Cross-warehouse equipment allocation method, equipment, medium and program product

Abstract

The invention discloses a cross-warehouse equipment allocation method, equipment, a medium and a program product, and relates to the technical field of data center operation and maintenance, and the method comprises the steps: constructing an equipment compatibility quantification model according to the multi-dimensional features and corresponding dimension weights of the equipment; defining a transportation cost function by using a transportation decision influence factor containing a task emergency degree; receiving and responding to an allocation task, and performing multi-target hierarchical solution on the equipment compatibility, the transportation economy and the task timeliness according to the equipment compatibility quantitative model in combination with the transportation cost function; and adjusting the dimension weight and / or the transportation cost function based on the related data of the transportation process, and generating an optimal allocation scheme conforming to a preset constraint. In this way, a multi-target hierarchical solution strategy is applied, the generation time of an allocation scheme is greatly shortened, meanwhile, the defects of compatibility misjudgment, transportation cost fluctuation, emergency response delay and the like in a traditional allocation decision are effectively overcome, and precision, intelligence and self-adaptive optimization of the whole process of equipment allocation are achieved.

Description

Technical Field

[0001] The present invention relates to the field of data center operation and maintenance technology, and in particular to a method, equipment, medium and program product for cross-warehouse equipment allocation. Background Art

[0002] With the rapid development of information technology, equipment allocation faces complex challenges in the multi-warehouse management of large data centers. Traditional solutions rely on rule-based allocation decision-making systems that rely on static data and manual experience, making it difficult to dynamically respond to changing environments and needs, and resulting in low decision-making accuracy. For example, the system typically evaluates device compatibility through database queries, considering only hardware models and firmware versions, while ignoring more complex compatibility factors. Transportation cost calculations often use fixed models that fail to account for environmental changes and the status of transportation tools, resulting in inaccurate cost assessments. Equipment allocation also responds slowly to urgent tasks. Overall, relevant technical solutions still have limitations when addressing complex and changing equipment allocation needs. Summary of the Invention

[0003] The present invention provides a method, device, medium and program product for cross-warehouse equipment allocation, which at least solves the problem in related technologies of relying on static data and manual experience, being difficult to dynamically respond to changing environments and needs, and having low decision-making accuracy.

[0004] The present invention provides a method for allocating equipment across warehouses, comprising:

[0005] Build a device compatibility quantification model based on the multi-dimensional characteristics of the device and the corresponding dimension weights;

[0006] Using factors influencing transportation decision including mission urgency, the transportation cost function is defined;

[0007] Receiving and responding to the allocation task, performing a multi-objective hierarchical solution for equipment compatibility, transportation economy, and task timeliness based on the equipment compatibility quantification model and the transportation cost function;

[0008] While solving the problem, the dimension weights of the equipment compatibility quantification model and / or the transportation cost function are adjusted based on relevant data of the transportation process to generate an optimal allocation plan that meets the preset constraints.

[0009] The present invention also provides an electronic device, comprising: a memory for storing a computer program; and a processor for implementing the steps of any of the above-mentioned methods for allocating equipment across warehouses when executing the computer program.

[0010] The present invention also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for allocating equipment across warehouses are implemented.

[0011] The present invention also provides a computer program product, comprising a computer program, which implements the steps of any of the above-mentioned methods for allocating equipment across warehouses when executed by a processor.

[0012] The present invention first constructs a device compatibility quantification model based on the multi-dimensional characteristics of the equipment and the corresponding dimensional weights; defines a transportation cost function using factors influencing transportation decisions, including the urgency of the task; then, upon receiving and responding to an allocation task, performs a multi-objective hierarchical solution for equipment compatibility, transportation economy, and task timeliness based on the device compatibility quantification model combined with the transportation cost function; while solving the problem, the dimensional weights of the device compatibility quantification model and / or the transportation cost function are adjusted based on relevant data from the transportation process to generate an optimal allocation plan that meets the preset constraints. By constructing a device compatibility quantification model and a transportation cost function and applying a multi-objective hierarchical solution strategy, the allocation plan generation time is significantly shortened while significantly improving the device compatibility compliance rate and the emergency task response timeliness achievement rate, effectively avoiding the drawbacks of compatibility misjudgment, transportation cost fluctuations, and emergency response delays in traditional allocation decisions; and by dynamically optimizing the device compatibility quantification model and the transportation cost function based on transportation process data, the entire equipment allocation process can be accurately, intelligently, and adaptively optimized, highly adaptable to large-scale data center physical equipment allocation scenarios, and effectively ensuring the efficiency and accuracy of equipment allocation.

