Storage management method, system and equipment of goods frame and medium

Abstract

The invention discloses a storage management method, system and device of a goods frame and a medium. The method comprises the steps of obtaining first product data of a first waste electronic product in a to-be-stored goods frame, second product data of a second waste electronic product in a target goods frame on a target goods shelf, environment data of the target goods shelf and to-be-stored position data of each to-be-stored position in the target goods shelf; generating a first risk category label based on the first product data, and generating a second risk category label based on the second product data; determining a storage conflict index of the to-be-stored goods frame and the target goods frame; calculating a storage adaptation score of the to-be-stored goods frame at each to-be-stored position and determining a to-be-stored position set; determining a storage and transportation path set of each to-be-stored position; determining an electrostatic induction interference intensity distribution sequence and a magnetic interference intensity distribution sequence of each storage transportation path; and determining a target storage and transportation path of the to-be-stored goods frame. The reliability of goods frame storage can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of storage management, in particular to a storage management method and system of a cargo frame, equipment and medium. BACKGROUND

[0002] With the development of resource recycling industry and intelligent warehousing technology, cargo frames, as standardized packaging units for carrying waste electronic products, have been widely used in various electronic waste treatment and warehousing management scenarios. By loading different types of waste electronic products in cargo frames and uniformly performing storage, scheduling and storage operations, it not only helps to improve the processing efficiency, but also provides operational convenience for subsequent disassembly, classification and reuse.

[0003] The prior art determines the storage position of the cargo frame according to the type of waste electronic products in the cargo frame, and transports the cargo frame to the storage position through the shortest transportation path. However, in the actual management process, the physical characteristics of the waste electronic products in each cargo frame are uncertain and different, and the cargo frames may produce uncertain mutual influence in the transportation and storage process, affecting the reliability of the cargo frame storage. SUMMARY

[0004] The present application provides a storage management method, system, equipment and medium of a cargo frame, for improving the reliability of cargo frame storage.

[0005] In a first aspect, a storage management method of a cargo frame is provided. The method is applied to a server and includes: obtaining first product data of first discarded electronic products in a to-be-stored cargo frame, second product data of second discarded electronic products in a target cargo frame of a target shelf, environment data of the target shelf, and to-be-stored location data of each to-be-stored location in the target shelf; generating a first risk category label based on the first product data, and generating a second risk category label based on the second product data; determining a storage conflict indicator of the to-be-stored cargo frame and the target cargo frame based on the first risk category label, the second risk category label, and the environment data; calculating a storage adaptation score of the to-be-stored cargo frame in each to-be-stored location based on the storage conflict indicator and the to-be-stored location data, and determining a to-be-stored location set in which the storage adaptation score is greater than a preset score threshold; determining a storage transportation path set of the to-be-stored cargo frame in each to-be-stored location in the to-be-stored location set; determining electrostatic induction interference intensity distribution sequences and magnetic interference intensity distribution sequences of each storage transportation path in the storage transportation path set based on first magnetic material parameters, first charge distribution characteristics in the first product data, second magnetic material parameters, and second charge distribution characteristics in the second product data; and determining a target storage transportation path of the to-be-stored cargo frame based on the electrostatic induction interference intensity distribution sequences and the magnetic interference intensity distribution sequences.

[0006] Optionally, the first risk category label is generated based on the first product data, specifically including: analyzing damage state parameters in the first product data, identifying structural abnormality characteristics of the first discarded electronic products, and constructing a structural risk factor sequence; comparing chemical substance parameters in the first product data with a preset material database, identifying dangerous chemical components of the first product data, and constructing a chemical risk factor sequence; performing correlation analysis on the structural risk factor sequence and the chemical risk factor sequence, and constructing a joint risk behavior graph; identifying a first risk linkage path in the joint risk behavior graph, and generating the first risk category label according to a severity score of the first risk linkage path, wherein the first risk linkage path is a risk linkage path of the first discarded electronic products in the joint risk behavior graph.

[0007] Optionally, the storage conflict indicator of the to-be-stored goods frame and the target goods frame is determined based on the first risk category label, the second risk category label and the environment data, specifically including: performing interaction influence analysis on the first risk linkage path in the first risk category label and the second risk linkage path in the second risk category label in the joint risk behavior graph to determine a set of risk path nodes between the to-be-stored goods frame and the target goods frame that exist interaction influence; determining the propagation connection relationship between the risk path nodes in the set of risk path nodes and calculating the conflict intensity value of each risk path node; determining the conflict source based on the conflict intensity value and determining the storage conflict indicator in combination with the environment data.

[0008] Optionally, the conflict source is determined based on the conflict intensity value, and the storage conflict indicator is determined in combination with the environment data, specifically including: determining the risk path node with the conflict intensity value greater than a preset conflict intensity threshold as the conflict source; determining the spatial position constraint between the to-be-stored goods frame and the target goods frame based on the risk type of the conflict source; analyzing the risk characteristics of the conflict source to determine the environment isolation mode between the to-be-stored goods frame and the target goods frame; determining the achievable degree of the spatial position constraint and the environment isolation mode in combination with the environment data, and determining the storage conflict indicator according to the achievable degree.

[0009] Optionally, the storage adaptation score of the to-be-stored goods frame in each of the to-be-stored positions is calculated based on the storage conflict indicator and the to-be-stored position data, specifically including: analyzing the to-be-stored position data to determine the spatial adjacency relationship between each of the to-be-stored positions in the target goods frame and the target goods frame to construct an adjacency influence matrix; calculating the conflict influence value of each of the to-be-stored positions according to the adjacency influence matrix and the storage conflict indicator; setting a corresponding environment mitigation factor for each to-be-stored position; and performing weighted calculation on the conflict influence value and the environment mitigation factor of each of the to-be-stored positions to obtain a storage adaptation degree score.

[0010] Optionally, the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence of each of the storage and transportation paths in the storage and transportation path set are determined based on the first magnetic material parameter, the first charge distribution feature in the first product data, the second magnetic material parameter, the second charge distribution feature in the second product data, and the environment data, specifically including: determining the environment conductor characteristic and the shielding structure data of each preset position point in each of the storage and transportation paths based on the environment data to obtain a path environment interference response set; performing coupling analysis on the first charge distribution feature and the second charge distribution feature to determine the electrostatic induction coupling strength of each of the preset position points in each of the storage and transportation paths; performing magnetic interference analysis on the first magnetic material parameter and the second magnetic material parameter to determine the magnetic interference superposition strength of each of the preset position points in each of the storage and transportation paths; performing interference intensity mapping on the path environment interference response set and the electrostatic induction coupling strength to form an electrostatic induction interference distribution sequence; and performing interference intensity mapping on the path environment interference response set and the magnetic interference superposition strength to form a magnetic interference distribution sequence.

[0011] Optionally, the target storage and transportation path of the to-be-stored cargo box is determined based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, specifically including: identifying the interference preset position points in each of the storage and transportation paths, where the electrostatic induction coupling strength is greater than a preset electrostatic interference intensity threshold value and the magnetic interference superposition strength is greater than a magnetic interference intensity threshold value, based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence; eliminating the path segments between the continuous interference preset position points in each of the storage and transportation paths to obtain a candidate path segment set; analyzing the electrostatic interference fluctuation feature of the electrostatic induction interference intensity distribution sequence and the magnetic interference fluctuation feature of the magnetic interference intensity distribution sequence; determining the path segment topology relationship of all the candidate path segments in the candidate path segment set to obtain a candidate target storage and transportation path, and performing connectivity verification on the candidate target storage and transportation path; calculating the interference stability score of the candidate target storage and transportation path subjected to the connectivity verification based on the electrostatic interference fluctuation feature and the magnetic interference fluctuation feature, and taking the candidate target storage and transportation path with the highest interference stability score as the target storage and transportation path.

[0012] In a second aspect of the present application, a storage management system of a pallet is provided, comprising: an acquisition module configured to acquire first product data of first discarded electronic products in a to-be-stored pallet, second product data of second discarded electronic products in a target pallet in a target shelf, environment data of the target shelf, and to-be-stored location data of each to-be-stored location in the target shelf; a generation module configured to generate a first risk category label based on the first product data, and generate a second risk category label based on the second product data; a first determination module configured to determine a storage conflict indicator of the to-be-stored pallet and the target shelf based on the first risk category label, the second risk category label, and the environment data; a calculation module configured to calculate a storage adaptation score of the to-be-stored pallet in each to-be-stored location based on the storage conflict indicator and the to-be-stored location data, and determine a to-be-stored location set in which the storage adaptation score is greater than a preset score threshold; a second determination module configured to determine a storage transportation path set of the to-be-stored pallet in each to-be-stored location in the to-be-stored location set; a third determination module configured to determine electrostatic induction interference intensity distribution sequences and magnetic interference intensity distribution sequences of each storage transportation path in the storage transportation path set based on first magnetic material parameters and first charge distribution characteristics in the first product data, second magnetic material parameters and second charge distribution characteristics in the second product data, and the environment data; and a fourth determination module configured to determine a target storage transportation path of the to-be-stored pallet based on the electrostatic induction interference intensity distribution sequences and the magnetic interference intensity distribution sequences.

[0013] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface, and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method of any one of the above aspects.

