time efficiency includes the order completion time, order creation time, and the total number of orders in the third evaluation index set; the per capita picking efficiency includes the total number of picking items and the picking personnel hours in the third evaluation index set; the equipment utilization rate includes the actual operating time and planned available time of the equipment in the third evaluation index set; The inventory accuracy rate includes the number of correctly counted SKUs and the total number of counted SKUs in the third evaluation index set; the picking accuracy rate includes the number of error-free order lines and the total number of shipped order lines in the third evaluation index set; the shelf accuracy rate includes the number of correctly put on shelf positions and the total number of put on shelf positions in the third evaluation index set; The storage capacity utilization rate includes the occupied storage volume and the total storage volume of the warehouse in the third evaluation index set; the shelf storage density includes the actual storage capacity of the SKU and the theoretical maximum storage capacity of the shelf in the third evaluation index set.

[0022] Further, in order to better illustrate the data sources corresponding to the indicator variables in the first evaluation indicator set, the second evaluation indicator set and the third evaluation indicator set, the relationship between the second evaluation indicator set and the third evaluation indicator set, and the actual business significance of the corresponding indicators in this embodiment, the relationship between the first evaluation indicator set, the second evaluation indicator set and the third evaluation indicator set is specifically shown in Table 1, Table 2 and Table 3 respectively; Table 1: Efficiency indicators

[0023] Table 2: Accuracy index

[0024] Table 3: Space utilization index

[0025] Furthermore, in this embodiment, the corresponding English abbreviations in Table 1, Table 2 and Table 3 have the following meanings: SKU: Stock Keeping Unit, which is a code used to uniquely identify a commodity or product in inventory management. In warehouse management, different SKUs represent different commodity types, specifications, models, etc. For example, different brands and different volumes of shampoo will correspond to different SKUs.

[0026] RFID: Radio Frequency Identification is a wireless communication technology that can identify specific targets and read and write related data through radio signals without establishing mechanical or optical contact between the identification system and the specific target. In warehouse scenarios, it is often used for tracking and inventorying goods. For example, by attaching RFID tags to goods, the goods information can be quickly obtained through the reader / writer.

[0027] PDA: Personal Digital Assistant, also known as a handheld computer, usually refers to a handheld terminal device in warehouse management. Workers can use PDA to scan goods, enter data, confirm tasks, etc. For example, when putting goods on the shelves, they can use PDA to scan the barcodes of goods and cargo locations and record the shelf information.

[0028] IoT: Internet of Things, or IoT, is the use of various information sensors, radio frequency identification technology, global positioning system, infrared sensors, laser scanners and other devices and technologies to collect any objects or processes that need to be monitored, connected, and interacted in real time, and to achieve ubiquitous connection between objects and objects and between objects and people through various possible network accesses, and to achieve intelligent perception, identification and management of objects and processes. IoT device sensors in warehouses can be used to monitor warehouse environment (such as temperature and humidity), equipment status, etc.

[0029] AGV: Automated Guided Vehicle, is a type of transport equipment that can automatically travel along a preset path. It is often used for cargo transportation within warehouses. It can improve transportation efficiency and accuracy and reduce labor costs.

[0030] WMS: Warehouse Management System, also known as warehouse management system, is an information system for managing and controlling materials, personnel, equipment and other resources in the warehouse. It can realize functions such as warehousing management, outbound management, inventory management, and storage location management. For example, the WMS inventory module can be used to perform inventory operations on warehouse goods.

[0031] For further information, see Figure 2 , the steps of constructing the hierarchical association index space in this embodiment include: Constructing a first indicator node layer in an evaluation-related indicator library based on the indicator variables in the first evaluation indicator set, constructing a second indicator node layer in an evaluation-related indicator library based on the indicator variables in the second evaluation indicator set, and constructing a third indicator node layer in an evaluation-related indicator library based on the indicator variables in the third evaluation indicator set; Constructing a first inter-layer index connection and a second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer based on the indicator variable inclusion relationship corresponding to the first evaluation indicator set, the second evaluation indicator set and the third evaluation indicator; Obtain historical evaluation indicator combinations of different types of warehouses, the index frequency of each indicator variable, the combined index frequency between different indicator variables in the second evaluation indicator set and the third evaluation indicator set, and the maximum efficiency indicator, accuracy indicator, and space utilization indicator results in the first evaluation indicator set corresponding to the historical evaluation indicator combination, and construct an indicator association data set; Based on the indicator association data set, the association degree between each indicator variable in the second evaluation indicator set and the association degree between each indicator variable in the third evaluation indicator set are obtained through an association algorithm.