[0013] In addition, the present invention also provides corresponding electronic equipment, computer-readable storage media and program products for the cross-warehouse equipment allocation method, which have the same or corresponding technical features as the above-mentioned cross-warehouse equipment allocation method and have the same effects as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0015] Figure 1 A flow chart of a method for allocating equipment across warehouses provided in an embodiment of the present invention;

[0016] Figure 2 A schematic diagram of the interaction between the screening layer and the solution layer provided in an embodiment of the present invention;

[0017] Figure 3A schematic diagram illustrating the relationship between allocation tasks, merging optimization, and exception response provided in an embodiment of the present invention;

[0018] Figure 4 This is a structural diagram of the device for allocating equipment across warehouses provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Traditional allocation solutions often rely on static data and manual experience-based decision-making, making it difficult to quantitatively assess equipment compatibility, unable to dynamically respond to sudden demands, and ignoring the coupling between transportation routes and equipment status. Therefore, traditional methods have certain limitations and, in large-scale data center equipment allocation, suffer from the following major drawbacks: Traditional solutions often rely on manual experience for decision-making, lacking systematic and scientific approaches and susceptible to subjective factors, leading to inconsistent and inaccurate decisions; They often only consider hardware models and firmware versions, while ignoring other key factors such as power interfaces, network compatibility, cooling requirements, and historical failure rates, increasing compatibility risks after equipment allocation; Traditional methods struggle to achieve rapid response in the event of emergencies, resulting in insufficient timeliness in allocation decisions, which can lead to critical equipment being delayed in time; Transportation cost calculations often rely on fixed models, failing to factor in real-time environmental changes (such as temperature, humidity, and traffic conditions) and the status of transportation vehicles, resulting in inaccurate cost estimates; Traditional systems often have information silos and lack effective data exchange between work order systems, warehouse management systems, and logistics monitoring platforms, preventing real-time updates of allocation information, thus compromising the comprehensiveness of decision-making. In order to overcome the above shortcomings, the present invention provides a method for cross-warehouse equipment allocation, which effectively solves problems such as compatibility, cost fluctuations and emergency response in the equipment allocation process, and realizes precise, intelligent and adaptive optimization of equipment allocation.

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] It should be noted that, in the description of the present invention, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. The terms "first," "second," etc., in the present invention are used to distinguish similar objects, and are not used to describe a particular order or precedence.

[0022] In order to enable those skilled in the art to better understand the solutions of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0023] In conjunction with the specific application environment architecture or specific hardware architecture on which the execution of the cross-warehouse equipment allocation method depends, the specific application environment architecture or specific hardware architecture is described herein.

[0024] An embodiment of the present invention provides a method for allocating equipment across warehouses. The method is described in detail in conjunction with the execution flow of the method for allocating equipment across warehouses. Figure 1 The flowchart of the cross-warehouse equipment allocation method provided by the embodiment of the present invention is as follows: Figure 1 As shown, the method includes:

[0025] S101. Construct a device compatibility quantification model based on the multi-dimensional characteristics of the device and the corresponding dimensional weights.

[0026] It should be noted that multi-dimensionality refers to multiple attributes of a device and is the basis for analyzing device compatibility. The device compatibility quantification model is an evaluation model that converts device compatibility into quantitative values. It can be understood as a tool for transforming compatibility assessment during the device allocation process from empirical judgment to a data-based, standardized, and scientific decision-making tool. This invention comprehensively considers multiple dimensions of a device and constructs a device compatibility quantification model based on the multi-dimensional characteristics of the device and their corresponding weights, providing data support for subsequent allocation decisions.

[0027] S102. Define a transportation cost function using transportation decision-making influencing factors including task urgency.

[0028] In practice, the present invention is based on the influencing factors such as the urgency of the task in the transportation decision-making, and constructs a function model that can quantify the transportation cost, so that the transportation cost calculation can be dynamically evaluated in combination with multi-dimensional conditions such as the urgency of the task.

[0029] S103: Receive and respond to the allocation task, and perform multi-objective hierarchical solution for equipment compatibility, transportation economy, and task timeliness based on the equipment compatibility quantification model combined with the transportation cost function.

[0030] In implementation, after receiving the allocation task, the present invention can use a multi-objective hierarchical optimization strategy based on the equipment compatibility quantification model and the transportation cost function to carry out multi-objective hierarchical solution for equipment compatibility, transportation economy and task timeliness to generate a comprehensive optimized allocation plan.

[0031] S104. While solving the problem, the dimension weights and / or transportation cost function of the equipment compatibility quantification model are adjusted based on relevant data of the transportation process to generate an optimal allocation plan that meets the preset constraints.

[0032] In implementation, the present invention can dynamically adjust the equipment compatibility model weights and transportation cost function according to real-time data during the transportation process when solving the allocation plan, and then generate the optimal allocation plan that meets the preset constraints, realizing dual adaptive optimization of the model and the plan.

[0033] In the above-mentioned cross-warehouse equipment allocation method provided by the embodiment of the present invention, first, a device compatibility quantitative model is constructed based on the multi-dimensional characteristics of the equipment and the corresponding dimensional weights; a transportation cost function is defined using the transportation decision-making influencing factors including the task urgency; then, upon receiving and responding to the allocation task, a multi-objective hierarchical solution is performed on the equipment compatibility, transportation economy, and task timeliness based on the device compatibility quantitative model combined with the transportation cost function; while solving the problem, the dimensional weights of the device compatibility quantitative model and / or the transportation cost function are adjusted based on relevant data of the transportation process to generate an optimal allocation plan that meets the preset constraints. In this way, by constructing the device compatibility quantitative model and the transportation cost function and applying the multi-objective hierarchical solution strategy, the allocation plan generation time is greatly shortened, while the equipment compatibility compliance rate and the emergency task response timeliness achievement rate are significantly improved, effectively avoiding the drawbacks of compatibility misjudgment, transportation cost fluctuation, and emergency response delay in traditional allocation decision-making; and, by dynamically optimizing the device compatibility quantitative model and the transportation cost function based on transportation process data, the entire equipment allocation process can be accurately, intelligently, and adaptively optimized, highly adapted to large-scale data center physical equipment allocation scenarios, and effectively ensuring the efficiency and accuracy of equipment allocation.