[0014] In a fourth aspect of the present application, a computer-readable storage medium is provided, and the computer-readable storage medium stores instructions, when the instructions are executed, the method of any one of the above aspects is performed.

[0015] In summary, the one or more technical solutions provided by the present application have at least the following technical effects or advantages: 1. By acquiring primary product data, secondary product data, environmental data, and data on the storage location, a comprehensive understanding of the current status of the storage frames and target shelves is obtained. Combined with generated primary and secondary risk category labels, storage conflict indicators are determined, enabling an assessment of potential interference risks between storage frames. Furthermore, based on the indicators and location data, a storage suitability score is calculated, and a set of storage locations is selected to improve the accuracy of storage suitability assessment. On this basis, combining magnetic material parameters and charge distribution characteristics from the product data with environmental data, the intensity distribution of electrostatic induction interference and magnetic interference along each storage and transportation path is analyzed. Ultimately, the target storage and transportation path with the least interference impact is determined, thereby improving the safety and reliability of the storage frames during transportation and storage.

[0016] 2. Based on the damage status parameters in the first product data, the structural risk factor sequence of the first waste electronic product is determined, and based on the chemical substance parameters, the chemical risk factor sequence is determined. This can comprehensively characterize the risk sources of waste electronic products from two dimensions: physical structure and chemical composition. Furthermore, a joint risk behavior map is constructed by the structural risk factor sequence and the chemical risk factor sequence. Based on the first risk linkage path and its corresponding severity level reflected in the map, a first risk category label is generated. This makes the risk label not only reflect the static risk of a single factor, but also reflect the dynamic risk evolution trend under the linkage of multiple factors. This improves the accuracy and foresight of risk assessment, provides more in-depth risk data support for subsequent storage conflict judgment and path interference analysis, and enhances the system's intelligent judgment capability on the storage adaptability of waste electronic products in complex scenarios.

[0017] 3. By analyzing the first risk linkage path in the first risk category label and the second risk linkage path in the second risk category label, a set of risk path nodes with interactive influence between the storage container and the target container is identified. The propagation connection relationship between each risk path node in this set is further determined, and the conflict intensity value is calculated, thereby achieving quantitative identification of potential risk propagation chains and their conflict levels. Based on this, risk path nodes with conflict intensity values ​​exceeding a preset conflict intensity threshold are identified as conflict sources. Combining the risk type and risk characteristics corresponding to the conflict sources, the spatial location constraints and environmental isolation methods that the storage container and the target container must meet are determined. Then, the feasibility of the constraints and isolation methods is evaluated by combining the propagation connection relationship and environmental data, and storage conflict indicators are determined accordingly. This allows for dynamic and accurate identification of high-risk interaction relationships that may arise between containers under multi-dimensional risk factor linkage conditions, improving the system's ability to make safe judgments and formulate intervention strategies for container deployment in complex warehousing environments. This effectively reduces the uncertainty risk caused by storage interference, ensuring the safety and robustness of intelligent electronic waste warehousing management. Attached Figure Description

[0018] Figure 1 is a system architecture schematic diagram of an embodiment of a kind of storage management method of goods frame or a kind of storage management system of goods frame in the embodiment of the application; Figure 2 is a flow schematic diagram of a kind of storage management method of goods frame in the embodiment of the application; Figure 3 is the structure schematic diagram of the system of an embodiment of a kind of storage management system of goods frame in the embodiment of the application; Figure 4 is the structure schematic diagram of electronic equipment in the embodiment of the application.

[0019] Figure legend: 301, acquisition module;302, generation module;303, first determination module;304, calculation module;305, second determination module;306, third determination module;307, fourth determination module;401, processor;402, communication bus;403, user interface;404, network interface;405, memory. DETAILED DESCRIPTION

[0020] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiment of the specification. Obviously, the described embodiments are only a part of the embodiments of the application, not all embodiments.

[0021] In the description of the embodiments of the application, "for example" or "for example" and the like are used to represent as an example, illustration or explanation. Any embodiment or design scheme described as "for example" or "for example" in the embodiments of the application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of "for example" or "for example" and the like is intended to present the relevant concept in a specific manner.

[0022] Figure 1 An exemplary system architecture 100 that can apply an embodiment of a kind of storage management method of goods frame or a kind of storage management system of goods frame of the application is shown.

[0023] As Figure 1 shown, the system architecture 100 can be used to implement a kind of storage management method of goods frame provided by the application, and the system architecture 100 includes a plurality of data acquisition terminals 101, 102, 103, a network 104 and a server 105.

[0024] The data acquisition end 101, 102, and 103 can be deployed at different positions in the warehouse site, and is configured to acquire first product data of the first waste electronic product in the to-be-stored container, second product data of the second waste electronic product in the target container on the target shelf, environment data of the target shelf, and to-be-stored location data of each to-be-stored location. The product data can include, but is not limited to, damage state parameters, chemical substance parameters, magnetic material parameters, and charge distribution characteristics. The environment data can include temperature and humidity, electromagnetic interference level, and electrostatic value. The to-be-stored location data can include spatial coordinates, structural dimensions, and ventilation conditions.

[0025] The data acquisition end can include an industrial camera, a temperature and humidity sensor, an electromagnetic sensor, and an electrostatic detector, and is configured to realize rapid identification and data acquisition of the container and the waste electronic product in the container. The data acquisition end can upload the acquired raw data to the server 105 through local edge computing or directly through the network 104. The server 105 can be a server that provides various services, such as a background server for processing data displayed on the data acquisition end 101, 102, and 103. The background server can analyze and process the received data, and can feed back the processing result (such as an identification result) to the terminal device.

[0026] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software or software modules (such as multiple software or software modules for providing distributed services), or as a single software or software module. In this case, no specific limitation is made. It should be understood that Figure 1 The number of terminal devices, networks, and servers in the above description is only illustrative. According to the implementation needs, there can be any number of terminal devices, networks, and servers.

[0027] Figure 2 FIG. 1 is a flow diagram of a container storage management method according to an embodiment of the present application.

[0028] Referring to FIG. 1, Figure 2 The container storage management method according to an embodiment of the present application is applied to a server, and the method includes the following steps. In step S201, the server obtains first product data of the first discarded electronic product from the to-be-stored goods frame through a communication connection with a plurality of data acquisition terminals deployed in the intelligent storage site. The to-be-stored goods frame is a goods frame to be placed on the goods shelf. The first product data of the to-be-stored goods frame includes damage state parameters for describing the physical state of the discarded electronic product, magnetic material parameters and chemical substance parameters for identifying the material properties thereof, and charge distribution characteristics for characterizing the electrostatic characteristics, etc. The damage state parameters can be obtained by a video acquisition device installed above or on the side of the goods frame in combination with an image recognition algorithm, for example, by a convolutional neural network to identify and classify appearance damage, deformation, cracks, etc. The magnetic material parameters are obtained by reading the material code stored in the radio frequency identification tag bound to the discarded electronic product, and classifying the materials in combination with the background database, such as judging whether there is a ferromagnetic element. The chemical substance parameters are obtained by comparing with the list of hazardous chemicals in the recycling material database to identify whether there are potential hazardous components such as lithium, mercury, lead, etc. The charge distribution characteristics can be obtained by an electrostatic induction sensor embedded in the bottom or side of the goods frame to collect the overall surface potential distribution data of the goods frame, and the frequency domain characteristics are extracted through Fourier analysis to construct an electrostatic coupling risk model of the goods frame during transportation and stacking. At the same time, the server also obtains second product data of the second discarded electronic product stored in the target goods frame on the target goods shelf, which includes parameters corresponding to the first product data, ensuring that the input information has the same dimension when generating the second risk category label, so as to perform risk path analysis and interference coupling judgment.

[0029] On the basis of collecting product data, the server further collects environmental data of the target goods shelf. The environmental data mainly includes environmental conductor characteristics, shielding structure data, temperature and humidity parameters, electrostatic potential and electromagnetic interference background values of the space region where the target goods shelf is located. The environmental conductor characteristics refer to the conductivity of the goods shelf body and the surrounding structure materials, such as whether they are metal components, whether they are grounded, etc., which affect the boundary conditions of electrostatic induction and magnetic field diffusion; the shielding structure data are used to judge whether the goods shelf has electromagnetic shielding devices such as insulating plates and metal mesh covers; the temperature and humidity parameters are collected by temperature and humidity sensors to judge the influence of the environment on electrostatic accumulation; the electrostatic potential is collected by electrostatic voltage sensors arranged in the field to reflect the local electrostatic field intensity distribution; and the electromagnetic interference background value is recorded by an inductive coil array to evaluate whether there is a high-frequency interference source in the environment.

[0030] In addition, in order to support subsequent adaptive score calculation and transportation path planning, the server also needs to collect the to-be-stored position data of each to-be-stored position in the target shelf. The data includes spatial coordinate information, structural size parameters, ventilation condition indicators, environmental isolation capability parameters corresponding to the position, and position shielding capability parameters of each to-be-stored position. The spatial coordinate information is used to calculate the relative position relationship between the goods frame and the surrounding goods frame; the structural size parameters are used to judge whether the goods frame can be physically matched and placed in the target position; the ventilation condition indicators are collected by a wind speed sensor and are used to infer the local heat and chemical gas diffusion capability; the environmental isolation capability parameters reflect whether the surrounding of the position has physical isolation facilities such as partitions, sealed cabins, etc.; and the shielding capability parameters are based on the number and material of the shielding structure near the position, and the attenuation capability of the shielding structure to electromagnetic interference is calculated through modeling.

