RFID-based steel warehouse management system

Abstract

The invention discloses a steel warehouse management system based on RFID, and belongs to the technical field of warehouse management, and the system comprises a steel state analysis module which constructs a steel state model based on the historical use frequency, the weight and the steel corrosion rate, and outputs a state coefficient; the storage location complexity analysis module is used for calculating a complexity coefficient through storage location height, humidity and historical extraction time; the storage location resource analysis module is used for generating a resource coefficient in combination with the use frequency and the weight index; the storage location evaluation module is used for fusing the complexity coefficient and the resource coefficient to output a storage location evaluation coefficient; the storage location-steel adaptation analysis module is used for outputting a storage location-steel adaptation coefficient based on the steel value index, the storage location evaluation coefficient and the state coefficient; and the transfer vehicle speed optimization module is used for dynamically adjusting the target speed of the transfer vehicle according to the adaptation coefficient, the vehicle load index and the parking emergency index. According to the invention, intelligent matching of the storage location and optimization of the transfer speed are realized, and the storage efficiency, safety and resource utilization rate are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of warehouse management, and particularly relates to a steel warehouse management system based on RFID. BACKGROUND

[0002] Traditional steel warehouse management relies on manual experience and static rules, and has the following defects:

[0003] Unreasonable allocation of storage locations: high-value, corrosion-prone steel is not preferentially allocated to dry, high-quality storage locations, resulting in rusting risk;

[0004] Low work efficiency: manual decision-making of storage locations and transfer speed, delayed response, and delayed processing of urgent tasks;

[0005] Imbalance of resource utilization: high-frequency use of storage locations leads to congestion, and heavy steel storage is prone to overload;

[0006] Poor environmental adaptability: humidity-sensitive steel is not dynamically matched with dry storage locations, accelerating corrosion.

[0007] Existing RFID technology is mainly used for tracking goods, and does not deeply integrate intelligent decision-making models for steel characteristics (corrosion rate, weight), storage location status (height, humidity), and work efficiency (pick-up time, urgency), which restricts the fine level of warehouse management. SUMMARY

[0008] To overcome the deficiencies of the prior art, the present application provides a steel warehouse management system based on RFID, which solves the above problems.

[0009] To achieve the above purpose, the present application realizes the following technical scheme: a steel warehouse management system based on RFID, comprising:

[0010] A steel state analysis module, which constructs a steel state model based on the historical use frequency of the incoming steel, the weight of the steel, and the corrosion rate of the steel under standard conditions (the rusted area ratio per unit time can be obtained through a salt spray test) to output a steel state coefficient;

[0011] A storage location complexity analysis module, which constructs a storage location complexity model based on the height of the storage location, the humidity of the storage location, and the historical pick-up time of the steel in the storage location to output a storage location complexity coefficient;

[0012] A storage location resource analysis module, which constructs a storage location resource model based on the historical use frequency of the storage location and the storage location weight index to output a storage location resource coefficient;

[0013] A storage location evaluation module, which constructs a storage location evaluation model based on the storage location complexity coefficient and the storage location resource coefficient to output a storage location evaluation coefficient;

[0014] The warehouse position-steel material adaptation analysis module is configured based on a warehouse position evaluation coefficient under the value of the warehouse-in steel material and a steel material state coefficient to construct a warehouse position-steel material adaptation model to output a warehouse position-steel material adaptation coefficient.

[0015] The transfer vehicle speed optimization module is configured based on a transfer vehicle load index under the warehouse position-steel material adaptation coefficient, a steel material warehouse-in emergency index and a transfer vehicle reference speed to construct a vehicle speed optimization model to output a transfer vehicle target speed.

[0016] On the basis of the above technical solutions, the application further provides the following optional technical solutions.

[0017] The further technical solution further comprises a data acquisition module configured to acquire a warehouse-in steel material historical use frequency, a steel material weight, a steel material corrosion rate under a standard state, a warehouse position height, a warehouse position humidity and a historical time required for taking out the steel material in the warehouse, a steel material value, a transfer vehicle load index, a steel material warehouse-in emergency index and a transfer vehicle reference speed, wherein the warehouse position weight index is a ratio of the warehouse-in steel material weight to a maximum allowed storage weight, the vehicle load index is a ratio of the steel material weight to a rated load of the transfer vehicle, and the steel material warehouse-in emergency index is a normalized value after maximum-minimum normalization processing of a remaining warehouse-in time.