[0034] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention, step S101 constructs a device compatibility quantitative model based on the multi-dimensional characteristics of the equipment and the corresponding dimensional weights, which may specifically include: obtaining the multi-dimensional characteristics of the equipment; the multi-dimensional characteristics include model matching, firmware version, rack margin, power interface, network compatibility, heat dissipation requirements and historical failure rate; using the obtained multi-dimensional characteristics and corresponding dimensional weights to model the equipment compatibility and construct a device compatibility quantitative model.

[0035] In practice, this invention models device compatibility using seven dimensions: model match, firmware version, rack headroom, power interface, network compatibility, heat dissipation requirements, and historical failure rate. Model match refers to the degree of match between the device model and the target warehouse's standard model: perfect match = 1.0, same-series compatibility = 0.8, form factor compatibility = 0.5, and incompatibility = 0, based on the compatibility matrix published by the device manufacturer. Firmware version difference refers to the difference in the major firmware version number: same version = 1.0, 1 version difference = 0.7, 2 versions difference = 0.3, and ≥3 versions difference = 0. Rack U space headroom = the target cabinet's remaining U space divided by the device's required U space. A headroom of ≥2 U = 1.0, a headroom of 1 U = 0.6, and a headroom of 0 U = 0 (capacity expansion required). Power interface type refers to the power plug type (e.g., C13 / C19 / PDU direct connection). Perfect match = 1.0, transfer-compatible = 0.6, and incompatibility = 0. Network interface matching refers to the matching speed between the network port type (e.g., RJ45 / SFP+ / QSFP28) and the target switch port. Compatible = 1.0, requires conversion = 0.5, and incompatible = 0. Cooling requirement refers to the ratio of the device's cooling requirement (kW / U) to the target cabinet's cooling capacity. Required cooling capacity ≤ 80% = 1.0, 80%-100% = 0.5, and exceeded = 0. Historical failure rate compares the annualized average failure rate (AFR) of similar devices in the target warehouse environment to the target warehouse. AFR ≤ source warehouse = 1.0, with a 0.1 deduction for every 1% increase.

[0036] The expression of the device compatibility quantitative model can be:

[0037] ;

[0038] in, is the dimension weight, Score the dimension, m is the total number of dimensions, when m=7, the default weight =0.2, =0.15, =0.15, =0.1, =0.1, =0.2, =0.1. Dimension weights can be dynamically adjusted in specific scenarios, for example: Emergency mission scenario: Improve model matching ( +0.2), firmware version ( +0.1) weight. Energy saving priority scenario: Improve heat dissipation requirements ( +0.3), power interface ( +0.2) weight.

[0039] The aforementioned device compatibility quantification model integrates multiple core dimensions, such as model matching and firmware version, through a weighted summation expression. This model not only provides a quantitative compatibility assessment in general scenarios based on default weights, ensuring the comprehensiveness and scientific nature of device compatibility analysis, but also dynamically adjusts dimension weights based on different business scenarios. For example, in emergency mission scenarios, it prioritizes rapid device adaptation, while in energy-saving scenarios, it prioritizes energy and heat dissipation performance, significantly enhancing the model's flexibility and business adaptability. This design not only effectively improves the accuracy of device compatibility assessments, but also provides targeted decision support for diverse equipment allocation needs, reducing the risk of misjudgment and improving resource scheduling efficiency and utilization benefits.

[0040] Furthermore, in the specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in the embodiment of the present invention, step S102 uses the transportation decision influencing factors including the urgency of the task to define the transportation cost function, which can specifically include: using the urgency of the task, transportation distance, equipment volume and weight, type of transportation tool, ambient temperature and humidity, and path complexity to define the transportation cost function.

[0041] In implementation, the present invention defines a transportation cost function based on multiple factors: transportation distance, task urgency, transportation vehicle type, ambient temperature and humidity, and path complexity. Transportation distance includes warehouse GIS coordinates, AutoNavi Map API, and actual path distance. Task urgency includes the work order system priority tag: Urgency Factor = 1 + 0.3 × (UrgencyLevel - 1). UrgencyLevel represents the grading of task urgency and the event management priority grading standard (P1-P4). Equipment volume and weight represent equipment specifications in the asset database. Transportation vehicle type includes transportation vehicle price estimates, including automated guided vehicles (AGVs), forklifts, manual labor, regular trucks, cold chain trucks, and aviation. Ambient temperature and humidity are collected by warehouse IoT sensors. For every 10% deviation from the standard value (22°C / 50% RH), a 5% cost increase is added. Path complexity can include penalty factors for mountainous sections, bridge sections, and congested urban sections.

[0042] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention, after executing step S102 to define the transportation cost function, it can also include: calculating the transportation cost using the transportation cost function based on the task urgency coefficient, the allocation transportation distance value, the price valuation matrix associated with the transportation tool type, the path complexity penalty coefficient and the environmental penalty factor.