[0032] Based on the correlation between the indicator variables in the second evaluation indicator set, construct a second relationship connection set between the nodes in the second indicator node layer; Based on the correlation between the indicator variables in the third evaluation indicator set, a third relationship connection set between the nodes in the third indicator node layer is constructed; Based on the inter-layer index connection, the second relationship connection set, the third relationship connection set and the first indicator node layer, the second indicator node layer and the third indicator node layer, a hierarchical association indicator space is constructed by a topological space algorithm; Based on the hierarchical association indicator space and the association degree on the corresponding connection relationship, the indicator variables in the second indicator node layer and the third indicator node layer are respectively subjected to cyclic community clustering by combining the graph algorithm with the density function in the density clustering algorithm; When the modularity corresponding to each clustering community after community clustering in the second indicator node layer and the third indicator node layer and the density of neighboring nodes corresponding to each node in the corresponding community are a fixed value, the cyclic community clustering is stopped to obtain the second community clustering node group and the third community clustering node group corresponding to the second indicator node layer and the third indicator node layer respectively; Each community clustering node group in the second community clustering node group and the third community clustering node group is taken as a super node, and a hierarchical super node association space is constructed through a hypergraph algorithm.

[0033] Based on the indicator variables between each layer in the hierarchical correlation indicator space, a visualization contribution mapping is set, specifically: At the current moment, the enhanced screening scheduling model is combined with the attribute configuration of the warehouse to be evaluated at the current moment, all indicator variables are screened from the first evaluation indicator set, N variable indicators are screened from the second evaluation indicator set, and M indicator variables are screened from the third evaluation indicator set to construct an evaluation variable indicator set for the warehouse to be evaluated; The evaluation variable index set of the warehouse to be evaluated is input into the hierarchical evaluation model constructed by the comprehensive fuzzy evaluation algorithm combined with the autocorrelation function, and the evaluation score corresponding to each evaluation index in the first evaluation index set is obtained, and the second autocorrelation contribution space and the third autocorrelation contribution space corresponding to the N and M variable indexes in the second and third evaluation index sets are selected, and the inter-layer contribution space is constructed by the inter-layer contribution of the third evaluation index to the second evaluation index and the inter-layer contribution of the second evaluation index to the first evaluation index; Based on the magnitude and positive and negative of the autocorrelation contribution between two adjacent indicator variables in the third autocorrelation contribution space combined with the preset color type and color gradient, construct an intra-layer visual contribution map between two adjacent indicator variables; The visualization contribution map within the layer is built into the third relationship connection set corresponding to the third indicator node layer to obtain a visualization third indicator node layer. Similarly, the visualization second indicator node layer is obtained by using the second autocorrelation contribution space; Based on the preset color type and color gradient and the size and positive and negative of the inter-layer contribution of the third evaluation indicator to the second evaluation indicator in the inter-layer contribution space, a second inter-layer visualization mapping between the second indicator node layer and the third indicator node layer is obtained. Similarly, a first inter-layer visualization mapping is obtained through the inter-layer contribution of the second evaluation indicator to the first evaluation indicator. The first inter-layer visualization mapping and the second inter-layer visualization mapping are embedded into the first inter-layer index connection and the second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer, so as to obtain the visualization hierarchical association indicator space corresponding to the warehouse to be evaluated; When the corresponding evaluation and solution push of the warehouse to be evaluated are completed, the inter-layer index connection and the visualization mapping on the corresponding layer intra-layer connection in the visualization second indicator node layer and the visualization third indicator node layer are automatically eliminated, and the visualization mapping corresponding to the current warehouse to be evaluated is marked and the historical evaluation is traced; The above-mentioned evaluation visualization process of the current warehouse to be evaluated is mapped to all warehouses evaluated by distributed control, and the visualization evaluation mapping of the corresponding warehouse is performed.