[0018] The further technical solution is that the transfer vehicle speed optimization model is expressed as:

[0019] v tar =v base (w1e ind +w2(1-r d,ind )+w3M)

[0020] wherein v tar represents the transfer vehicle target speed, v base represents the transfer vehicle reference speed, e ind represents the steel material warehouse-in emergency index, r d,ind represents the transfer vehicle load index, M represents the warehouse position-steel material adaptation coefficient, w i represents a weight coefficient and ∑d i =1.

[0021] The further technical solution is that the working steps of the warehouse position-steel material adaptation analysis module are:

[0022] The warehouse-in steel material value is subjected to maximum-minimum normalization processing to acquire a warehouse-in steel material value index;

[0023] The warehouse position-steel material adaptation model is constructed based on the warehouse-in steel material value index, a warehouse position evaluation coefficient of a storage warehouse position and a steel material state coefficient to output a warehouse position-steel material adaptation coefficient, and the warehouse position-steel material adaptation model is expressed as:

[0024] M = V ind K s K z,l

[0025] wherein, M represents the storage location-steel material matching coefficient, V ind represents the storage steel material value index, K z,l represents the storage location evaluation coefficient of the storage location, K s represents the steel material state coefficient, and the M∈[0, 1] and the higher the value represents the better matching of the storage location and the steel material.

[0026] Further technical solutions: the step of the steel material state analysis module is:

[0027] The historical use frequency of the storage steel material, the steel material weight, and the steel material corrosion rate are maximum-minimum normalized to obtain a steel material use frequency index, a steel material weight index, and a corrosion rate index;

[0028] A steel material state model is constructed based on the steel material use frequency index, the steel material weight index, and the corrosion rate index, and the steel material state model is represented as:

[0029] K s = a1f ind +a2w ind +a3c ind

[0030] wherein, K s represents the steel material state coefficient, f ind represents the steel material use frequency index, w ind represents the steel material weight index, c ind represents the corrosion index, a i represents the weight coefficient and ∑a i = 1.

[0031] The current steel material use frequency index, the current steel material weight index, and the current corrosion rate index are imported into the steel material state model to output the current steel material state coefficient.

[0032] Further technical solutions: the working steps of the storage location complexity analysis module are:

[0033] The storage location height, the storage location humidity, and the historical time required for taking out the steel material in the storage location are maximum-minimum normalized to obtain a storage location height index, a storage location humidity index, and a taking-out time index;

[0034] A storage location complexity model is constructed based on the storage location height index, the storage location humidity index, and the taking-out time index, and the storage location complexity model is represented as:

[0035] K l = b1h ind +b2dind +b3t ind

[0036] wherein, K l represents a warehouse complexity coefficient, h ind represents a warehouse height index, d ind represents a warehouse humidity index, t ind represents a retrieval time index, a i represents a weight coefficient and ∑a i =1, the K l ∈[0,1] and the higher the value, the more complex the warehouse is;

[0037] The current warehouse height index, the current warehouse humidity index and the current retrieval time index are introduced into the warehouse complexity model to obtain a current warehouse complexity coefficient.

[0038] Further technical solutions: the working steps of the warehouse resource analysis module are:

[0039] The maximum-minimum normalization processing is performed on the warehouse historical use frequency to obtain a warehouse use frequency index;

[0040] A warehouse resource model is constructed based on the warehouse use frequency index and the warehouse weight index, and the warehouse resource model is represented as:

[0041] K z =1-(c1f l,ind +c2r ind )

[0042] wherein, K z represents a warehouse resource coefficient, f l,ind represents a warehouse use frequency index, r ind represents a warehouse weight index, c i represents a weight coefficient and ∑c i =1, the K z ∈[0,1] and the higher the value, the more abundant the warehouse resource is;

[0043] The current warehouse use frequency index and the current warehouse weight index are introduced into the warehouse resource model to output a current warehouse resource coefficient.

[0044] Further technical solutions: the working steps of the warehouse evaluation module are:

[0045] The current warehouse complexity coefficient and the current warehouse resource coefficient are introduced into the constructed warehouse evaluation model to output a warehouse evaluation coefficient, and the warehouse evaluation model is represented as:

[0046] K z,l =d1(1-K l )+d2K z

[0047] wherein, K z,l represents a warehouse position evaluation coefficient, K l represents a current warehouse position complexity coefficient, K z represents a current warehouse position resource coefficient, d i represents a weight coefficient and ∑d i =1, the K z,l ∈[0,1] and the higher the value, the better the warehouse position evaluation is;

[0048] Taking the warehouse position with the maximum warehouse position evaluation coefficient among all warehouse positions as the storage warehouse position.