[0031] S202, generating a first risk category label based on the first product data and generating a second risk category label based on the second product data; In step S202, the first risk category label and the second risk category label are used as the identification of the system to identify the potential safety risk level of the goods frame content, and are important basis for subsequent judgment of whether there is a storage conflict between the goods frames and whether they can be placed adjacent to each other. In order to ensure the accuracy and interpretability of the label generation, the system adopts a behavior graph modeling method based on multi-dimensional risk factors, and fuses structural and chemical risk characteristics, so as to generate label information that can reflect the risk linkage mechanism. Generating a first risk category label based on the first product data can include the following steps: analyzing the damage state parameters in the first product data, identifying the structural abnormality characteristics of the first waste electronic product, and constructing a structural risk factor sequence; comparing the chemical substance parameters in the first product data with a preset material database, identifying the hazardous chemical components of the first product data, and constructing a chemical risk factor sequence; performing correlation analysis on the structural risk factor sequence and the chemical risk factor sequence, and constructing a joint risk behavior graph; identifying a first risk linkage path in the joint risk behavior graph, and generating the first risk category label according to the severity score of the first risk linkage path, wherein the first risk linkage path is a risk linkage path of the first waste electronic product in the joint risk behavior graph.

[0032] Specifically, the server first determines a structural risk factor sequence of the first waste electronic product based on a damage state parameter in the first product data. The damage state parameter is usually derived from a cargo box surface image or sensor data. The server can classify and encode structural abnormalities such as cracking, deformation, and shell damage through image recognition algorithms, and conduct failure propagation analysis in combination with the internal structure of the product. For example, when detecting that the battery compartment is cracked and the electrode is exposed, the system maps it to a high-risk structural risk factor “electrochemical exposure”, and adds the high-risk structural risk factor to the structural risk factor sequence. The structural risk factor sequence reflects potential failure paths related to the structural integrity of the product, providing a physical basis for subsequent risk linkage modeling.

[0033] After generating the structural risk factor sequence, the server further determines a chemical risk factor sequence of the first waste electronic product based on a chemical substance parameter in the first product data. The chemical substance parameter is usually obtained by comparison with a product material database. The system determines whether the extracted bill of materials contains volatile, flammable, corrosive, or toxic chemical components. For example, the system may identify a waste battery containing lithium, nickel, cobalt, and other chemical elements as a “high-activity positive electrode material” or “electrolyte leakage risk” chemical risk factor. The server establishes a mapping relationship between chemical components and risk characteristics to construct the risk characteristics of the waste electronic product in the chemical layer, thereby supplementing the chemical reaction potential and medium diffusion risk that the structural risk factor sequence cannot fully reflect.

[0034] Subsequently, the server performs correlation analysis on the structural risk factor sequence and the chemical risk factor sequence to construct a joint risk behavior graph that reflects the linkage relationship between multi-dimensional risk factors. The joint risk behavior graph is implemented in a graph structure modeling manner, where each risk factor is defined as a node in the graph, and the causal triggering relationship between risk factors under certain physical, chemical, or environmental conditions is defined as a directed edge of the graph. The server compares and analyzes a pre-set risk linkage rule base and a large-scale historical accident data set to determine which structural risk factors and which chemical risk factors have linkage mechanisms. For example, when the structural risk factor sequence contains a “battery shell damage” node and the chemical risk factor sequence contains an “electrolyte leakage” node, according to the battery structure failure and chemical leakage linkage path summarized in the rule base, a directed edge from “battery shell damage” to “electrolyte leakage” can be established in the graph, indicating that the former may induce the latter. The establishment of such linkage edges relies on a pre-established risk factor triggering matrix, in which the linkage probability between each pair of factors is provided by historical case statistics, physical model simulation, or expert knowledge system, for quantifying the dynamic correlation strength between factors.

[0035] After the construction of the graph structure is completed, the server identifies all possible risk linkage paths in the joint risk behavior graph through a graph traversal algorithm (such as depth-first traversal), and filters out the paths ending with high-risk consequence event nodes as a candidate path set. The server further performs severity assessment on each candidate path, which consists of three dimensions: path length, path linkage strength, and path terminal event strength. Among them, the path length represents the complexity of the risk factor linkage chain, the path linkage strength is the product of the linkage probabilities of the edges in the path, and the path terminal event strength is the danger level of the event represented by the path endpoint node (for example, the “thermal runaway explosion” event strength is level 5, and the “local corrosion” event strength is level 2). The server calculates the comprehensive severity score of the path according to the preset comprehensive scoring function, which weights the above three indicators. An example of the scoring function is: S = a x L + b x P + g x E, where S is the severity score, L is the path length, P is the linkage strength, E is the terminal event strength, and a, b, and g are empirical weight coefficients.

[0036] A second risk category label is generated based on the same principle through second product data. The second product data is multi-dimensional parameter information collected for the discarded electronic products in the target bin already stored on the target shelf, including damage state parameters, chemical substance parameters, magnetic material parameters, and charge distribution characteristics, etc. The data types are consistent in structure with the first product data to ensure the equivalence of the subsequent risk labels and the feasibility of risk path comparison.

[0037] In the specific implementation process, the server will also first analyze the damage state parameters in the second product data, identify the structural abnormality characteristics of the discarded electronic products in the target bin, generate a sequence of structural risk factors, and extract the corresponding chemical risk factor sequence in combination with the chemical substance parameters. Then, the structural risk factor sequence and the chemical risk factor sequence are fused and modeled to construct the joint risk behavior graph of the second discarded electronic products. Through graph analysis technology, the second risk linkage path is identified and extracted, and combined with the path severity level, the second risk category label is finally generated. The second risk category label reflects the risk linkage mechanism that the discarded electronic products in the target bin may trigger under the current structural state and material properties and its severity, and is one of the core comparison bases for the subsequent system to judge whether the to-be-stored bin can be safely stored adjacent to the target bin.

[0038] S203, determining a storage conflict indicator of the to-be-stored bin and the target bin based on the first risk category label, the second risk category label, and the environment data; In this embodiment, to realize the safety coexistence evaluation between the to-be-stored goods frame and the existing target goods frame on the target goods shelf, after the first risk category label and the second risk category label are generated, based on the first risk category label and the second risk category label and the environmental data related to the target goods shelf, a storage conflict index between the to-be-stored goods frame and the target goods frame is determined. The storage conflict index is used to reflect the adverse interaction effect that may be caused by the risk factors between the to-be-stored goods frame and the target goods frame in the spatial proximity state, so as to identify the potential risk in advance before the spatial arrangement, and avoid the occurrence of the associated effects such as structural failure, chemical reaction or electromagnetic interference. Steps S2031-S2033 can be included: S2031, the first risk linkage path in the first risk category label and the second risk linkage path in the second risk category label are interactively analyzed in the joint risk behavior graph to determine a set of risk path nodes that exist between the to-be-stored goods frame and the target goods frame and have interaction effects; To realize accurate identification of the storage conflict, the server first analyzes the set of risk path nodes that exist in the two paths and have interaction effects according to the first risk linkage path contained in the first risk category label and the second risk linkage path contained in the second risk category label. The risk path node is a way of structurally expressing the potential hazard source of the product, and each node represents a risk point related to structural abnormalities, chemical activity or charge / magnetic characteristics. The server can identify a pair of nodes that have factor overlap, similar trigger mechanism or complementary reaction link in the two risk linkage paths through a graph matching algorithm, and classify the pair of nodes that have factor overlap, similar trigger mechanism or complementary reaction link into the set of interaction nodes. For example, if there is a “lithium battery thermal runaway” node in the first path and a “high-density metal powder deposition” node in the second path, the system determines that the two may induce metal spontaneous combustion in a high-temperature scene, constituting an interaction risk.

[0039] S2032, the propagation connection relationship between the risk path nodes in the set of risk path nodes is determined, and the conflict intensity value of each risk path node is calculated; Step S2032 quantifies the propagation ability between the risk factors to determine which nodes have higher risk transmission, thereby laying a foundation for subsequent conflict source identification and generation of the storage conflict index.

[0040] In the implementation process, the server first extracts a subgraph from the set of risk path nodes based on the node topology structure in the constructed joint risk behavior graph, and constructs a propagation connection relationship graph. The propagation connection relationship graph is used to describe whether there is a direct or indirect risk transmission path between two risk nodes. For example, if node A represents "battery thermal expansion" and node B represents "electrolyte leakage", the server will determine whether there is a physical or chemical triggering relationship between the two according to the pre-defined causal chain in the graph, and generate a directed edge to represent the propagation direction. The propagation connection relationship not only includes the connection state between nodes, but also records the propagation probability, path length, trigger delay and other propagation attributes of the connection edge.