[0034] This process builds a distributed three-dimensional virtual warehouse, combines the enhanced screening and scheduling model with the evaluation-related index library, and uses the first evaluation index set including efficiency index, accuracy index and space utilization index, as well as the second evaluation index set covering the timeliness of receipt and shelf timeliness, and the more detailed third evaluation index set to comprehensively evaluate warehouse operations from multiple dimensions. For example, in the efficiency index, the timeliness and throughput indicators of each link from receipt to delivery are covered, which can fully reflect the efficiency of warehouse operations; the accuracy index ensures the accuracy of inventory and operations; the space utilization index focuses on the utilization of warehouse space resources, avoiding the limitation of focusing on only a single indicator, and comprehensively reflects the warehouse operation status. Among them, firstly, by establishing a three-level evaluation indicator set, the hierarchical order and inclusion relationship between each indicator set are clarified, and a hierarchical associated indicator space is constructed; each layer node is constructed based on the indicator variable, and the inter-layer index connection is established according to the indicator inclusion relationship. At the same time, the correlation between the indicator variables is considered to construct the intra-layer relationship connection set, which makes the evaluation system structure clear, can strictly evaluate the various operating efficiencies of large warehouse clusters, and accurately find out the specific defects and associated defect configurations of each warehouse; for example, when analyzing the shelving delay rate, through the correlation analysis with indicators such as receipt timeliness and warehousing throughput, it is possible to find out various factors affecting the shelving delay, such as slow receipt speed leading to goods backlog affecting the shelving timeliness, so as to solve the problem in a targeted manner and avoid a decline in efficiency.

[0035] Secondly, by obtaining the historical evaluation index combination of different types of warehouses, the index variable index frequency, etc., the index association data set is constructed. Based on this, the correlation between the indicator variables is obtained through the association algorithm, which provides data support for warehouse operation decisions and can formulate more reasonable operation strategies based on historical data and indicator correlation relationships. For example, it is found that there is a strong correlation between the inbound throughput and the equipment utilization rate. When planning to increase the inbound throughput, the equipment utilization rate can be evaluated in advance and corresponding measures can be taken, such as reasonably arranging the equipment maintenance time and increasing the equipment available time to meet the needs of business growth. The setting of the visual contribution mapping presents the evaluation process and results in an intuitive way. The hierarchical evaluation model is constructed by combining the comprehensive fuzzy evaluation algorithm with the autocorrelation function to obtain the evaluation scores of each indicator and the contribution space between different indicators. Based on these constructions, the intra-layer and inter-layer visual mappings are obtained to obtain the visual hierarchical correlation indicator space, which helps warehouse managers quickly understand the complex indicator relationship and evaluation results and make more accurate decisions. For example, through the visual interface, managers can intuitively see which indicators have a greater impact on the overall efficiency, as well as the positive and negative correlations between different indicators and the positive and negative contribution relationships to the final indicators, so as to adjust the operation strategy in time. At the same time, the visual mapping after the evaluation is completed maintains the marking and historical evaluation traceability, which is convenient for subsequent review and analysis, and continuously optimizes warehouse operation management. In addition, this process selects indicator variables from different evaluation indicator sets to construct an evaluation variable indicator set based on the current warehouse attribute configuration to be evaluated, and realizes dynamic evaluation of different warehouses. This flexibility enables the evaluation method to adapt to the characteristics and needs of various types of warehouses. Regardless of the size of the warehouse and the difference in business type, it can perform effective performance diagnosis and evaluation, thereby improving the versatility and practicality of the method.

[0036] S3, based on the difference association evaluation index set combined with the association evaluation model of each three-dimensional virtual warehouse configuration, obtain a distributed evaluation result level cluster; Furthermore, in this embodiment, an association evaluation model is used to evaluate each warehouse according to a set of differential association evaluation indicators to obtain corresponding evaluation results. At the same time, an association evaluation is performed on different warehouses belonging to the same user in combination with an association algorithm to obtain the association influence between the corresponding results of different warehouses corresponding to the same user, and cross-scheme adjustments are made to all warehouses through the association influence.

[0037] S4. Based on the distributed evaluation result level clustering combined with the preset differential area evaluation result mapping, the evaluation results are visualized and mapped to the corresponding three-dimensional virtual warehouse area in real time. At the same time, through the configured integrated reasoning model, the evaluation solution is generated and synchronously mapped to the corresponding three-dimensional virtual visualization warehouse area.