[0049] The application provides a steel storage management system based on RFID, and has the following beneficial effects compared with the prior art:

[0050] 1. The steel state coefficient and the warehouse position evaluation coefficient are dynamically matched, so that high-value and easily-corroded steels are automatically distributed to dry and high-quality warehouse positions, the rust loss rate is reduced, the vehicle speed optimization model improves the emergency task response speed by coupling the load, the urgency and the matching coefficient, the multi-module collaborative replacement of artificial experience reduces the warehouse position distribution time, the warehouse position-steel matching coefficient quantifies the matching quality and reduces the failure rate. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The figure is a flowchart of the application. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and do not limit the application.

[0053] The specific implementation of the application is described in detail below in combination with specific examples.

[0054] Please refer to Figure 1 An embodiment of the application provides a steel storage management system based on RFID, which comprises:

[0055] The steel state analysis module is used to construct a steel state model based on the historical use frequency of the incoming steel, the weight of the steel and the corrosion rate of the steel under the standard state (the rust area ratio per unit time can be obtained through a salt spray test), and outputs a steel state coefficient.

[0056] The warehouse position complexity analysis module is used to construct a warehouse position complexity model based on the height of the warehouse position, the humidity of the warehouse position and the historical time required for taking out the steel in the warehouse, and outputs a warehouse position complexity coefficient.

[0057] The warehouse position resource analysis module constructs a warehouse position resource model based on the historical use frequency of the warehouse position and a warehouse position weight index to output a warehouse position resource coefficient, wherein the warehouse position weight index is a ratio of the weight of the incoming steel to the maximum allowable storage weight of the warehouse position.

[0058] The warehouse position evaluation module constructs a warehouse position evaluation model based on the warehouse position complexity coefficient and the warehouse position resource coefficient to output a warehouse position evaluation coefficient.

[0059] The warehouse position-steel material adaptation analysis module constructs a warehouse position-steel material adaptation model based on the warehouse position evaluation coefficient under the value of the incoming steel material and the steel material state coefficient to output a warehouse position-steel material adaptation coefficient.

[0060] The transfer vehicle speed optimization module constructs a vehicle speed optimization model based on the transfer vehicle load index under the warehouse position-steel material adaptation coefficient, the steel material incoming warehouse emergency index, and the reference vehicle speed of the transfer vehicle to output a transfer vehicle target speed, wherein the vehicle load index refers to a ratio of the weight of the steel material to the rated load of the transfer vehicle, and the incoming warehouse emergency index refers to a normalized value obtained by maximum-minimum normalization processing on the remaining incoming warehouse time.

[0061] The steel material corrosion rate can be obtained by a salt spray test to quantify the loss speed of the steel material in the storage environment. The warehouse position weight index is a ratio of the weight of the incoming steel material to the maximum allowable storage weight of the warehouse position, which is used to prevent overload and optimize space utilization. The steel material incoming warehouse emergency index is obtained by maximum-minimum normalization processing on the remaining incoming warehouse time, which is used to dynamically adjust the transportation priority. The transfer vehicle load index is a ratio of the weight of the steel material to the rated load of the vehicle, which ensures the safety of the transportation process. The warehouse position evaluation coefficient is generated by combining the complexity coefficient and the resource coefficient, which is used to screen the storage location with the best comprehensive performance.

[0062] Specifically, the steel material state analysis module generates a state coefficient reflecting the storage urgency of the steel material by normalizing the historical use frequency, weight, and corrosion rate. The warehouse position complexity analysis module converts the height, humidity, and retrieval time into an operation difficulty index to provide environmental adaptability evaluation for warehouse position allocation. The warehouse position resource analysis module dynamically monitors the load state of the warehouse position by calculating the use frequency and weight index. The warehouse position evaluation module integrates complexity and resource data to generate a multi-dimensional warehouse position quality score. The warehouse position-steel material adaptation analysis module introduces a value index to weight the evaluation coefficient and the state coefficient, ensuring that high-value or easily damaged steel materials are matched with high-quality warehouse positions. The transfer vehicle speed optimization module adjusts the reference speed according to the adaptation coefficient to prioritize emergency tasks and high-adaptation steel materials within the safe load range.

[0063] Compared with the prior art, the existing system relies on manual experience to allocate the storage location, and lacks quantitative analysis of dynamic parameters such as the corrosion rate of steel and the humidity of the storage location. The traditional method sets the transfer speed according to fixed rules, and cannot dynamically adjust according to the urgency and adaptability of the storage location. The scheme realizes closed-loop optimization of the storage location and the transportation strategy by constructing a multi-layer decision model and fusing the parameters of the steel characteristics, the storage location state and the operation efficiency. For example, when high-corrosion-rate steel is detected, the system automatically matches a low-humidity storage location and increases the transfer priority, while the traditional system cannot establish such dynamic correlation.