[0041] After establishing the propagation connection relationship graph, the server further calculates the conflict intensity value of each risk path node through a path evaluation algorithm. The conflict intensity value is used to reflect the influence breadth and depth of a certain risk node in the entire propagation network, which is determined by multiple factors, including the risk level of the node, the number of downstream nodes connected, the weighted length of the propagation path, and the environmental sensitivity coefficient. The server can use a graph centrality algorithm improved based on PageRank, combined with the propagation probability between nodes on each propagation path, to assign a propagation influence factor to each node. The propagation influence factor is then weighted with the risk level of the node itself to generate the final conflict intensity value. The environmental sensitivity coefficient is dynamically adjusted according to the environmental data of the target shelf to adjust the propagation probability, for example, in areas with high temperature, high humidity or high static field strength, the propagation of certain risk paths may be amplified, and the system will accordingly increase the conflict intensity value of the nodes on that path.

[0042] For example, when the identified risk path node set contains nodes such as "abnormal lithium ion migration", "shell rupture", "electrolyte leakage", and "flammable gas accumulation", the server finds multiple high-probability excitation paths after analyzing the propagation connection relationship, and in the current environment, the ventilation is poor and the electric field strength is high, thus the influence ability of the "electrolyte leakage" node is significantly increased. The conflict intensity value calculation result further shows that such intermediate nodes have strong triggering ability to multiple downstream high-risk nodes, and therefore may be identified as conflict sources in subsequent steps. Through the construction of the propagation connection relationship and the calculation of the conflict intensity value, not only can the linkage mechanism between risk factors be structurally modeled, but also the risk nodes can be ranked based on the propagation ability, improving the modeling accuracy of the system for the multi-source risk interaction process and providing more targeted risk control strategy input for the warehouse system.

[0043] S2033, determine a conflict source based on the conflict intensity value, and determine the storage conflict indicator in combination with the environmental data.

[0044] To further quantify the real risk linkage capability between the to-be-stored goods frame and the target goods frame and develop intervention strategies, after the propagation connection relationship of the risk path node set is constructed and the conflict intensity value is calculated, the conflict source is determined based on the conflict intensity value, and the storage conflict index is determined in combination with the propagation connection relationship, the conflict source and the environment data. Step S2033 is not only a further excavation of the risk linkage mechanism, but also a key link to realize risk regulation and safety layout strategy output. The core is to identify the high-risk starting source point from the complex risk propagation network, and deduce the executable space and environment control constraints based on the risk characteristics of the source point, so as to provide structured and quantifiable risk index input for the system. It can include the following steps: determining the risk path node with a conflict intensity value greater than a preset conflict intensity threshold as a conflict source; determining the spatial position constraint between the to-be-stored goods frame and the target goods frame based on the risk type of the conflict source; analyzing the risk characteristics of the conflict source to determine the environmental isolation mode between the to-be-stored goods frame and the target goods frame; combining the environment data to determine the realizability of the spatial position constraint and the environmental isolation mode, and determining the storage conflict index according to the realizability.

[0045] In the specific implementation process, the server first screens the conflict intensity value of each risk path node, and marks the node with a conflict intensity value higher than a preset conflict intensity threshold as a conflict source. The preset conflict intensity threshold is set by historical accident cases, experience models or expert knowledge, and is used to distinguish ordinary linkage nodes from risk starting points with strong excitation capability. The conflict source is the node in the risk linkage chain that is most likely to induce secondary risks, and usually has high propagation probability, high environmental sensitivity or multi-path linkage capability. For example, if a node represents "high-voltage capacitor rupture", and the node is located at the starting position in multiple risk paths and has a high triggering probability in a high-humidity environment, then its conflict intensity value will often exceed the set threshold, and the system identifies it as a conflict source accordingly.

[0046] After identifying the conflict source, the server further analyzes the risk type of the conflict source to determine the spatial position constraint between the to-be-stored cargo box and the target cargo box. The risk type is a classification of the conflict source in the risk behavior dimension, such as structural risk, thermal runaway risk, electromagnetic interference risk, or chemical diffusion risk, and different types of risks have different sensitivities to spatial adjacency relationships. The system generates corresponding spatial constraint rules based on the spatial coupling model between the risk type and the cargo box. The spatial position constraint refers to the physical arrangement requirements proposed based on the risk type of the conflict source, such as requiring a minimum distance between cargo boxes, avoiding stacking in a ventilation blind area, or setting an area range that cannot be adjacent, etc. The environmental isolation method is an environmental intervention measure determined according to the risk characteristics of the conflict source, such as deploying electrostatic shielding devices, enabling local exhaust systems, setting heat insulation materials, or increasing the humidity of the area, etc. The proposal of the above two types of control measures is derived from the comprehensive modeling of the risk propagation mechanism and environmental response characteristics of the conflict source, and is an important means to achieve fine-grained warehouse risk regulation.

[0047] After the spatial position constraint is generated, the server also determines the environmental isolation method based on the risk characteristics of the conflict source. The risk characteristics are a detailed description of the conflict source in terms of propagation behavior, environmental response, triggering mechanism, etc., such as whether it has high flammability, whether it is prone to discharge in a strong electric field, whether it releases corrosive gas, etc. The server selects appropriate isolation methods from the available environmental isolation resources in the warehouse environment based on the adaptation relationship between risk characteristics and environmental intervention means, such as setting metal shielding plates, increasing negative pressure exhaust systems, introducing electrostatic neutralization equipment, or enabling local closure mode in the cabin. For example, if the conflict source is "static electricity accumulation triggering electric arc discharge", the risk characteristics are high electric field sensitivity and discharge flammability, and the system will select electrostatic shielding layer and humidity control unit around the cargo box as isolation means.

[0048] To quantify the evaluation of the realizability of the spatial position constraint and the environmental isolation method, the server first calls the environmental data and structural parameter information of the target shelf to build a complete spatial layout model and environmental resource configuration model. The spatial layout model mainly includes the three-dimensional coordinates of each shelf unit, the channel width, the cargo box placement density and arrangement method, etc., which are used to judge whether the physical feasibility of implementing spatial constraints is available. The environmental resource configuration model covers the types of environmental control equipment configured in each storage area and their current working states, such as wind speed control systems, temperature and humidity adjustment units, electromagnetic shielding devices, electrostatic neutralizers, etc., which are used to judge whether the required environmental isolation requirements can be met.

[0049] In the evaluation process of the space position constraint realizable degree, the server calculates whether the minimum safety distance required by the conflict source can be realized in the current target shelf area based on the space layout model. The evaluation indicators include the number of available spaces, the actual distance between adjacent shelves, the ventilation channel width between storage units, etc. If the calculation result shows that the current area can meet the required distance and will not cause shelf congestion or logistics bottlenecks, the evaluation is high realizable degree; if the space is limited and cannot meet the layout requirements, the evaluation is low realizable degree.

[0050] In the evaluation process of the environment isolation method realizable degree, the server retrieves whether the target shelf area has the required environmental intervention equipment according to the environmental resource configuration model, and evaluates its running state and control ability. For example, when the conflict source is an electrostatic sensitive node, the server detects whether the current area is deployed with an electrostatic shielding layer and whether it has an automatic humidity adjusting device, and analyzes its suppression ability to the local electrostatic field strength. If the system judges that the distribution density, response speed and control accuracy of these devices can meet the suppression needs of the conflict source, it is determined that this environment isolation method has high realizable degree; otherwise, if the equipment is missing, the running ability is insufficient or the area coverage is insufficient, the evaluation is low realizable degree.

[0051] The server marks the evaluation results of the above two dimensions as the first realizable degree of space position constraint and the second realizable degree of environment isolation method respectively, and further constructs a comprehensive realizable degree model. This model evaluates the feasibility of the overall risk control strategy by weighting and fusing the first realizable degree and the second realizable degree. The weight coefficients of the weighting and fusion are dynamically adjusted according to the risk type of the conflict source, for example, for heat diffusion type conflict sources, the importance of space constraint is higher, so the weight of the first realizable degree is larger; and for electromagnetic interference type conflict sources, the environment isolation measures are dominant, so the weight of the second realizable degree is higher.

[0052] The comprehensive realizable degree, as an important input of the final storage conflict indicator, is used to judge whether the adjacent storage operation of the to-be-stored shelf is allowed at the current target position. If the evaluation result shows that the comprehensive realizable degree is high, it means that the system has sufficient environmental and structural resources to support the corresponding risk control strategy, so the storage conflict indicator is set to a low risk level and the shelf storage is allowed; if the comprehensive realizable degree is low, it means that the current environment cannot effectively implement the necessary prevention and control measures, so the storage conflict indicator is set to a high risk level, and the system will refuse the storage request at this position and prompt to select other suitable positions or adjust the surrounding environmental configuration.

[0053] Finally, the server outputs a storage conflict index between the to-be-stored goods frame and the target goods frame according to the degree of realization of the spatial constraint and the environmental isolation mode. The index is an important parameter for the system to determine whether the goods frame can be adjacently arranged, and reflects not only the potential strength of the risk linkage relationship, but also the adaptation degree of the current environment to the risk intervention ability. For example, in an actual application scenario, if the to-be-stored goods frame contains a damaged lithium battery pack and the target goods frame is a bulk circuit board assembly containing magnesium powder, the system identifies the conflict source as “leakage of lithium battery flammable”, the spatial constraint requires separate isolation of 3 meters, and the environmental isolation mode requires a metal fireproof cabin, but the current goods shelf has no independent cabin position. The system will output a high-level storage conflict index, prompting the need to replace the storage area or delay the warehousing operation.