[0038] Furthermore, in this embodiment, based on the above evaluation results and the corresponding contribution of the corresponding single indicator variables, an integrated reasoning model (such as a deep reasoning algorithm) is used to generate personalized, actionable optimization suggestions and decision-making plans. For example, order scheduling and replenishment strategies are automatically generated based on inventory out-of-stock risks and order priorities; preventive maintenance plans are formulated for equipment failure risks, etc. Furthermore, the evaluation in this embodiment not only focuses on the final results, but also on the influence of the intermediate indicator variables, and can generate more detailed solutions to the problems in each corresponding process, so that each process of warehouse operation can be well diagnosed and improved.

[0039] Furthermore, the process of constructing the difference area assessment result map in this embodiment includes: The spatial three-dimensional coordinates corresponding to the three-dimensional virtual warehouse corresponding to the warehouse to be evaluated are used as pixel projection coordinates; Furthermore, the spatial three-dimensional coordinates of the three-dimensional virtual warehouse in this embodiment accurately define the geometric information of each location in the warehouse, and by using it as a pixel projection coordinate, the evaluation results can be accurately mapped to the specific location of the warehouse; For example, in a large stereoscopic warehouse, the location of different shelves, the direction of aisles, etc. can be accurately represented by three-dimensional coordinates, thus providing an accurate location basis for subsequent visual mapping.

[0040] Furthermore, in this embodiment, the color and color gradient corresponding to the connection relationship in the visual hierarchical association indicator space corresponding to the warehouse to be evaluated are combined with the dynamic LOD (Level of Detail) algorithm to construct a differential regional evaluation result mapping. The color and color gradient of the connection relationship in the visual hierarchical association indicator space represent the degree of association and contribution between different indicators; and the spatial indicator variables in the space utilization index, such as storage capacity utilization, shelf storage density, etc., reflect the use of warehouse space. The dynamic LOD algorithm can dynamically adjust the level of detail of the model according to different needs and scenarios; when constructing the mapping, different areas will be represented differently according to the value of the spatial indicator variable, combined with the color and color gradient. For example, areas with high storage capacity utilization can be represented by brighter colors, while areas with low utilization can be represented by darker colors. At the same time, according to the dynamic LOD algorithm, when observing from a distance, the model's level of detail can be reduced to improve rendering efficiency, while when observing from a close distance, details can be increased to make the display clearer. At the same time, it will also more intuitively show which efficiency indicators have a negative impact on the operation of the warehouse at the current moment, so as to better improve the warehouse operation process corresponding to the corresponding efficiency indicators. The color and color gradient corresponding to the connection relationship in the visualization hierarchical association index space corresponding to the current warehouse to be evaluated are mapped to the pixel projection coordinates corresponding to the current warehouse to be evaluated through the difference area evaluation result mapping, so as to obtain a three-dimensional virtual warehouse with regional visualization; In this embodiment, through this step, the abstract evaluation results and indicator association information are converted into an intuitive three-dimensional visualization effect. Warehouse managers can quickly understand which areas in the warehouse are operating well and which areas have problems by observing the three-dimensional virtual warehouse with regional visualization.

[0041] Furthermore, the screening process of the enhanced screening scheduling sub-model in this embodiment includes: Establish a frequent indicator variable set through the evaluation variable indicator set of the warehouse to be evaluated corresponding to each evaluation of each warehouse; In this embodiment, when evaluating a warehouse, a large amount of evaluation variable indicator data will be generated. The establishment of a frequent indicator variable set is to perform statistical analysis on these data to find indicator variables that appear frequently in multiple evaluations. For example, in multiple evaluations of multiple warehouses, it is found that indicator variables such as delivery timeliness and inventory accuracy often appear, and they can be included in the frequent indicator variable set. These frequent indicator variables are usually key indicators that have a greater impact on the warehouse's operating conditions. Paying attention to and analyzing them helps to more accurately evaluate the warehouse's performance; Establishing a frequent index connection between a frequent indicator variable set and a corresponding enhanced screening scheduling sub-model, and embedding the frequent index connection into the corresponding enhanced screening scheduling sub-model; In this embodiment, the establishment of frequent index connections is to quickly locate and obtain information related to frequent indicator variables. By building frequent index connections into the enhanced screening and scheduling sub-model, the sub-model can more efficiently access and process the data of these key indicator variables when performing screening and scheduling operations. For example, when a warehouse needs to be evaluated, the enhanced screening and scheduling sub-model can quickly find the frequent indicator variables and their data related to the warehouse through frequent index connections, thereby improving the efficiency and accuracy of the evaluation.