[0064] Through the above technical scheme, the present application realizes accurate matching of high-value or easily-corroded steel with storage locations, reduces the risk of rust damage caused by environmental inadaptability. By dynamically evaluating the operation complexity and resource utilization rate of the storage location, the delay of manual decision-making is reduced, and the warehouse operation efficiency is improved. Based on the real-time load index and the urgency, the transfer speed is adjusted to optimize the task processing time efficiency under the premise of ensuring transportation safety. Through multi-dimensional parameter fusion decision-making, the allocation of storage location resources is effectively balanced, and resource waste caused by local overload or idling is avoided.

[0065] Preferably, it further comprises a data acquisition module for acquiring the historical use frequency of the incoming steel, the weight of the steel, the corrosion rate of the steel in the standard state, the height of the storage location, the humidity of the storage location, and the historical time required for taking out the steel in the storage, the value of the steel, the load index of the transfer vehicle, the emergency index of the incoming steel, and the reference speed of the transfer vehicle.

[0066] Preferably, the step of the steel state analysis module is:

[0067] The historical use frequency of the incoming steel, the weight of the steel, and the corrosion rate of the steel are subjected to maximum-minimum normalization processing to obtain a steel use frequency index, a steel weight index, and a corrosion rate index;

[0068] A steel state model is constructed based on the steel use frequency index, the steel weight index, and the corrosion rate index, and the steel state model is represented as:

[0069] K s =a1f ind +a2w ind +a3c ind

[0070] Wherein, K s represents a steel state coefficient, f ind represents a steel use frequency index, w ind represents a steel weight index, c ind represents a corrosion index, a i represents a weight coefficient and ∑a i =1.

[0071] The current steel material usage frequency index, the current steel material weight index, and the current corrosion rate index are introduced into the steel material state model to output a current steel material state coefficient.

[0072] The steel material historical usage frequency refers to the number of warehouse in and out statistics of the steel material in the historical period. Specifically, the ratio of the number of calls in the time window to the total inventory can be used to achieve the steel material turnover demand. The steel material weight refers to the actual mass of a single steel material, which can be obtained through a weighing sensor or pre-recorded material data, and is used to associate the load-bearing capacity requirement of the storage location. The steel material corrosion rate refers to the proportion of the rusted area of the steel material surface per unit time, which can be calculated through a salt spray test or environmental sensor monitoring data, and is used to represent the rust damage risk of the steel material in the storage environment. The max-min normalization processing refers to linearly mapping the original data to the 0-1 interval, which is used to eliminate the influence of different dimensions on the model calculation. The weight coefficient a i refers to the contribution of each feature index in the state model, which can be dynamically adjusted according to the storage strategy, such as determined by expert experience method or machine learning training, and is used to balance the priority of different characteristics in the steel material state evaluation.

[0073] Specifically, the steel material historical usage frequency, weight, and corrosion rate are first normalized to generate standardized indexes. The steel material usage frequency index reflects its turnover demand, such as high-frequency usage of steel material needing to be close to the operation area; the steel material weight index determines the load-bearing requirement of the storage location, such as heavy steel material needing to be allocated to low-level storage locations; and the corrosion rate index quantifies environmental sensitivity, such as high-corrosion-risk steel material needing to be preferentially matched with dry storage locations. By linearly weighting and fusing the three indexes, a comprehensive steel material state coefficient is generated. The weight coefficient can be adjusted according to actual needs, such as increasing the weight of the corrosion rate index for high-value steel material to strengthen rust protection, and increasing the weight of the usage frequency index for frequently called steel material to optimize access efficiency. The final output state coefficient dynamically reflects the real-time state of the steel material, providing a quantitative basis for subsequent storage location adaptation.

[0074] Compared with the prior art, the traditional method relies on manual experience to statically evaluate the steel material state, and does not quantize the dynamic correlation of historical usage frequency and corrosion rate, resulting in high-value steel material being allocated to unsuitable storage locations due to evaluation deviation. The present scheme eliminates dimensional differences through normalization processing, constructs a quantitative model with multiple factors, and realizes dynamic balance evaluation of steel material turnover demand, load-bearing requirement, and environmental sensitivity, thereby accurately matching storage location resources.