[0054] S204, calculating a storage adaptation score of the to-be-stored goods frame at each of the to-be-stored positions based on the storage conflict index and the to-be-stored position data, and determining the to-be-stored positions with a storage adaptation score greater than a preset score threshold as a to-be-stored position set; To realize quantitative analysis of the risk adaptation degree of the to-be-stored goods frame at different to-be-stored positions, step S204 considers the risk linkage between goods frames, introduces warehouse structure and environmental factors, identifies and selects the positions that best meet the safety storage requirements under the current environmental conditions, thereby reducing hidden dangers in the storage process and improving the safety and intelligence of the overall warehouse system. It can include the following steps: analyzing the to-be-stored position data to determine the spatial adjacency relationship between each to-be-stored position in the target goods shelf and the target goods frame to construct an adjacency influence matrix; calculating the conflict influence value of each to-be-stored position according to the adjacency influence matrix and the storage conflict index; setting a corresponding environmental mitigation factor for each to-be-stored position; and weighting the conflict influence value and the environmental mitigation factor of each to-be-stored position to obtain a storage adaptation degree score.

[0055] In the specific implementation process, first, the spatial adjacency relationship between each to-be-stored position and the target goods frame is extracted based on the to-be-stored position data to construct an adjacency influence matrix. The adjacency influence matrix is a two-dimensional matrix used to describe the mutual influence strength between goods frames in space. The elements in the adjacency influence matrix represent the spatial distance, relative orientation, airflow path overlap degree, and other information between the to-be-stored position and the corresponding position of the target goods frame. The spatial adjacency relationship not only determines the physical channel of risk propagation, but also affects the linkage possibility when a risk event occurs. For example, if two goods frames are located in the same channel and are separated by only one layer of goods shelves, the linkage risk is significantly higher than that of goods frames located in different temperature control areas or ventilation areas.

[0056] After the adjacency influence matrix is constructed, the server further calculates a conflict influence value of each to-be-stored position based on a spatial adjacency relationship between each to-be-stored position and a conflict source in the target goods frame in the adjacency influence matrix and in combination with the determined storage conflict indicators, for quantifying a potential risk exposure degree of the position under the current shelf structure and risk environment. The conflict influence value is a capability of the position in a specific spatial position to be excited or infected by a risk linkage due to the existence of a high-risk goods frame in the vicinity, and a core role thereof is to provide a spatial risk evaluation reference value with strong comparability and clear calculation logic for the system.

[0057] In the calculation process, first, for each conflict source node in the target goods frame, corresponding risk feature parameters are extracted, including a risk propagation path length of the conflict source in the joint risk behavior graph, a risk propagation probability under current environmental conditions, and a risk level labeled by the conflict source in the risk category label. At the same time, according to the information recorded in the adjacency influence matrix, the spatial distance between the current to-be-stored position and the conflict source is obtained and standardized as a distance coefficient. Subsequently, the server substitutes the above four types of parameters: distance coefficient, path length, propagation probability, and risk level into a weighted superposition model for fusion calculation. In the weighted superposition model, the server assigns a preset weight to each risk factor to reflect its importance in the comprehensive risk evaluation, and performs standardization transformation on each parameter to enable each parameter to participate in total value operation in the same numerical scale. Among them, the distance coefficient and the path length are transformed by reciprocal to reflect the risk space coupling characteristics that the closer the distance, the more dangerous and the shorter the path, the easier to excite; the propagation probability is directly involved in the calculation as a standardized value to reflect the excitation possibility of the conflict source in the current environment; and the risk level is mapped to a discrete or continuous value to measure the potential harm degree caused by the risk event once it occurs. The server adds the distance coefficient, the path length, the propagation probability, and the risk level after being multiplied by the corresponding weight coefficients to form a single-point risk exposure score between the to-be-stored position and the conflict source, and finally adds the scores of all conflict sources to form the total conflict influence value of the position.

[0058] Through the calculation of the conflict influence value, the system can comprehensively depict the exposure degree of each to-be-stored position when facing multiple-source and multiple-type risk linkages, thereby providing a risk basis for subsequent adaptation scoring. For example, if a to-be-stored position is close to two high-risk chemical storage frames, and the two frames have high propagation probability and high risk level in the graph, the conflict influence value of the position will be significantly increased, indicating that the position has a high possibility of linkage excitation under the current spatial layout, and is not suitable as a recommended scheme for the to-be-stored goods frame. If another position is spatially adjacent but has a long risk path and low propagation probability, the conflict influence value of the position is relatively low, and the position can be a better candidate position.

[0059] To not completely lose the storage utilization possibility of the area due to local high risk, the server further introduces isolation condition data and shielding ability data corresponding to each to-be-stored location in the environment data into the environment relief factor. The isolation condition data includes information of whether there is a physical partition, ventilation isolation, independent air flow channel and other isolation structure information at the location, and the shielding ability data represents the shielding ability level of the location to risk factors such as electromagnetic interference, heat diffusion or electrostatic induction. Based on a preset relief ability evaluation model, the server calculates the environment relief factor of the location in each type of risk corresponding relief mechanism, and the higher the value, the stronger the risk buffering ability of the location, which helps to inhibit risk transmission.

[0060] Finally, the server weights and fuses the conflict influence value of each to-be-stored location and the corresponding environment relief factor to obtain the final storage adaptation degree score of the location. The higher the storage adaptation degree score, the stronger the safety adaptation ability of the location in the face of the current cargo box combination risk, and the system sorts all to-be-stored locations according to this and selects the part with a score value higher than a preset score threshold as the final to-be-stored location set. The preset score threshold is determined according to system operation requirements, safety specifications, historical data statistics and multi-objective optimization strategy. In the storage adaptation degree score calculation process, the system can dynamically adjust the weighting proportion according to the risk type, for example, for heat runaway risk, the ventilation and heat isolation ability is given priority to; for electrostatic risk, the weight of electromagnetic shielding factor is increased.

[0061] A typical example is given: in an actual warehouse scene, the to-be-stored cargo box is a group of damaged control modules containing lithium batteries, and the target cargo box is a group of waste frequency converter components containing electrolyte. After step S203, the system may determine that there is a “heat-induced gas volatilization explosion” risk path between the to-be-stored cargo box and the target cargo box, and identify that the middle location between the A and B layers of the shelf is a high conflict point. Through adjacency influence matrix and conflict index analysis, the system gives a higher conflict influence value to the location, but because the location has an independent metal shielding cabin and good ventilation, the server evaluates a strong environment relief factor, and finally the storage adaptation score of the location is still higher than the threshold, and is included in the selectable to-be-stored location set.

[0062] S205, determining a storage transportation path set of the to-be-stored cargo box in each to-be-stored location in the to-be-stored location set; After determining the to-be-stored positions of the to-be-stored goods frame in the target goods shelf with a plurality of storage adaptation scores higher than a preset score threshold, a storage path planning process is further performed to realize the collaborative optimization of storage position screening and transportation path safety control. Specifically, the server constructs a corresponding storage transportation path set for each of the to-be-stored positions in the to-be-stored position set, which is used to represent all feasible transportation paths from the initial position of the current to-be-stored goods frame to the to-be-stored position, for subsequent interference strength analysis and path optimization. The purpose of step S205 is to solve the potential safety risk problems of product function failure, material denaturation or information leakage caused by static induction interference or magnetic field interference that may exist in the transportation path when the waste electronic products enter the target goods shelf system. Since the waste electronic products often have conductive elements, magnetic materials or charge accumulation structures, and the transportation process may pass through a plurality of electrical equipment, metal channels or other charged areas, when performing storage recommendation, not only the safety of the final storage position should be considered, but also the influence of each segment of space in the transportation path on the electromagnetic properties of the product should be comprehensively evaluated to realize whole-process risk control.

[0063] In a specific implementation, the server first calls a path search module to perform a multi-path planning algorithm based on the structure topology data of the target goods shelf, the spatial connection relationship between the current position of the goods frame and each to-be-stored position, the transportation channel feasibility constraint and the path length limit, to generate all feasible paths from the starting point to each to-be-stored position. The path search module can use an improved A* algorithm or Dijkstra algorithm, and combine the transportation equipment movement rules, the goods shelf structure obstacle distribution and the path traffic level limit to ensure that the generated paths not only meet the physical accessibility, but also meet the transportation strategy of the scheduling system.

[0064] Each path is composed of a series of consecutive three-dimensional space nodes, and each node corresponds to a space position unit in the target goods shelf structure. The server combines these path nodes in order to form a path vector set, which is the storage transportation path set corresponding to the to-be-stored position. The generation of the path set not only provides the spatial basis for the transportation trajectory, but also provides input parameters for subsequent spatial profile calculation in electromagnetic interference modeling.

[0065] For example, if a to-be-stored position is located in the upper corner of the target goods shelf, its transportation path may need to pass through a plurality of vertical lifting points and horizontal module nodes, and the number of paths is relatively large; while another position is located in the central part of the bottom of the goods shelf, its transportation path may be less but pass through more high-voltage areas. By uniformly constructing the path set corresponding to each target position, the server can comprehensively evaluate the interference load distribution in the transportation process, and provide spatial trajectory reference for subsequent interference strength modeling based on the magnetic material parameters and charge distribution characteristics of the product.