[0042] When evaluating the warehouse corresponding to the current enhanced screening scheduling sub-model, the evaluation variable indicator set of the warehouse to be evaluated is indexed from the evaluation association indicator library through frequent index connection combined with the current warehouse attribute configuration to be evaluated, and the index frequency and joint index frequency of the corresponding indicator variable are fed back to the hierarchical association indicator space, and the correlation degree of the corresponding connection relationship annotation within and between layers is adjusted in real time; In the evaluation process of this embodiment, according to the specific attribute configuration of the warehouse, such as the type of warehouse (such as cold storage warehouse, ordinary warehouse, etc.), business model (such as wholesale, retail, etc.), etc., the corresponding evaluation variable indicator set is obtained from the evaluation association indicator library through frequent index connection. At the same time, the index frequency and joint index frequency of the indicator variables are recorded. These frequency information reflects the degree of association between the indicator variables. After this information is fed back to the hierarchical association indicator space, the correlation of the corresponding connection relationship annotations within and between layers will be adjusted in real time according to the set rules. For example, if it is found that a certain indicator variable has a high index frequency in the evaluation of the current warehouse, and the joint index frequency with other indicator variables is also high, the correlation between the connection relationship between the indicator variable and other related indicator variables can be appropriately increased to more accurately reflect the actual relationship between them.

[0043] When the current index needs to evaluate the warehouse evaluation variable indicator set does not belong to any community clustering node group in the hierarchical association indicator space, a new community clustering node group is constructed through the current index needs to evaluate the warehouse evaluation variable indicator set and used as a new supernode in the hierarchical supernode association space to store indicator variables and construct frequent index connections.

[0044] During the evaluation process of this embodiment, some new indicator variable combinations or special cases may appear, and the corresponding warehouse evaluation variable indicator sets to be evaluated may not belong to the existing community clustering node group. At this time, in order to effectively manage and analyze these new situations, a new community clustering node group will be constructed based on these indicator sets, and it will be used as a new supernode in the hierarchical supernode association space. In the new supernode, the relevant indicator variables will be saved, and the corresponding frequent index connections will be established so that subsequent evaluation and analysis work can proceed smoothly. This can ensure that the hierarchical association indicator space can continuously adapt to new situations and needs, and improve the flexibility and adaptability of the entire evaluation system.

[0045] This process can not only accurately evaluate a single warehouse by combining the association evaluation model with the differential association evaluation indicator set, but also use the association algorithm to evaluate the association impact between different warehouses of the same user and make cross-scheme adjustments, so as to achieve overall optimization of warehouse operations, avoid isolated evaluation, and improve comprehensive benefits. The distributed evaluation result level cluster is visualized in combination with the preset mapping, and the three-dimensional coordinates are used as the pixel projection coordinates. The mapping is constructed by combining the spatial indicator variables and the dynamic LOD algorithm, which can transform the abstract evaluation into an intuitive three-dimensional effect. The color difference can also show the impact of the efficiency indicator, which is convenient for managers to quickly locate the problem area and optimize the warehouse operation process. The enhanced screening scheduling sub-model can quickly obtain key indicator data and improve the evaluation efficiency and accuracy by establishing frequent indicator variable sets and index connections. Real-time adjustment of the correlation makes the indicator relationship more realistic and enhances the scientific nature of the evaluation. The construction of a new community clustering node group can adapt to new situations and improve the flexibility and adaptability of the evaluation system.

[0046] Example 2

[0047] See also Figure 3 ,Another embodiment provided by the present invention: A warehouse performance diagnosis and evaluation system, comprising: a three-dimensional simulation module, an indicator screening module, an evaluation module and a mapping and recommendation module; The 3D simulation module obtains a distributed 3D virtual warehouse based on different warehouse attributes combined with 3D modeling algorithms; The indicator screening module obtains the differential correlation evaluation indicator set based on the distributed three-dimensional virtual warehouse combined with the enhanced screening scheduling model and the evaluation correlation indicator library; The evaluation module obtains distributed evaluation result level clusters based on the difference-related evaluation indicator set combined with the associated evaluation model of each three-dimensional virtual warehouse configuration; The mapping and recommendation module maps the evaluation results to the corresponding three-dimensional virtual warehouse area in real time based on the distributed evaluation result level cluster combined with the preset difference area evaluation result mapping. At the same time, through the configured integrated reasoning model, it generates the evaluation solution and synchronously maps it to the corresponding visualization area of ​​the three-dimensional virtual warehouse.