[0075] By the technical solution, the application solves the evaluation deviation problem caused by not quantifying the steel characteristics dynamically in traditional warehouse management. Through normalization processing and a weighted fusion mechanism, the historical use frequency, weight and corrosion rate of the steel are converted into comparable standardized indexes, and combined with configurable weight coefficients, a comprehensive evaluation result reflecting the real-time state of the steel is dynamically generated. For example, for easily-corroded steel, by increasing the weight of the corrosion rate index, it is ensured that it is preferentially allocated to a low-humidity storage location; for heavy steel, by adjusting the weight to strengthen the constraint condition of load-bearing restriction. Thus, the risk of storage decision-making errors caused by inaccurate state evaluation is reduced, and the storage rationality of high-value and easily-corroded steel is optimized.

[0076] Preferably, the working steps of the storage location complexity analysis module are:

[0077] The storage location height, storage location humidity and historical time required for taking out the steel in the storage location are subjected to maximum-minimum normalization processing to obtain a storage location height index, a storage location humidity index and a taking-out time index;

[0078] A storage location complexity model is constructed based on the storage location height index, the storage location humidity index and the taking-out time index, and the storage location complexity model is represented as:

[0079] K l =b1h ind +b2d ind +b3t ind

[0080] wherein K l represents a storage location complexity coefficient, h ind represents a storage location height index, d ind represents a storage location humidity index, t ind represents a taking-out time index, a i represents a weight coefficient and ∑a i =1, the K l ∈[0,1] and the higher the value, the more complex the storage location is;

[0081] The current storage location height index, the current storage location humidity index and the current taking-out time index are imported into the storage location complexity model to obtain a current storage location complexity coefficient.

[0082] wherein the storage location height index refers to converting the physical height of the storage location into a value with a unified dimension by a maximum-minimum normalization method, which can specifically be realized by mapping the storage location height data to the 0-1 interval, for eliminating the influence of height difference on the difficulty of storage location operation.

[0083] The warehouse location humidity index refers to converting warehouse location environment humidity data into standardized numerical values through a maximum-minimum normalization method. Specifically, real-time data collected based on a humidity sensor can be normalized to quantify the risk degree of humidity on the steel storage environment.

[0084] The taking-out time index refers to converting the time required for historical taking-out of steel from a warehouse location into a standardized parameter through a maximum-minimum normalization method. Specifically, historical operation records can be analyzed to calculate the time distribution, reflecting the historical performance of warehouse location operation efficiency.

[0085] The linear weighting model refers to a mathematical model that linearly combines the height index, humidity index, and taking-out time index according to preset weight coefficients. Specifically, a constraint condition can be set that the sum of the weight coefficients is 1, to dynamically quantify the comprehensive influence of different factors on warehouse location complexity.

[0086] Specifically, after normalization of the warehouse location height, humidity, and historical taking-out time, the dimensional differences are eliminated and unified to the same numerical range. For example, the warehouse location height index maps the actual height value to the 0-1 interval, allowing for horizontal comparison of warehouse locations with different heights. The humidity index converts the environment humidity into a parameter positively correlated with corrosion risk through normalization. The taking-out time index reflects the historical level of warehouse location operation efficiency through normalization. Subsequently, the three indices are superimposed according to the preset weight coefficients through the linear weighting model, and the weight coefficients can be adjusted according to the actual scene. For example, when the warehouse humidity has a greater impact on steel storage, the weight coefficient of the humidity index can be increased. The final output of the warehouse location complexity coefficient dynamically represents the comprehensive complexity of the warehouse location in the 0-1 interval, providing data support for subsequent warehouse location evaluation.

[0087] Compared with the prior art, the traditional method relies on manual experience to judge warehouse location complexity and does not quantitatively analyze warehouse location height, humidity, and historical operation efficiency, resulting in subjective bias in warehouse allocation. The present scheme converts multi-dimensional warehouse features into a complexity coefficient that can be dynamically calculated through data normalization and a linear weighting model, avoiding the subjectivity of manual experience and enabling real-time response to changes in warehouse state. For example, when the humidity of a certain warehouse location suddenly increases, the humidity index and the corresponding complexity coefficient will automatically adjust, triggering dynamic optimization of the warehouse evaluation model.

[0088] By the technical solution, the application solves the problems of low work efficiency and unbalanced resource utilization caused by insufficient evaluation of storage location complexity. For example, by quantifying the influence of storage location height on the operation difficulty of the handling equipment, heavy steel can be preferentially allocated to low-height storage locations, reducing the load pressure on the equipment; by dynamically calculating the humidity index, the storage of easily corroded steel in high-humidity storage locations can be avoided, reducing the risk of rust damage; by analyzing the historical pick-up time index, inefficient storage locations can be identified and work paths can be optimized, improving the response speed of emergency tasks.