[0043] In implementation, the dynamic transportation cost function of the present invention combines the allocation transportation path distance, task urgency, the transportation type associated price valuation matrix, the penalty coefficient for the deviation of ambient temperature and humidity from the standard value, and the path complexity based on the penalty coefficient superimposed on mountainous areas / bridges / congested sections to dynamically evaluate the allocation transportation cost.

[0044] In specific implementation, the following formula can be used to calculate the transportation cost:

[0045] ;

[0046] in, To allocate the transport distance value, is the price corresponding to the type of transportation, is the path complexity penalty coefficient, is the task urgency coefficient, For emergency surcharge, is the time sensitivity coefficient, is the environmental penalty factor, For transportation costs, Indicates the total number of devices, .

[0047] Time sensitivity coefficient :When the remaining time of the work order is less than the threshold, Grow exponentially:

[0048] ;

[0049] in, Is the remaining time of the work order, is the threshold;

[0050] Environmental penalty factor , when the temperature and humidity exceed the standard:

[0051] ;

[0052] in, It's the temperature, It's humidity.

[0053] The aforementioned transportation cost function comprehensively considers task urgency, transportation distance, tool price, path complexity, time sensitivity, and environmental factors, achieving precise calculations through dynamic coefficients and penalty mechanisms. The task urgency coefficient and time sensitivity coefficient are linked to ensure priority cost accounting for urgent tasks; the path complexity penalty coefficient avoids the selection of high-risk paths, and the environmental penalty factor quantifies the cost impact of excessive temperature and humidity. This multi-dimensional, adaptive calculation method not only ensures a refined assessment of transportation costs, but also flexibly adapts to the needs of different scenarios, providing a scientific basis for equipment allocation decisions, effectively balancing timeliness, economy, and environmental adaptability, and improving the overall efficiency of resource scheduling.

[0054] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention, step S103 performs a multi-objective hierarchical solution to equipment compatibility, transportation economy and task timeliness based on the equipment compatibility quantification model combined with the transportation cost function, which may specifically include: establishing a multi-objective optimization model based on the equipment compatibility quantification model combined with the transportation cost function; the multi-objective optimization model includes a screening layer and a solution layer; using the screening layer to obtain hard constraints corresponding to the allocation task, filtering the hard constraints of the equipment compatibility quantification model to obtain a feasible solution set; combining the calculation results of the transportation cost function, using the solution layer to optimize the equipment compatibility, transportation economy and task timeliness of the feasible solution set output by the screening layer.

[0055] In implementation, the present invention can establish a branch solution framework (i.e., a multi-objective optimization model) based on the equipment compatibility quantification model combined with the transportation cost function. The framework can include a screening layer (also called a fast screening layer) and a solution layer (also called a precise solution layer).

[0056] Figure 2 Schematic diagram of the interaction between the screening layer and the solution layer provided in the embodiment of the present invention. Figure 2 As shown in the figure, the quick screening layer filters the allocation demand equipment list, target warehouse information, and other information based on the 7-dimensional compatibility model according to hard constraints. For example, if the equipment size is greater than the remaining space on the target shelf, the firmware version difference is ≥ 3 generations, and the transport vehicle load exceeds the standard, absolute filtering will be performed. If any condition constraint is violated, the equipment will be immediately eliminated. Based on the allocation demand, suitable equipment will be quickly screened from the 7-dimensional equipment evaluation model.

[0057] The solution layer performs multi-objective optimization on the feasible solution set output by the screening layer. Combined with dynamic parameters such as traffic status, warehouse load, equipment failure information and other data, the optimization weight is dynamically adjusted (the transportation cost weight is automatically reduced for urgent tasks) to calculate the multi-objective solution.

[0058] The multi-objective optimization model of the present invention deeply integrates the equipment compatibility quantification model and the transportation cost function through a layered architecture, thereby achieving efficient and accurate optimization of allocation decisions. The screening layer quickly filters the results of the equipment compatibility quantification model based on hard constraints, eliminates solutions that do not meet the basic conditions, greatly reduces the search space, and improves computational efficiency; the solution layer combines the transportation cost function to comprehensively optimize the feasible solution set in terms of equipment compatibility, transportation economy, and task timeliness, while ensuring equipment adaptability, taking into account cost control and emergency task response. This hierarchical and collaborative optimization mechanism not only avoids the inefficiency problem caused by mixed conditions in traditional decision-making, but also effectively solves problems such as compatibility misjudgment, cost fluctuations, and response delays through multi-dimensional trade-offs, significantly improving the scientificity, timeliness, and economy of equipment allocation solutions, and providing reliable intelligent decision-making support for large-scale equipment scheduling.

[0059] Furthermore, in the specific implementation, the above steps filter the device compatibility quantification model by hard constraints to obtain a feasible solution set, which may specifically include: using the device compatibility quantification model to calculate the compatibility score between devices through device parameters with multi-dimensional features; the device parameters include the matching degree between the device model and the target warehouse model, the difference in firmware version numbers, the proportion of remaining space on the target shelf, the power plug type, the matching degree between the network port type and the target switch port, the ratio of the device heat dissipation requirement to the cooling capacity of the target cabinet, and the average annual failure rate of similar devices in the target warehouse environment; based on the calculated device compatibility score and the obtained hard constraints, the equipment in each warehouse is filtered to screen out the equipment that meets the hardware constraints to obtain a feasible solution set.