[0048] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0049] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A warehouse performance diagnosis and evaluation method, characterized in that: include: Based on different warehouse attributes combined with 3D modeling algorithms, a distributed 3D virtual warehouse is obtained; Based on the distributed three-dimensional virtual warehouse combined with the enhanced screening scheduling model and the evaluation correlation index library, a differential correlation evaluation index set is obtained; The evaluation association index library has a built-in hierarchical association index space, which is constructed based on the three-level evaluation index set through topological algorithm and graph algorithm; Based on the difference-based association evaluation index set combined with the association evaluation model of each three-dimensional virtual warehouse configuration, a distributed evaluation result level cluster is obtained; Based on the distributed evaluation result level clustering combined with the preset differential area evaluation result mapping, the evaluation results are visualized and mapped to the corresponding three-dimensional virtual warehouse area in real time. At the same time, through the configured integrated reasoning model, the evaluation solution is generated and synchronously mapped to the corresponding visualization area of ​​the three-dimensional virtual warehouse.

2. A warehouse performance diagnosis and evaluation method as claimed in claim 1, characterized in that: The three-level evaluation index set includes a first evaluation index set, a second evaluation index set and a third evaluation index set; The order of the first evaluation index set, the second evaluation index set and the third evaluation index set is: the first evaluation index set> the second evaluation index set> the third evaluation index set; the first evaluation index set includes an efficiency index, an accuracy index and a space utilization index; The second evaluation index set includes receiving time efficiency, putting on shelves time efficiency, putting on shelves delay rate, inbound throughput, picking time efficiency, outbound throughput, order processing time efficiency, per capita picking efficiency, equipment utilization rate, inventory accuracy rate, picking accuracy rate, putting on shelves accuracy rate, storage capacity utilization rate and shelf storage density; The efficiency indicators include receiving time, putting on shelves time, putting on shelves delay rate, inbound throughput, picking time, outbound throughput, order processing time, per capita picking efficiency, and equipment utilization rate; The accuracy index includes inventory accuracy, picking accuracy and shelving accuracy; the space utilization index includes storage capacity utilization and shelf storage density.

3. A warehouse performance diagnosis and evaluation method as claimed in claim 2, characterized in that: The receiving time efficiency includes the acceptance completion time, arrival time, and total number of incoming batches in the third evaluation index set; the shelving time efficiency includes the total number of shelving items in a single day and the total working hours of shelving personnel in the third evaluation index set; the shelving delay rate includes the number of orders that have timed out and not been shelved and the total number of shelving orders in the third evaluation index set; the incoming throughput includes the total number of incoming items in a single day and the warehouse operation time in the third evaluation index set; the picking time efficiency includes the picking start time, order creation time, and the total number of orders in the third evaluation index set; the outbound throughput includes the total number of outbound items in a single day and the warehouse operation time in the third evaluation index set; the order processing time efficiency includes the order completion time, order creation time, and the total number of orders in the third evaluation index set; the per capita picking efficiency includes the total number of picking items and the picking personnel hours in the third evaluation index set; the equipment utilization rate includes the actual operating time and planned available time of the equipment in the third evaluation index set; The inventory accuracy rate includes the number of correctly counted SKUs and the total number of counted SKUs in the third evaluation index set; the picking accuracy rate includes the number of error-free order lines and the total number of shipped order lines in the third evaluation index set; the shelf accuracy rate includes the number of correctly put on shelf positions and the total number of put on shelf positions in the third evaluation index set; The storage capacity utilization rate includes the occupied storage volume and the total storage volume of the warehouse in the third evaluation index set; the shelf storage density includes the actual storage capacity of the SKU and the theoretical maximum storage capacity of the shelf in the third evaluation index set.