[0066] S206, determine the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence of each storage and transportation path in the storage and transportation path set based on the first magnetic material parameters, the first charge distribution characteristics in the first product data, the second magnetic material parameters, the second charge distribution characteristics in the second product data, and the environment data; Step S206 models and quantitatively analyzes the intensity of the electrostatic induction interference and the magnetic interference that each space node in each storage and transportation path can generate, thereby providing accurate electromagnetic environment judgment data set for subsequent path optimization. Specifically, the server analyzes the interference intensity point by point for each path based on the magnetic material parameters and the charge distribution characteristics contained in the first product data and the second product data, and the electromagnetic environment attributes in the target shelf environment data, and finally forms the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence. It can include the following steps: determining the environment conductor characteristics and shielding structure data of each preset position point in each storage and transportation path based on the environment data to obtain a path environment interference response set; coupling analysis is performed on the first charge distribution characteristics and the second charge distribution characteristics to determine the electrostatic induction coupling intensity of each preset position point in each storage and transportation path; magnetic interference analysis is performed on the first magnetic material parameters and the second magnetic material parameters to determine the magnetic interference superposition intensity of each preset position point in each storage and transportation path; the path environment interference response set and the electrostatic induction coupling intensity are mapped for interference intensity to form an electrostatic induction interference distribution sequence; the path environment interference response set and the magnetic interference superposition intensity are mapped for interference intensity to form a magnetic interference distribution sequence.

[0067] In a specific actual implementation process, the server first extracts the environment conductor characteristics and the corresponding shielding structure data of all preset position points in each storage and transportation path based on the environment data of the target shelf, to construct a path environment interference response set. The preset position points are space sampling points determined by the server in the path planning stage according to the key node positions where the goods frame can stop, turn or pass in the transportation path, combined with the shelf structure layout and the transportation equipment running track rules.

[0068] The path environment interference response set is a basic data set describing the response capability of each space point in the transportation path to charge coupling and magnetic field change, wherein the environment conductor characteristics include parameters such as metal material, electrical conductivity, grounding state, etc., which are used to simulate the amplification or conduction effect of the position on charge induction; the shielding structure data includes whether there is an electromagnetic shielding plate, a shielding net, an electrostatic isolation film material, etc. around the node, which functions to suppress the induction capability of the space point to external electromagnetic signals. The path environment interference response set provides a response weight matrix for subsequent interference intensity mapping through the combination of space nodes and physical environment information.

[0069] After obtaining the path environmental interference response set, the server further determines the electrostatic induction coupling strength of each node in each storage transportation path according to the first charge distribution feature and the second charge distribution feature. The charge distribution feature refers to the spatial distribution mode of the residual charge inside the discarded electronic product, including charge density, polarity distribution, and electrostatic field gradient and other parameters. The charge distribution feature determines the ability of the product to form an induced electric potential between the product and an external conductor during movement. The server performs electrostatic field coupling model calculation on each path node, combines the conductivity at the node and the product potential difference, solves the coupling current and the induced charge amount, and thereby quantifies the electrostatic induction coupling strength at the node. The higher the electrostatic induction coupling strength, the more likely the product is to be excited by static electricity when passing through the node, and there is a higher risk of discharge or functional disturbance.

[0070] In order to accurately evaluate the magnetic interference risk of each preset position point in each storage transportation path, the server fully considers the magnetic field influence of two key sources in the process of determining the magnetic interference superposition strength according to the first magnetic material parameter and the second magnetic material parameter: one is the response behavior of the magnetic material carried by the to-be-stored cargo box about to pass through the preset position point on the current path, and the other is the static magnetic field environment formed by the magnetic material in the stored cargo box around the position point. The magnetic induction effects of the two in space have superposition characteristics and may cause composite interference on product function, device stability, or information security. The server calculates the magnetic interference superposition strength of each node in each path according to the first magnetic material parameter and the second magnetic material parameter. The magnetic material parameters include residual magnetic induction strength, coercive force, magnetic permeability, and other indicators, which determine the magnetic response ability of the product itself under the action of the external magnetic field.

[0071] In this embodiment, in order to quantify the magnetic interference risk that the to-be-stored cargo box may suffer at each node in the transportation path, the server constructs a magnetic field superposition model based on the vector superposition principle in electromagnetism, performs spatial vector superposition on the magnetic induction strength of multiple magnetic sources (such as motors, wires, metal structures, etc.) around the path node to obtain the external total magnetic field distribution at the node; then, the server constructs a magnetic response function combining the residual magnetic induction strength, coercive force, and magnetic permeability and other indicators included in the first magnetic material parameter and the second magnetic material parameter, and the specific function is as follows: Wherein, R(H) represents the response strength of the magnetic material under the external magnetic field strength; μ is the relative magnetic permeability of the magnetic material; represents the sensitivity of the magnetic material to the change of the external magnetic flux; H is the external magnetic field strength at the current path node; H c is the coercive force of the magnetic material, reflecting the ability of the magnetic material to resist magnetization or demagnetization; B r is the residual magnetic induction strength of the magnetic material, reflecting the residual magnetism ability of the magnetic material after demagnetization; is an exponential decay term, reflecting the hysteresis and nonlinear saturation in the magnetization process; is a hyperbolic tangent function, used to simulate the saturation response trend of the magnetic material when approaching the remanence state.

[0072] The response function represents the magnetization ability and anti-interference ability of the discarded electronic product under the influence of different magnetic field strengths. The server performs discrete convolution analysis on the magnetic response function and the total magnetic field strength at the node to calculate the magnetic interference superposition strength of the node. The magnetic interference superposition strength reflects the risk level of induced current, magnetic deformation or data degradation that the product may generate due to external magnetic field excitation during transportation.

[0073] After obtaining the electrostatic induction coupling strength and the magnetic interference superposition strength, the server respectively performs mapping operation on them and the path environment interference response set to form an interference strength distribution sequence. For the electrostatic part, the server multiplies the electrostatic coupling strength of each node by the electrostatic response coefficient of the corresponding node to obtain a complete electrostatic induction interference strength distribution sequence; for the magnetic induction part, the server multiplies the magnetic interference superposition strength by the magnetic shielding coefficient of the node to form a magnetic interference strength distribution sequence. The two groups of distribution sequences are arranged in the order of the path to form a complete transportation path interference risk map, which provides continuous spatial interference indicators for subsequent path selection.

[0074] For example, in the storage transportation path, the server detects that at two nodes close to the bottom of the shelf, due to the existence of exposed metal tracks around and the lack of shielding structure, and the high product charge density, the electrostatic induction interference strength reaches the warning value; while in the middle of the path, the magnetic interference superposition strength significantly increases due to passing through the area beside the high-voltage power supply cabinet. The server accurately identifies the high-risk paragraphs in the path by performing spatial sequencing processing on the interference strengths of these nodes, thereby providing a basis for the final path screening and avoiding selecting a transportation route with high excitation risk.

[0075] S207, determining a target storage transportation path of the to-be-stored goods frame based on the electrostatic induction interference strength distribution sequence and the magnetic interference strength distribution sequence.

[0076] In step S207, based on the calculated electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, the spatial interference risk of each path in the storage transportation path set is analyzed and evaluated, which is used as the basis for finally determining the target storage transportation path. The electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence respectively represent the spatial distribution characteristics of the charge coupling interference and the magnetic field superposition interference that the to-be-stored cargo box may encounter along different paths during transportation. The first magnetic material parameters and the first charge distribution characteristics in the first product data, and the second magnetic material parameters and the second charge distribution characteristics in the second product data are considered, and are modeled in combination with the spatial electromagnetic background field at each path node in the environment data. Therefore, in step S207, the server further carries out interference path screening and stability evaluation according to the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, which can include the following steps: based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, identifying an interference preset position point in each of the storage transportation paths, wherein the electrostatic induction coupling intensity of the interference preset position point is greater than a preset electrostatic interference intensity threshold, and the magnetic interference superposition intensity is greater than a magnetic interference intensity threshold; removing the path segments between the continuous interference preset position points in each of the storage transportation paths to obtain a candidate path segment set; analyzing the electrostatic interference fluctuation characteristics of the electrostatic induction interference intensity distribution sequence and the magnetic interference fluctuation characteristics of the magnetic interference intensity distribution sequence; determining the path segment topology relationship of all the candidate path segments in the candidate path segment set to obtain a candidate target storage transportation path, and verifying the connectivity of the candidate target storage transportation path; calculating the interference stability score of the candidate target storage transportation path that passes the connectivity verification based on the electrostatic interference fluctuation characteristics and the magnetic interference fluctuation characteristics, and taking the candidate target storage transportation path with the highest interference stability score as the target storage transportation path.

[0077] The server identifies the key nodes with potential strong interference risk in each storage transportation path based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence. Specifically, the server traverses each path and judges whether the electrostatic induction coupling intensity of the preset position point exceeds the preset electrostatic interference intensity threshold, and simultaneously judges whether the magnetic interference superposition intensity exceeds the preset magnetic interference intensity threshold. The two thresholds are set according to product safety standards, electromagnetic anti-interference design specifications and actual test data, and are used to distinguish the boundaries between acceptable interference and unacceptable interference. If a preset position point simultaneously satisfies that the electrostatic and magnetic field interference both exceed the threshold, the point is marked as an interference preset position point. Through this step, the server can quickly identify the high-risk nodes with complex electromagnetic environment that may cause functional damage or data disturbance to electronic products, and provide interference characteristic basis for subsequent path optimization.