[0060] During implementation, based on a quantitative model for device compatibility, we accurately calculated inter-device compatibility scores based on multiple dimensions, including device model matching, firmware version, and spatial adaptation. This comprehensively considered the compatibility of devices in terms of hardware, software, and environment, effectively avoiding compatibility misjudgments. On this basis, we screened and filtered each warehouse's equipment in accordance with hard constraints, quickly eliminating devices that didn't meet basic requirements and efficiently generating a set of feasible solutions. This significantly narrowed the range of options for equipment allocation and improved decision-making efficiency, while also providing scientific and reliable candidate solutions for subsequent optimization, ensuring that equipment scheduling precisely aligned with business needs and reducing deployment risks and costs.

[0061] Furthermore, in a specific implementation, the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention may also include: using the solution layer to feed back the multi-objective optimization solution results to the screening layer, so that the screening layer adjusts the hard constraints until the optimal allocation plan is generated.

[0062] In implementation, Figure 2As shown, the screening layer and the solving layer collaborate to process the problem. The screening layer transmits the hard-constrained feasible solutions that have been screened to the solving layer. Meanwhile, when no feasible solutions are found, the solving layer feeds back the optimization results to adjust the screening constraints. For example, when the solving layer cannot find a feasible solution, it notifies the screening layer to adjust the "firmware version difference < 2" constraint to "firmware version difference < 3," triggering the screening layer to re-screen. This collaborative process quickly produces the optimal solution. This feedback of the multi-objective optimization results from the solving layer to the screening layer establishes a dynamic, cyclical optimization mechanism. The screening layer can flexibly adjust the hard constraints based on optimization results from multiple dimensions, such as compatibility, economy, and timeliness, achieving iterative upgrades from initial constraints to precise adaptation. This process effectively avoids the solution limitations caused by static constraints, enabling the model to continuously converge to the optimal solution through continuous feedback and adjustment. Ultimately, it generates an optimal allocation plan that balances multiple objectives, significantly improving the scientific nature, flexibility, and global optimization capabilities of equipment allocation decisions and ensuring maximum resource allocation efficiency.

[0063] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention, step S104 adjusts the dimensional weights and / or transportation cost function of the equipment compatibility quantification model to generate an optimal allocation plan that meets the preset constraints. Specifically, it may include: adjusting the dimensional weights and / or transportation cost function of the equipment compatibility quantification model to minimize transportation costs, maximize equipment compatibility, and maximize timeliness as the goals, and generating an optimal allocation plan for a set number that meets the preset constraints.

[0064] In implementation, the present invention can, when calculating multi-objective solutions, take minimizing transportation costs (vehicles, manpower, energy consumption), maximizing equipment compatibility (hardware model, firmware version, interface matching, heat dissipation requirements), and maximizing timeliness (priority scheduling of key equipment) as the goals, and ultimately output a set number (e.g., 3 to 5) of optimal candidate allocation solutions that meet preset constraints.

[0065] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation method provided in an embodiment of the present invention, step S104 adjusts the dimensional weights and / or transportation cost function of the equipment compatibility quantification model based on relevant data of the transportation process, which may specifically include: obtaining multi-source data from the work order system, warehouse management system and monitoring platform; using multi-source data to obtain equipment status, road condition data and transportation tool status, and adjusting the dimensional weights and / or transportation cost function of the equipment compatibility quantification model.

[0066] In practice, the present invention can achieve multi-source data interaction based on real-time data collaborative processing, and output an optimal allocation plan that meets preset constraints through a dynamic weight adjustment mechanism. Specifically, when the solution layer performs multi-objective optimization based on the feasible solution set output by the screening layer, it can dynamically obtain real-time equipment status, current location and power level of transportation vehicles, route planning, and road congestion data based on multi-source data interaction between the work order system, warehouse management system, and logistics monitoring platform, in order to adjust the dimensional weights and / or transportation cost function of the equipment compatibility quantification model and thus obtain the optimal solution.

[0067] Furthermore, in specific implementation, the above-mentioned cross-warehouse equipment allocation method provided in the embodiment of the present invention may also include: tracking the allocation process by collaborative processing of multi-source data, so as to adjust the allocation plan when an event occurs that exceeds the set range.

[0068] During implementation, multi-source data from the work order system, warehouse management system, and monitoring platform can be collaboratively processed in real time, and the allocation process can be tracked in real time. Abnormal events that exceed the set range can be captured in a timely manner. If an emergency occurs, the allocation plan can be dynamically adjusted to meet real-time needs, effectively improving the dynamic adaptability and risk response capabilities of the allocation process, and ensuring efficient and stable equipment allocation.

[0069] Furthermore, in specific implementation, the above-mentioned cross-warehouse equipment allocation method provided in the embodiment of the present invention may also include: when there are multiple allocation tasks received, merging the multiple allocation tasks, integrating and analyzing the merged allocation tasks to track the allocation progress, combining and optimizing multiple allocation plans, and generating an integrated plan for shared transportation resources.

[0070] During implementation, when faced with multiple transfer tasks, task consolidation and integrated analysis can coordinate and plan dispersed transfer requirements, breaking down information barriers between tasks and achieving a holistic perspective on resource utilization. On this basis, centralized tracking of transfer progress and combined optimization of multiple transfer plans can fully tap the shared potential of transportation resources, reduce inefficiencies such as duplicate transportation and empty runs, and significantly reduce transportation costs. At the same time, integrated solutions enable intensive scheduling of resources such as vehicles and routes, improving logistics efficiency, shortening the overall transfer cycle, and enhancing flexibility and collaboration in responding to complex task scenarios, providing strong support for enterprises to reduce costs and increase efficiency.