4. A warehouse performance diagnosis and evaluation method as claimed in claim 3, characterized in that: The steps of constructing the hierarchical association index space include: Constructing a first indicator node layer in an evaluation-related indicator library based on the indicator variables in the first evaluation indicator set, constructing a second indicator node layer in an evaluation-related indicator library based on the indicator variables in the second evaluation indicator set, and constructing a third indicator node layer in an evaluation-related indicator library based on the indicator variables in the third evaluation indicator set; Constructing a first inter-layer index connection and a second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer based on the indicator variable inclusion relationship corresponding to the first evaluation indicator set, the second evaluation indicator set and the third evaluation indicator; Obtain historical evaluation indicator combinations of different types of warehouses, the index frequency of each indicator variable, the combined index frequency between different indicator variables in the second evaluation indicator set and the third evaluation indicator set, and the maximum efficiency indicator, accuracy indicator, and space utilization indicator results in the first evaluation indicator set corresponding to the historical evaluation indicator combination, and construct an indicator association data set; Based on the indicator association data set, the association degree between each indicator variable in the second evaluation indicator set and the association degree between each indicator variable in the third evaluation indicator set are obtained through an association algorithm.

5. A warehouse performance diagnosis and evaluation method as claimed in claim 4, characterized in that: The step of constructing the hierarchical association index space also includes: Based on the correlation between the indicator variables in the second evaluation indicator set, construct a second relationship connection set between the nodes in the second indicator node layer; Based on the correlation between the indicator variables in the third evaluation indicator set, a third relationship connection set between the nodes in the third indicator node layer is constructed; Based on the inter-layer index connection, the second relationship connection set, the third relationship connection set and the first indicator node layer, the second indicator node layer and the third indicator node layer, a hierarchical association indicator space is constructed by a topological space algorithm; Based on the hierarchical association indicator space and the association degree on the corresponding connection relationship, the indicator variables in the second indicator node layer and the third indicator node layer are respectively subjected to cyclic community clustering by combining the graph algorithm with the density function in the density clustering algorithm; When the modularity corresponding to each clustering community after community clustering in the second indicator node layer and the third indicator node layer and the density of neighboring nodes corresponding to each node in the corresponding community are a fixed value, the cyclic community clustering is stopped to obtain the second community clustering node group and the third community clustering node group corresponding to the second indicator node layer and the third indicator node layer respectively; Each community clustering node group in the second community clustering node group and the third community clustering node group is taken as a super node, and a hierarchical super node association space is constructed through a hypergraph algorithm.

6. A warehouse performance diagnosis and evaluation method as claimed in claim 5, characterized in that: The step of constructing the hierarchical association index space also includes: Based on the indicator variables between each layer in the hierarchical correlation indicator space, a visualization contribution mapping is set, specifically: At the current moment, the enhanced screening scheduling model is combined with the attribute configuration of the warehouse to be evaluated at the current moment, all indicator variables are screened from the first evaluation indicator set, N variable indicators are screened from the second evaluation indicator set, and M indicator variables are screened from the third evaluation indicator set to construct an evaluation variable indicator set for the warehouse to be evaluated; The evaluation variable index set of the warehouse to be evaluated is input into the hierarchical evaluation model constructed by the comprehensive fuzzy evaluation algorithm combined with the autocorrelation function to obtain the evaluation score corresponding to each evaluation index in the first evaluation index set, and the inter-layer contribution space is constructed by the second autocorrelation contribution space and the third autocorrelation contribution space corresponding to the N and M variable indexes in the screened second and third evaluation index sets, and the inter-layer contribution space corresponding to the third evaluation index to the second evaluation index and the inter-layer contribution of the second evaluation index to the first evaluation index.