[0078] Subsequently, the server performs position correlation analysis on the interference preset position points in each path, identifies the continuous path segments formed between high-risk nodes, and removes these path segments from the original path to form a candidate path segment set. The above processing is completed based on path topology structure and node sequence information. The server takes the interference preset position points as boundary nodes, and regards the continuous path segments on both sides of the boundary nodes as areas where electromagnetic interference is not suitable for passing through. These areas may have problems such as strong induced current triggering, magnetic material excitation, or electrostatic discharge, which cannot guarantee the safety of the transport of the cargo box. By removing the above continuous high-risk segments, the server retains a set of candidate path segments with relatively low interference and complete structure in each path, providing a basis for subsequent path reconstruction and connectivity judgment.

[0079] Next, the server further extracts the fluctuation characteristics in the electrostatic induction interference intensity distribution sequence, and identifies possible electrostatic coupling stability problems in the path. Specifically, the server extracts the fluctuation amplitude, frequency change trend, and mutation point distribution of electrostatic interference in each path segment by methods such as sliding window mean square deviation calculation and Fourier transform spectral energy calculation on the distribution sequence. These characteristics reflect whether there are potential risks such as unstable electrostatic environment, rapid accumulation and discharge in the path during operation. Similarly, for the magnetic interference intensity distribution sequence, the server extracts its fluctuation characteristics to judge the stability of the magnetic field environment in each path segment. The extraction methods of the magnetic interference fluctuation characteristics include calculating the gradient change rate and spatial mean deviation of the magnetic induction intensity, which are used to describe the variation trend and local disturbance sensitivity of the magnetic field in the path segment. Through the extraction of the two interference fluctuation characteristics, the server can further judge the interference stability level of the candidate path segments and provide a basis for constructing stable path combinations.

[0080] After obtaining the candidate path segment set and the interference fluctuation characteristics, the server reconstructs the topology of the path segments based on the spatial connection relationship between the path segments. This process is achieved by constructing a directed graph model, where the nodes represent the start and end points of the candidate path segments, and the edges represent the reachable relationship between the path segments. The server performs path merging and topology connectivity verification in the graph to determine whether there is at least one complete path that can start from the starting point, pass through multiple candidate path segments, and finally reach the target storage location without passing through the high-risk segments that have been removed. If there are multiple path combinations that meet the connectivity requirements, these combined paths are marked as candidate target storage and transport paths, ensuring that the final path is continuous and feasible in terms of physical reachability, while avoiding the possibility of passing through electromagnetic high-risk areas.

[0081] After the candidate target storage transportation path screening is completed, the server calculates the interference stability score of each candidate path based on the previously extracted electrostatic interference fluctuation characteristics and magnetic interference fluctuation characteristics. The server builds a set of interference stability evaluation models for this purpose, in which the mean value of the interference intensity in the path segment, the fluctuation amplitude, the maximum mutation rate and other indicators are used as inputs. By using weighted fusion, normalization processing and risk function mapping, the interference stability of the path segment is converted into a quantitative scoring indicator. The higher the score, the more stable the path segment is in the electromagnetic environment, and the more suitable it is for electronic product transportation. The server selects the path with the highest score as the final target storage transportation path based on the overall score of the candidate path, and instructs the automatic handling equipment to complete the pallet handling according to the path. For example, if a path has small magnetic interference changes and the electrostatic fluctuation is within the safety threshold on the premise of good overall connectivity, the path will be selected as the target path, thereby achieving the goal of minimizing interference and ensuring safe transportation.

[0082] Referring to Figure 3 A structural schematic diagram of a storage management system for pallets is provided for the embodiments of the present application, and the storage management system 300 for pallets specifically includes: The acquisition module 301 is configured to acquire first product data of first discarded electronic products in a to-be-stored pallet, second product data of second discarded electronic products in a target pallet on a target shelf, environment data of the target shelf, and to-be-stored location data of each to-be-stored location in the target shelf. The generation module 302 is configured to generate a first risk category label based on the first product data and a second risk category label based on the second product data. The first determination module 303 is configured to determine a storage conflict indicator of the to-be-stored pallet and the target shelf based on the first risk category label, the second risk category label, and the environment data. The calculation module 304 is configured to calculate a storage adaptation score of the to-be-stored pallet at each to-be-stored location based on the storage conflict indicator and the to-be-stored location data, and determine a to-be-stored location set in which the storage adaptation score is greater than a preset score threshold. The second determination module 305 is configured to determine a storage transportation path set of the to-be-stored pallet at each to-be-stored location in the to-be-stored location set. The third determination module 306 is configured to determine electrostatic induction interference intensity distribution sequences and magnetic interference intensity distribution sequences of each storage transportation path in the storage transportation path set based on first magnetic material parameters and first charge distribution characteristics in the first product data, second magnetic material parameters and second charge distribution characteristics in the second product data, and the environment data. The fourth determination module 307 is configured to determine a target storage transportation path of the to-be-stored pallet based on the electrostatic induction interference intensity distribution sequences and the magnetic interference intensity distribution sequences.

[0083] Optionally, the generating module 302 is specifically configured to: analyze a damage state parameter in the first product data, identify a structural abnormality feature of the first discarded electronic product, and construct a structural risk factor sequence; compare a chemical substance parameter in the first product data with a preset material database, identify a dangerous chemical component of the first product data, and construct a chemical risk factor sequence; perform correlation analysis on the structural risk factor sequence and the chemical risk factor sequence, and construct a joint risk behavior graph; identify a first risk linkage path in the joint risk behavior graph, and generate the first risk category label according to a severity score of the first risk linkage path, the first risk linkage path being a risk linkage path of the first discarded electronic product in the joint risk behavior graph.

[0084] Optionally, the first determining module 303 is specifically configured to: perform interaction influence analysis on the first risk linkage path in the first risk category label and a second risk linkage path in the second risk category label in the joint risk behavior graph, determine a risk path node set in which there is an interaction influence between the to-be-stored goods frame and the target goods frame, determine a propagation connection relationship between risk path nodes in the risk path node set, and calculate a conflict intensity value of each risk path node; determine a conflict source based on the conflict intensity value, and determine the storage conflict indicator in combination with the environment data.

[0085] Optionally, the first determining module 303 is further specifically configured to: determine the risk path node with the conflict intensity value greater than a preset conflict intensity threshold value as the conflict source; determine a spatial position constraint between the to-be-stored goods frame and the target goods frame based on a risk type of the conflict source; analyze a risk feature of the conflict source, determine an environmental isolation mode between the to-be-stored goods frame and the target goods frame; determine an implementability of the spatial position constraint and the environmental isolation mode in combination with the environment data, and determine the storage conflict indicator according to the implementability.

[0086] Optionally, the calculating module 304 is specifically configured to: analyze the to-be-stored location data to determine a spatial adjacency relationship between each to-be-stored location in the target goods shelf and the target goods frame, so as to construct an adjacency influence matrix; calculate a conflict influence value of each to-be-stored location according to the adjacency influence matrix and the storage conflict indicator; set a corresponding environment mitigation factor for each to-be-stored location; and perform weighted calculation on the conflict influence value and the environment mitigation factor of each to-be-stored location to obtain a storage adaptation degree score.

[0087] Optionally, the third determining module 306 is specific for: determining, based on the environment data, an environment conductor characteristic of each preset position point in each of the storage transportation paths and shielding structure data, obtaining a path environment interference response set; coupling analyzing the first charge distribution characteristic and the second charge distribution characteristic, determining electrostatic induction coupling strength of each of the preset position points in each of the storage transportation paths; magnetically interfering analyzing the first magnetic material parameter and the second magnetic material parameter, determining magnetic interference superposition strength of each of the preset position points in each of the storage transportation paths; interference strength mapping the path environment interference response set and the electrostatic induction coupling strength, forming an electrostatic induction interference distribution sequence; interference strength mapping the path environment interference response set and the magnetic interference superposition strength, forming a magnetic interference distribution sequence.

[0088] Optionally, the fourth determining module 307 is specific for: based on the electrostatic induction interference strength distribution sequence and the magnetic interference strength distribution sequence, identifying an interference preset position point in each of the storage transportation paths, where the electrostatic induction coupling strength is greater than a preset electrostatic interference strength threshold, and the magnetic interference superposition strength is greater than a magnetic interference strength threshold; eliminating a path segment between the interference preset position points in each of the storage transportation paths, obtaining a candidate path segment set; analyzing electrostatic interference fluctuation characteristics of the electrostatic induction interference strength distribution sequence, and analyzing magnetic interference fluctuation characteristics of the magnetic interference strength distribution sequence; determining path segment topological relations of all the candidate path segments in the candidate path segment set, obtaining a candidate target storage transportation path, and verifying connectivity of the candidate target storage transportation path; calculating an interference stability score of the candidate target storage transportation path of the connectivity verification based on the electrostatic interference fluctuation characteristics and the magnetic interference fluctuation characteristics, and taking the candidate target storage transportation path with the highest interference stability score as the target storage transportation path.