[0071] It should be noted that the present invention can track generated allocation plans in real time and is divided into a plan merging and optimization unit and an exception response unit. The merging and optimization unit can integrate and analyze concurrent allocation demands to generate an integrated plan for shared transportation resources. The exception response unit can be connected to the allocation monitoring platform. When extreme weather conditions on the transportation route or sudden fullness of the target warehouse are detected, it can promptly indicate the plan risks and adjust the allocation plan.

[0072] Figure 3 Schematic diagram of the relationship between the allocation tasks, merging optimization and abnormal response provided by the embodiment of the present invention. Figure 3 As shown, first, the plan merging and optimization unit combines and optimizes multiple allocation plans and merges scattered allocation demands to save costs; secondly, the abnormal response unit coordinates processing based on real-time data to adjust the allocation plan in time to respond to emergencies, such as sudden equipment failures, traffic conditions in extreme weather, and emergency fullness of warehouses, and quickly responds and gives prompts to adjust the current allocation plan.

[0073] The following example illustrates the cross-warehouse equipment transfer method provided by the present invention. Assume the transfer requirement is: an internet company is building a new data center in City A and needs to transfer 150 servers from warehouses in City B, City C, and City D to City A within 48 hours. Requirements include: device compatibility ≥ 95%, total transportation cost ≤ 600,000 RMB, and target cabinet temperature ≤ 25°C (the warehouse in City A currently has a 20kW cooling capacity reserve).

[0074] First, device compatibility modeling can be performed using a seven-dimensional rule engine to model device compatibility and obtain a corresponding device compatibility quantitative model.

[0075] After scoring by the device compatibility quantitative model, the output is: 80 qualified devices in City B (100% compatibility, some require firmware upgrades); 48 qualified devices in City C (compatibility ≥ 5%); 0 qualified devices in City D (version difference exceeds the limit).

[0076] Then, the dynamic transportation cost is calculated. Table 1 shows the real-time parameter input.

[0077] Table 1 Real-time parameter input

[0078]

[0079] Taking City B → City A as an example, assuming the time sensitivity coefficient =1, emergency surcharge =1000, from Table 1 we can see that the transport distance =1850, the price corresponding to the type of transport =3.2, path complexity penalty coefficient =0.18×0.5=0.09, environmental penalty factor =max((23-22) / 5, (65-55) / 10)×0.05=0.05, task urgency coefficient (i.e. work order priority) =1+0.3×(2-1)=1.3, now calculate the transportation cost of a single device: =(1850×3.2×1.09) / (1-0.05)+1000×1.3=8092.4.

[0080] Next, a layered solution is performed. Using the filtering layer, all devices in the warehouse in City D are excluded. 20 devices in the warehouse in City B are also excluded (due to firmware version differences and an upgrade time exceeding 24 hours). Air transport to City C was canceled due to the typhoon and replaced with high-speed rail (increasing the cost to 8 / km). The feasible solution is then output: City B: 60 devices (immediately deployable), City C: 48 devices (high-speed rail transport), and City D: 0 devices. Next, a refined solution is performed: through dynamic weighting, the urgency coefficient is increased to 0.6 due to time constraints.

[0081] Table 2 Allocation Plan

[0082]

[0083] As can be seen from Table 2, Option 2 was excluded due to the cooling limit exceeding, and Option 3 required manual confirmation due to the timeout risk (40 hours < 48 hours but close to the upper limit).

[0084] Finally, based on multi-source data, real-time collaboration and execution enable dynamic adjustments to the allocation plan. For example, if three server transport vehicles in City B malfunction, the system immediately activates spare vehicles, increasing the cost by 5,000 yuan, and allocates three additional servers from the warehouse in City C. The original plan 1 now changes to 57 servers in City B and 33 in City C.

[0085] It should be pointed out that the present invention can automatically avoid hidden dangers such as firmware version conflicts and physical damage to interfaces through mandatory verification of equipment compatibility and transportation risk assessment models, reduce equipment failure rates during transportation, ensure plug-and-play after the equipment is put on the shelf, reduce the risk of business interruption in the data center, and thus improve allocation reliability. Based on a multi-objective optimal solution set, the cost-compatibility-timeliness of different allocation plans is demonstrated, reducing blind spots in management personnel's decision-making and reducing resource mismatch events. The cross-warehouse equipment allocation method provided by the present invention has a complete technical chain from data modeling, dynamic cost calculation to hierarchical optimization. It can generate cost-effective solutions under complex constraints through the cooperation of real-time data collaboration and rule engines.

[0086] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method.