7. A warehouse performance diagnosis and evaluation method as claimed in claim 6, characterized in that: The step of constructing the hierarchical association index space also includes: Based on the magnitude and positive and negative of the autocorrelation contribution between two adjacent indicator variables in the third autocorrelation contribution space combined with the preset color type and color gradient, construct an intra-layer visual contribution map between two adjacent indicator variables; The visualization contribution map within the layer is built into the third relationship connection set corresponding to the third indicator node layer to obtain a visualization third indicator node layer. Similarly, the visualization second indicator node layer is obtained by using the second autocorrelation contribution space; Based on the preset color type and color gradient and the size and positive and negative of the inter-layer contribution of the third evaluation indicator to the second evaluation indicator in the inter-layer contribution space, a second inter-layer visualization mapping between the second indicator node layer and the third indicator node layer is obtained. Similarly, a first inter-layer visualization mapping is obtained through the inter-layer contribution of the second evaluation indicator to the first evaluation indicator. The first inter-layer visualization mapping and the second inter-layer visualization mapping are embedded into the first inter-layer index connection and the second inter-layer index connection corresponding to the first indicator node layer and the second indicator node layer and the second indicator node layer and the third indicator node layer, so as to obtain the visualization hierarchical association indicator space corresponding to the warehouse to be evaluated; When the corresponding evaluation and solution push of the warehouse to be evaluated are completed, the inter-layer index connection and the visualization mapping on the corresponding layer intra-layer connection in the visualization second indicator node layer and the visualization third indicator node layer are automatically eliminated, and the visualization mapping corresponding to the current warehouse to be evaluated is marked and the historical evaluation is traced; The above-mentioned evaluation visualization process of the current warehouse to be evaluated is mapped to all warehouses evaluated by distributed control, and the visualization evaluation mapping of the corresponding warehouse is performed.

8. A warehouse performance diagnosis and evaluation method as claimed in claim 7, characterized in that: The process of constructing the difference area assessment result map includes: The spatial three-dimensional coordinates corresponding to the three-dimensional virtual warehouse corresponding to the warehouse to be evaluated are used as pixel projection coordinates; The color and color gradient corresponding to the connection relationship in the visual hierarchical association index space corresponding to the warehouse to be evaluated are used to construct a differential area evaluation result mapping by combining the spatial indicator variables in the space utilization index with the dynamic LOD algorithm; The color and color gradient corresponding to the connection relationship in the visualization hierarchical association indicator space corresponding to the current warehouse to be evaluated are mapped to the pixel projection coordinates corresponding to the current warehouse to be evaluated through the difference area evaluation result mapping to obtain a three-dimensional virtual warehouse with regional visualization.

9. A warehouse performance diagnosis and evaluation method as claimed in claim 8, characterized in that: The enhanced screening scheduling model is constructed by G enhanced screening scheduling sub-models; the G enhanced screening scheduling sub-models are integrated into the edge control nodes corresponding to the corresponding Q warehouses; The screening process of the enhanced screening scheduling sub-model includes: Establish a frequent indicator variable set through the evaluation variable indicator set of the warehouse to be evaluated corresponding to each evaluation of each warehouse; Establishing a frequent index connection between a frequent indicator variable set and a corresponding enhanced screening scheduling sub-model, and embedding the frequent index connection into the corresponding enhanced screening scheduling sub-model; When evaluating the warehouse corresponding to the current enhanced screening scheduling sub-module, the evaluation variable indicator set of the warehouse to be evaluated is indexed from the evaluation association indicator library through frequent index connection combined with the current warehouse attribute configuration to be evaluated, and the index frequency and joint index frequency of the corresponding indicator variable are fed back to the hierarchical association indicator space, and the correlation degree of the corresponding connection relationship annotation within and between layers is adjusted in real time; When the current index needs to evaluate the warehouse evaluation variable indicator set does not belong to any community clustering node group in the hierarchical association indicator space, a new community clustering node group is constructed through the current index needs to evaluate the warehouse evaluation variable indicator set and used as a new supernode in the hierarchical supernode association space to store indicator variables and construct frequent index connections.

10. A warehouse performance diagnosis and evaluation system, which is used to implement a warehouse performance diagnosis and evaluation method according to any one of claims 1 to 9, characterized in that: include: Three-dimensional simulation module, indicator screening module, evaluation module and mapping and recommendation module; The three-dimensional simulation module obtains a distributed three-dimensional virtual warehouse based on different warehouse attributes combined with a three-dimensional modeling algorithm; The indicator screening module obtains a set of differential correlation evaluation indicators based on a distributed three-dimensional virtual warehouse combined with an enhanced screening scheduling model and an evaluation correlation indicator library; The evaluation module obtains a distributed evaluation result grade cluster based on the difference association evaluation indicator set combined with the association evaluation model of each three-dimensional virtual warehouse configuration; The mapping and recommendation module maps the evaluation results to the corresponding three-dimensional virtual warehouse area in real time based on the distributed evaluation result level cluster combined with the preset difference area evaluation result mapping. At the same time, through the configured integrated reasoning model, it generates an evaluation solution and synchronously maps it to the corresponding visualization area of ​​the three-dimensional virtual warehouse.

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