[0089] Preferably, the working steps of the storage location resource analysis module are:

[0090] The maximum-minimum normalization processing is performed on the historical usage frequency of the storage location to obtain a storage location usage frequency index;

[0091] A storage location resource model is constructed based on the storage location usage frequency index and the storage location weight index, and the storage location resource model is represented as:

[0092] K z = 1-(c1f l,ind +c2r ind )

[0093] wherein K z represents a storage location resource coefficient, f l,ind represents a storage location usage frequency index, r ind represents a storage location weight index, c i represents a weight coefficient and ∑c i = 1, K z ∈[0,1] and the higher the value, the more abundant the storage location resources are;

[0094] The current storage location usage frequency index and the current storage location weight index are imported into the storage location resource model to output the current storage location resource coefficient.

[0095] The historical usage frequency of the storage location refers to the number of times or the length of time the storage location is occupied within a certain time period, which can be realized by using a time series statistical method, for quantifying the frequency of the storage location being called to avoid congestion caused by continuous occupation of high-frequency use storage locations. The storage location weight index refers to the ratio of the weight of the incoming steel to the maximum allowable storage weight of the storage location, which can be obtained by combining real-time weighing sensors and database queries, for dynamically monitoring the load state of the storage location to prevent safety hazards caused by overloading storage. The maximum-minimum normalization processing refers to linearly mapping the original data to the [0,1] interval, which can be realized by using the extreme value method, for eliminating the differences between different dimension data and making the storage location usage frequency and weight load comparable. The storage location resource model refers to a mathematical model that integrates the storage location usage frequency index and the weight index by linear weighting, which can be dynamically adjusted by using a preset weight coefficient, for comprehensively evaluating the remaining availability of the storage location resources.

[0096] Specifically, the storage location resource analysis module first normalizes the historical usage frequency of the storage location to generate a storage location usage frequency index, which directly reflects the frequency of the storage location being occupied. At the same time, by calculating the ratio of the weight of the incoming steel to the maximum allowable storage weight of the storage location, the storage location weight index is obtained to monitor the load state of the storage location in real time. Subsequently, the two indexes are input into the storage location resource model, the model uses a linear weighting form to fuse the usage frequency and weight load, and the result is converted into a positive index through a complement operation. For example, when the weight coefficients c1 and c2 are set to 0.6 and 0.4 respectively, the model will pay more attention to the influence of storage location usage frequency on resource availability. The storage location resource coefficient output from this can dynamically represent the remaining availability of the storage location resource. When the coefficient tends to 1, it indicates that the storage location is idle and has low load, which is suitable for priority allocation; when the coefficient tends to 0, it indicates that the storage location is congested or overloaded, and further allocation should be avoided.

[0097] Compared with the prior art, the traditional method relies on fixed rules to evaluate the storage location resource, and cannot dynamically respond to changes in storage location usage frequency and load fluctuations, which easily leads to unbalanced resource allocation. The present scheme eliminates the dimensional difference of data through normalization processing, and combines a dynamic weight adjustment mechanism, so that the resource evaluation result can reflect the actual state of the storage location in real time. For example, in a heavy steel warehouse, by increasing the weight coefficient of the weight index, the monitoring capability of the storage location load can be strengthened. The prior art lacks such flexible adjustment mechanism and is difficult to adapt to the needs of different storage scenarios.

[0098] Through the above technical solutions, the present application can dynamically identify the congestion trend of high-frequency usage storage locations and monitor the weight load state of the storage location in real time, avoiding the imbalance of resource utilization caused by static allocation rules. For example, when a storage location is continuously used at high frequency, its resource coefficient will decrease significantly, and the system will automatically reduce the allocation of new incoming steel to this storage location, thereby relieving the congestion. At the same time, when the storage location weight index approaches the threshold, the resource coefficient decreases to trigger the early warning mechanism, preventing overloading storage from causing structural risks.

[0099] Preferably, the working steps of the storage location evaluation module are:

[0100] The current storage location complexity coefficient and the current storage location resource coefficient are input into the constructed storage location evaluation model to output a storage location evaluation coefficient, and the storage location evaluation model is represented as:

[0101] K z,l = d1(1-K l )+d2K z

[0102] Wherein, K z,l represents the storage location evaluation coefficient, K l represents the current storage location complexity coefficient, Kz represents the current bin resource coefficient, d i represents the weight coefficient and ∑d i = 1, the K z,l ∈ [0, 1] and the higher the value, the better the bin evaluation.

[0103] Take the bin with the maximum bin evaluation coefficient in all bins as the storage bin.