[0087] The embodiments of the present application also provide a device for allocating equipment across warehouses. Figure 4 This is a schematic diagram of the structure of the cross-warehouse equipment allocation device provided by the embodiment of the present invention. This embodiment is based on the perspective of functional modules, such as Figure 4 As shown, the device includes:

[0088] A model building module 10 is used to build a device compatibility quantification model based on the multi-dimensional features of the device and the corresponding dimensional weights;

[0089] A function definition module 11 is used to define a transportation cost function using factors affecting transportation decision-making including task urgency;

[0090] A hierarchical solution module 12 is configured to receive and respond to the allocation task, and perform a multi-objective hierarchical solution on equipment compatibility, transportation economy, and task timeliness based on the equipment compatibility quantitative model combined with the transportation cost function;

[0091] The parameter adjustment module 13 is used to adjust the dimension weights and / or transportation cost function of the equipment compatibility quantification model based on relevant data of the transportation process while solving the problem, so as to generate an optimal allocation plan that meets the preset constraints.

[0092] In the above-mentioned cross-warehouse equipment allocation device provided in the embodiment of the present invention, the interaction of the above-mentioned four modules can be used to apply a multi-objective hierarchical solution strategy, which can significantly shorten the allocation plan generation time while significantly improving the equipment compatibility compliance rate and the emergency task response timeliness achievement rate, effectively avoiding the drawbacks of compatibility misjudgment, transportation cost fluctuations and emergency response delays in traditional allocation decisions; and, based on the dynamic optimization of the equipment compatibility quantitative model and the transportation cost function based on the transportation process data, it can realize the precision, intelligence and adaptive optimization of the entire equipment allocation process, which is highly adaptable to the physical equipment allocation scenario of large-scale data centers, and effectively guarantees the efficiency and accuracy of equipment allocation.

[0093] Since the embodiments of the cross-warehouse equipment transfer apparatus correspond to the embodiments of the cross-warehouse equipment transfer method, the description of the features of the corresponding embodiments of the cross-warehouse equipment transfer apparatus can be found in the description of the corresponding embodiments of the cross-warehouse equipment transfer method, and will not be repeated here. The embodiments of the cross-warehouse equipment transfer apparatus and the cross-warehouse equipment transfer method have the same beneficial effects as the above-mentioned cross-warehouse equipment transfer method.

[0094] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation device provided in the embodiment of the present invention, the model construction module 10 can be specifically used to obtain multi-dimensional characteristics of the equipment; the multi-dimensional characteristics include model matching, firmware version, rack margin, power interface, network compatibility, heat dissipation requirements and historical failure rate; the equipment compatibility is modeled using the obtained multi-dimensional characteristics and the corresponding dimensional weights to construct a quantitative model of equipment compatibility.

[0095] Furthermore, in a specific implementation, in the above-mentioned inter-warehouse equipment allocation device provided in an embodiment of the present invention, the function definition module 11 can be specifically configured to define a transportation cost function using task urgency, transportation distance, equipment volume and weight, transportation vehicle type, ambient temperature and humidity, and path complexity. The transportation cost function is used to calculate the transportation cost based on the task urgency coefficient, the allocation transportation distance value, the price valuation matrix associated with the transportation vehicle type, the path complexity penalty coefficient, and the environmental penalty factor.

[0096] Furthermore, in specific implementation, in the above-mentioned cross-warehouse equipment allocation device provided in the embodiment of the present invention, the hierarchical solution module 12 can be specifically used to establish a multi-objective optimization model based on the equipment compatibility quantification model combined with the transportation cost function; the multi-objective optimization model includes a screening layer and a solution layer; the screening layer is used to obtain the hard constraints corresponding to the allocation task, and the hard constraints of the equipment compatibility quantification model are filtered to obtain a feasible solution set; combined with the calculation result of the transportation cost function, the solution layer is used to optimize the equipment compatibility, transportation economy and task timeliness of the feasible solution set output by the screening layer.

[0097] Furthermore, in a specific implementation, in the above-mentioned cross-warehouse equipment allocation device provided in an embodiment of the present invention, the parameter adjustment module 13 is used to obtain multi-source data from the work order system, the warehouse management system and the monitoring platform; the multi-source data is used to obtain the equipment status, road condition data and transportation tool status, and the dimensional weights and / or transportation cost function of the equipment compatibility quantification model are adjusted to minimize transportation costs, maximize equipment compatibility, and maximize timeliness as the goals, and generate an optimal allocation plan for a set number that meets preset constraints.

[0098] Furthermore, in a specific implementation, the above-mentioned cross-warehouse equipment allocation device provided in the embodiment of the present invention may also include: an integration module, which is used to merge multiple allocation tasks when there are multiple allocation tasks received, and to integrate and analyze the merged allocation tasks to track the allocation progress, combine and optimize multiple allocation plans, and generate an integrated plan for shared transportation resources.

[0099] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any of the above-mentioned embodiments of the cross-warehouse equipment allocation method.

[0100] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps of any of the above-mentioned cross-warehouse equipment allocation method embodiments when running.

[0101] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0102] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of any of the above-mentioned cross-warehouse equipment allocation method embodiments are implemented.

[0103] An embodiment of the present invention further provides another computer program product, comprising a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above-mentioned cross-warehouse equipment allocation method embodiments are implemented.

[0104] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0105] The above is a detailed introduction to the method, device, equipment and medium for cross-warehouse equipment allocation provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, the present invention can also be improved and modified in several ways, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A method for allocating equipment across warehouses, characterized in that: include: Build a device compatibility quantification model based on the multi-dimensional characteristics of the device and the corresponding dimension weights; Using factors influencing transportation decision including mission urgency, the transportation cost function is defined; Receiving and responding to the allocation task, performing a multi-objective hierarchical solution for equipment compatibility, transportation economy, and task timeliness based on the equipment compatibility quantification model and the transportation cost function; While solving the problem, the dimension weights of the equipment compatibility quantification model and / or the transportation cost function are adjusted based on relevant data of the transportation process to generate an optimal allocation plan that meets the preset constraints.