[0089] It should be noted that: the apparatus provided in the above embodiments is only used as an example for dividing the above functional modules to achieve its functions, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in the method embodiments, which will not be repeated here.

[0090] The embodiment also discloses an electronic device, which refers to Figure 4The electronic device may include: at least one processor 401, at least one communication bus 402, a user interface 403, a network interface 404, and at least one memory 405. The communication bus 402 is used to enable communication between these components. The user interface 403 may include a display screen and a camera; optionally, the user interface 403 may also include a standard wired interface or a wireless interface. The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 405, and by calling data stored in the memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of digital signal processing, field-programmable gate array, or programmable logic array. The processor 401 may integrate one or a combination of several of the following: a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content displayed on the screen; and the modem handles wireless communication. It is understood that the modem may not be integrated into the processor 401 and can be implemented as a separate chip.

[0091] The memory 405 may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory 405 may include a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. Figure 4 As shown, the memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a storage management method for a computer.

[0092] exist Figure 4 Figure 4In the electronic device shown, the user interface 403 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 401 can be used to call an application program stored in the memory 405 for storing a storage management method of a cargo box, which, when executed by one or more processors 401, causes the electronic device to perform the method of one or more of the above embodiments.

[0093] The above merely describes exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. Any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the disclosure herein. The present application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not described in the present disclosure. The scope and spirit of the present disclosure are defined by the claims.

Claims

1. A storage management method of a pallet, characterized by, Applied in a server, the method comprises: Obtaining first product data of first discarded electronic products in a to-be-stored goods frame, second product data of second discarded electronic products in a target goods frame on a target goods shelf, environment data of the target goods shelf, and to-be-stored location data of each to-be-stored location in the target goods shelf; Generating a first risk category label based on the first product data, and generating a second risk category label based on the second product data; Determining a storage conflict indicator of the to-be-stored goods frame and the target goods frame based on the first risk category label, the second risk category label, and the environment data; Calculating a storage adaptation score of the to-be-stored goods frame in each to-be-stored location based on the storage conflict indicator and the to-be-stored location data, and determining a to-be-stored location set in which the storage adaptation score is greater than a preset score threshold; Determining a storage transportation path set of the to-be-stored goods frame in each to-be-stored location in the to-be-stored location set; Determining electrostatic induction interference intensity distribution sequences and magnetic interference intensity distribution sequences of each storage transportation path in the storage transportation path set based on first magnetic material parameters, first charge distribution characteristics in the first product data, second magnetic material parameters, second charge distribution characteristics in the second product data, and the environment data; Determining a target storage transportation path of the to-be-stored goods frame based on the electrostatic induction interference intensity distribution sequences and the magnetic interference intensity distribution sequences.

2. The method of claim 1, wherein, The generating of the first risk category label based on the first product data specifically comprises: Analyzing damage state parameters in the first product data, identifying structural abnormality characteristics of the first discarded electronic products, and constructing a structural risk factor sequence; Comparing chemical substance parameters in the first product data with a preset material database, identifying hazardous chemical components of the first product data, and constructing a chemical risk factor sequence; Performing correlation analysis on the structural risk factor sequence and the chemical risk factor sequence, and constructing a joint risk behavior graph; Identifying a first risk linkage path in the joint risk behavior graph, and generating the first risk category label according to a severity score of the first risk linkage path, wherein the first risk linkage path is a risk linkage path of the first discarded electronic products in the joint risk behavior graph.

3. The method of claim 1, wherein, The determining of the storage conflict indicator of the to-be-stored goods frame and the target goods frame based on the first risk category label, the second risk category label, and the environment data specifically comprises: Performing interactive influence analysis on the first risk linkage path in the first risk category label and a second risk linkage path in the second risk category label in the joint risk behavior graph, to determine a risk path node set in which there is an interactive influence between the to-be-stored goods frame and the target goods frame; Determining a propagation connection relationship between risk path nodes in the risk path node set, and calculating a conflict intensity value of each risk path node; Determining a conflict source based on the conflict intensity value, and determining the storage conflict indicator in combination with the environment data.

4. The method of claim 3, wherein, The conflict source is determined based on the conflict intensity value, and the storage conflict indicator is determined in combination with the environment data, specifically comprising: The risk path node with the conflict intensity value greater than a preset conflict intensity threshold is determined as the conflict source; Based on the risk type of the conflict source, a spatial position constraint between the to-be-stored goods frame and the target goods frame is determined; Risk analysis is performed on the conflict source to obtain a risk feature of the conflict source, so as to determine an environmental isolation mode between the to-be-stored goods frame and the target goods frame; The spatial position constraint and the environmental isolation mode are analyzed for realizability in combination with the environment data, to obtain a first realizability of the spatial position constraint and a second realizability of the environmental isolation mode, and the storage conflict indicator is determined according to the realizability, wherein the realizability includes the first realizability and the second realizability.

5. The method of claim 1, wherein, The storage adaptation score of the to-be-stored goods frame at each to-be-stored position is calculated based on the storage conflict indicator and the to-be-stored position data, specifically comprising: The spatial adjacency relationship between each to-be-stored position and the target goods frame in the target goods shelf is analyzed based on the to-be-stored position data, to construct an adjacency influence matrix; A conflict influence value of each to-be-stored position is calculated based on the adjacency influence matrix and the storage conflict indicator; An environmental mitigation factor corresponding to each to-be-stored position is set; The conflict influence value and the environmental mitigation factor of each to-be-stored position are weighted and calculated to obtain a storage adaptation degree score.

6. The method of claim 1, wherein, The electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence of each storage transportation path in the storage transportation path set are determined based on the first magnetic material parameter, the first charge distribution feature in the first product data, the second magnetic material parameter, the second charge distribution feature in the second product data, and the environment data, specifically comprising: The environmental conductor characteristic and shielding structure data of each preset position point in each storage transportation path are determined based on the environment data to obtain a path environment interference response set; The first charge distribution feature and the second charge distribution feature are coupled and analyzed to determine the electrostatic induction coupling strength of each preset position point in each storage transportation path; The first magnetic material parameter and the second magnetic material parameter are magnetically interfered to determine the magnetic interference superposition strength of each preset position point in each storage transportation path; The path environment interference response set and the electrostatic induction coupling strength are mapped for interference intensity to form an electrostatic induction interference distribution sequence; The path environment interference response set and the magnetic interference superposition strength are mapped for interference intensity to form a magnetic interference distribution sequence.

7. The method of claim 6, wherein, The target storage transportation path of the to-be-stored goods frame is determined based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, specifically comprising: identify, based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence, an interference preset position point in each of the storage transportation paths, where the electrostatic induction coupling intensity is greater than a preset electrostatic interference intensity threshold value and the magnetic interference superposition intensity is greater than a magnetic interference intensity threshold value; remove path segments between the interference preset position points in each of the storage transportation paths to obtain a candidate path segment set; analyze electrostatic interference fluctuation characteristics of the electrostatic induction interference intensity distribution sequence and analyze magnetic interference fluctuation characteristics of the magnetic interference intensity distribution sequence; determine path segment topological relations of all the candidate path segments in the candidate path segment set to obtain a candidate target storage transportation path, and perform connectivity verification on the candidate target storage transportation path; calculate an interference stability score of the candidate target storage transportation path that passes the connectivity verification based on the electrostatic interference fluctuation characteristics and the magnetic interference fluctuation characteristics, and take the candidate target storage transportation path with the highest interference stability score as the target storage transportation path.

8. A storage management system for pallets, characterized in that comprise: an acquisition module, configured to acquire first product data of first discarded electronic products in a to-be-stored goods frame, second product data of second discarded electronic products in a target goods frame on a target goods shelf, environment data of the target goods shelf, and to-be-stored location data of each to-be-stored location in the target goods shelf; a generation module, configured to generate a first risk category label based on the first product data and generate a second risk category label based on the second product data; a first determination module, configured to determine a storage conflict indicator of the to-be-stored goods frame and the target goods frame based on the first risk category label, the second risk category label, and the environment data; a calculation module, configured to calculate a storage adaptation score of the to-be-stored goods frame at each to-be-stored location based on the storage conflict indicator and the to-be-stored location data, and determine a to-be-stored location set in which the storage adaptation score is greater than a preset score threshold value; a second determination module, configured to determine a storage transportation path set of the to-be-stored goods frame at each to-be-stored location in the to-be-stored location set; a third determination module, configured to determine an electrostatic induction interference intensity distribution sequence and a magnetic interference intensity distribution sequence of each of the storage transportation paths in the storage transportation path set based on first magnetic material parameters and first charge distribution characteristics in the first product data, second magnetic material parameters and second charge distribution characteristics in the second product data, and the environment data; a fourth determination module, configured to determine a target storage transportation path of the to-be-stored goods frame based on the electrostatic induction interference intensity distribution sequence and the magnetic interference intensity distribution sequence.

9. An electronic device, comprising: comprise: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is configured to store computer program codes, the computer program codes comprise computer instructions, and the one or more processors invoke the computer instructions to enable the electronic device to perform the method in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the electronic device, cause the electronic device to perform the method of any of claims 1-7.

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