[0104] The bin complexity coefficient is a quantitative index reflecting the difficulty of bin operation, which can be realized by normalized weighting calculation of bin height, humidity and historical taking-out time. The higher the value, the greater the complexity of bin operation. The bin resource coefficient is a comprehensive index reflecting the carrying capacity and use state of the bin, which can be realized by normalized weighting calculation of bin historical use frequency and weight index. The higher the value, the more sufficient the bin resource. The weight coefficient is a parameter for adjusting the importance of different evaluation dimensions, which can be realized by analytic hierarchy process or expert experience assignment, to ensure the balance between bin complexity reduction and resource optimization.

[0105] Specifically, the bin evaluation coefficient is generated by linear combination of the bin complexity coefficient converted into a positive index (1-K l ) and the bin resource coefficient K z ). The inverse processing of the bin complexity coefficient reflects the demand to reduce the operation complexity, while the bin resource coefficient directly reflects the balance of bin carrying capacity and use state. The introduction of weight coefficients d1 and d2 enables the system to dynamically adjust the evaluation focus according to different warehouse scenarios, for example, in emergency task scenarios, the d1 weight can be increased to preferentially select easy-to-operate bins. By traversing the evaluation coefficients of all bins and selecting the maximum value, it is ensured that the storage bin meets the operation convenience and resource sufficiency, avoiding the problems of high-frequency use bin overload and heavy steel storage over-limit.

[0106] Compared with the prior art, the traditional method relies on fixed rules for bin allocation and does not consider the dynamic changes of bin operation complexity and resource state, resulting in low efficiency when high-value steel is stored in complex bins. The present scheme solves the problem that static rules cannot adapt to dynamic environment by establishing a quantitative evaluation model, real-time fusion of bin operation complexity inverse index and resource state index, and realizes intelligent optimization configuration of bin resources.

[0107] Through the above technical scheme, the present application can dynamically balance the bin operation complexity and resource utilization, effectively reduce the risk of heavy steel storage overload, improve the emergency task response efficiency, and at the same time reduce the operation delay of high-value steel due to storage in complex bins, realize the fine management of warehouse resources and the substantial improvement of operation efficiency.

[0108] Preferably, the working steps of the storage location-steel material adaptation analysis module are:

[0109] The value of the incoming steel material is subjected to maximum-minimum normalization processing to obtain a value index of the incoming steel material;

[0110] Based on the value index of the incoming steel material, the storage location evaluation coefficient of the storage location, and the steel material state coefficient, a storage location-steel material adaptation model is constructed to output a storage location-steel material adaptation coefficient, which is represented as:

[0111] M = V ind K s K z,l

[0112] Wherein, M represents the storage location-steel material adaptation coefficient, V ind represents the value index of the incoming steel material, K z,l represents the storage location evaluation coefficient of the storage location, K s represents the steel material state coefficient, and the M ∈ [0, 1] and the higher the value represents the better adaptability of the storage location to the steel material.

[0113] Wherein, the value index of the incoming steel material refers to a standardized index obtained by mapping the original value data of different steel materials to the interval [0, 1] through maximum-minimum normalization processing, which can be realized by using the highest value and the lowest value of the historical incoming steel material as the normalization reference to eliminate the dimensional differences of different steel material values. The steel material state coefficient refers to a comprehensive parameter reflecting the corrosion resistance and usage state of the steel material, which can be realized by weighted calculation of the steel material usage frequency, weight, and corrosion rate to quantify the quality degradation risk of the steel material in the storage environment. The storage location evaluation coefficient refers to an evaluation index representing the quality of the storage location environment and the availability of resources, which can be realized by combined calculation of the storage location complexity coefficient and the resource coefficient to dynamically evaluate the adaptability of the storage location to the storage of steel materials.

[0114] Specifically, the value of the incoming steel material is first normalized to generate a value index, which is multiplied with the steel material state coefficient and the storage location evaluation coefficient. The steel material state coefficient is determined by the properties of the steel material itself, for example, the corrosion rate index of easily corroded steel material is higher, which will reduce the steel material state coefficient, while the storage location evaluation coefficient is determined by the environmental parameters of the storage location, for example, high-quality storage locations with low humidity and low complexity have higher storage location evaluation coefficients. When the value index of the steel material is high, the adaptation coefficient is more sensitive to changes in the storage location evaluation coefficient and the steel material state coefficient, making high-value and easily corroded steel materials preferentially match high-quality storage locations with high storage location evaluation coefficients. For example, when the value index of a certain steel material is 0.9, the steel material state coefficient is 0.8, and the target storage location evaluation coefficient is 0.7, the adaptation coefficient is 0.9 × 0.8 × 0.7 = 0.504, which will serve as the core basis for the decision of storage location allocation.