2. The method for transferring equipment across warehouses according to claim 1, characterized in that: Based on the multi-dimensional characteristics of the device and the corresponding dimension weights, a device compatibility quantitative model is constructed, including: Obtaining multi-dimensional characteristics of the device; the multi-dimensional characteristics include model matching, firmware version, rack margin, power interface, network compatibility, heat dissipation requirements, and historical failure rate; The device compatibility is modeled using the obtained multi-dimensional features and the corresponding dimension weights, and a device compatibility quantitative model is constructed.

3. The method for allocating equipment across warehouses according to claim 1, characterized in that: Using factors influencing transportation decisions including mission urgency, we define a transportation cost function, including: The transportation cost function is defined using task urgency, transportation distance, equipment volume and weight, transportation tool type, ambient temperature and humidity, and path complexity.

4. The method for transferring equipment across warehouses according to claim 3, characterized in that: After defining the transportation cost function, also include: The transportation cost is calculated using the transportation cost function based on the task urgency coefficient, the allocation transportation distance value, the price valuation matrix associated with the transportation tool type, the path complexity penalty coefficient and the environmental penalty factor.

5. The method for allocating equipment across warehouses according to claim 4, characterized in that: The transportation cost is calculated using the following formula: ； in, To allocate the transport distance value, is the price corresponding to the type of transportation, is the path complexity penalty coefficient, is the task urgency coefficient, For emergency surcharge, is the time sensitivity coefficient, is the environmental penalty factor, For transportation costs, Indicates the total number of devices, .

6. The method for allocating equipment across warehouses according to claim 1, characterized in that: Based on the equipment compatibility quantification model and the transportation cost function, a multi-objective hierarchical solution is performed for equipment compatibility, transportation economy, and task timeliness, including: Establishing a multi-objective optimization model based on the equipment compatibility quantification model and the transportation cost function; the multi-objective optimization model includes a screening layer and a solution layer; Utilizing the screening layer to obtain hard constraints corresponding to the allocation task, filtering the hard constraints on the device compatibility quantification model to obtain a feasible solution set; Combined with the calculation results of the transportation cost function, the solution layer is used to optimize the feasible solution set output by the screening layer in terms of equipment compatibility, transportation economy and task timeliness.

7. The method for transferring equipment across warehouses according to claim 6, characterized in that: The device compatibility quantification model is filtered by hard constraints to obtain a feasible solution set, including: The device compatibility quantification model is used to calculate the compatibility score between devices using multi-dimensional device parameters. The device parameters include the matching degree between the device model and the target warehouse model, the difference in firmware version numbers, the remaining space ratio of the target shelf, the power plug type, the matching degree between the network port type and the target switch port, the ratio of the device heat dissipation requirement to the cooling capacity of the target cabinet, and the average annual failure rate of similar devices in the target warehouse environment. Based on the calculated compatibility scores between devices and the obtained hard constraints, the devices in each warehouse are filtered to select the devices that meet the hardware constraints to obtain a feasible solution set.

8. The method for transferring equipment across warehouses according to claim 6, characterized in that: Also includes: The solution layer is used to feed back the multi-objective optimization solution results to the screening layer, so that the screening layer adjusts the hard constraints until the optimal allocation plan is generated.

9. The method for allocating equipment across warehouses according to claim 1, characterized in that: Adjusting the dimension weights of the device compatibility quantification model and / or the transportation cost function to generate an optimal allocation plan that meets preset constraints includes: The dimension weights of the device compatibility quantification model and / or the transportation cost function are adjusted to minimize transportation costs, maximize device compatibility, and maximize timeliness, and generate an optimal allocation plan for a set number that meets preset constraints.

10. The method for transferring equipment across warehouses according to claim 9, characterized in that: Adjusting the dimension weights of the equipment compatibility quantification model and / or the transportation cost function based on relevant data of the transportation process includes: Acquire multi-source data from the work order system, warehouse management system, and monitoring platform; The multi-source data is used to obtain device status, road condition data, and transportation tool status, and the dimension weights of the device compatibility quantification model and / or the transportation cost function are adjusted.

11. The method for transferring equipment across warehouses according to claim 10, characterized in that: Also includes: The allocation process is tracked by utilizing the collaborative processing of the multi-source data, so that the allocation plan can be adjusted when an event occurs that exceeds a set range.

12. The method for allocating equipment across warehouses according to claim 1, characterized in that: Also includes: When there are multiple transfer tasks received, the multiple transfer tasks are merged and integrated and analyzed to track the progress of the transfer, and multiple transfer plans are combined and optimized to generate an integrated plan for shared transportation resources.

13. An electronic device, characterized in that: include: memory for storing computer programs; A processor is configured to implement the steps of the method for allocating equipment across warehouses as claimed in any one of claims 1 to 12 when executing the computer program.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the cross-warehouse equipment allocation method according to any one of claims 1 to 12 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the cross-warehouse equipment allocation method according to any one of claims 1 to 12 are implemented.

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