[0115] Compared with the prior art, the traditional method only relies on manual experience to statically allocate the storage location, and the dynamic correlation of the value, state and storage location parameters is not established. Although the existing RFID technology can track the location of goods, it lacks the design of the value index normalization processing and the multi-coefficient product model of steel, and cannot realize the automatic matching of high-value and easily-corroded steel and high-quality storage location. The present scheme solves the fusion problem of the value dimension and the physical parameters by constructing a quantitative adaptive model.

[0116] Through the above technical scheme, the present application realizes the dynamic optimization and matching of the high-value steel storage location. When the steel value index and the storage location evaluation coefficient are simultaneously improved, the adaptive coefficient presents a nonlinear growth characteristic, which promotes the system to automatically allocate the high-value and easily-corroded steel to the dry and low-complexity high-quality storage location. At the same time, the boundary constraint of the adaptive coefficient avoids the influence of extreme parameters on the decision, for example, when the storage location evaluation coefficient is lower than 0.3, even if the steel value index reaches 1.0, the adaptive coefficient is only 0.3 at most, which effectively prevents the misallocation of low-quality storage location.

[0117] Preferably, the transfer vehicle speed optimization model is represented as:

[0118] v tar = v base (w1e ind +w2(1-r d,ind )+w3M)

[0119] wherein v tar represents the target speed of the transfer vehicle, v base represents the reference speed of the transfer vehicle, e ind represents the steel storage emergency index, r d,ind represents the load index of the transfer vehicle, M represents the storage-steel adaptive coefficient, w i represents the weight coefficient and ∑d i = 1.

[0120] The steel material storage emergency index is a value after the remaining storage time is normalized by maximum-minimum normalization. Specifically, the remaining time can be collected by a time sensor, and then linearly transformed to the interval [0, 1] to achieve the index. The index is used to quantify the timeliness requirement. The transfer vehicle load index is the ratio of the steel material weight to the rated load of the vehicle. Specifically, the steel material weight can be obtained by a weighing sensor, and the rated parameter of the vehicle can be calculated to obtain the index. The index is used to represent the safety margin of transportation. The storage location-steel material adaptation coefficient is a comprehensive index of the matching degree of the storage location evaluation and the steel material state. Specifically, the index can be generated by multiplying the normalized storage value index, the storage location evaluation coefficient and the steel material state coefficient. The coefficient is used to reflect the rationality of the storage decision. The weight coefficient is used to adjust the contribution of each factor to the target speed. Specifically, the value of w1, w2 and w3 can be 0.3, 0.4 and 0.3, respectively. The mechanism ensures the adjustability of the model parameters.

[0121] Specifically, the reference vehicle speed is used as a basic parameter to generate a dynamically optimized target speed by superimposing three core influencing factors. The normalized steel material storage emergency index is directly involved in the calculation. The higher the emergency index, the greater the speed increase. The vehicle load index is negatively related to the speed in the form of (1-r d,ind ) to make the load increase and the speed negatively related. When the load approaches the rated value, the speed is automatically reduced to avoid overload risk. The storage location-steel material adaptation coefficient is the quantitative result of the storage decision. The allocation of the high-adaptability storage location will trigger the speed increase to ensure the storage efficiency of high-value resources. The weight coefficient constraint mechanism ensures the balance of the contribution of each factor. By adjusting the weight, the demand of different storage scenarios can be adapted.

[0122] Compared with the prior art, the traditional method relies on artificial experience to set a fixed transfer speed, and cannot respond to the dynamic demand of emergency tasks, load changes and storage strategies. The present scheme establishes a mathematical optimization model to convert discrete business parameters into continuous speed control instructions, realizes the cooperative optimization of automatic acceleration of emergency tasks, automatic speed reduction of load overrun and priority acceleration of high-quality storage, and breaks through the response delay and subjective bias of artificial decision-making.

[0123] Through the above technical scheme, the present application can automatically adjust the transfer speed according to the task emergency degree during the storage of steel materials. For example, when the emergency index reaches 0.8, the vehicle speed is increased to 1.2 times the reference speed. When the vehicle load reaches 90% of the rated value, the vehicle speed is automatically reduced to 85% of the reference speed. When the storage location adaptation coefficient is higher than 0.7, the speed-up mechanism is triggered to preferentially complete the storage of high-value steel materials. Thus, the dynamic balance of transportation efficiency, safety and storage rationality is realized, and the operation delay and resource mismatch caused by the lag of artificial decision-making are effectively reduced.

[0124] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0125] While embodiments of the present application have been shown and described with reference to particular embodiments thereof, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the spirit and scope of the application. The scope of the application is defined by the appended claims and their equivalents.

Claims

1. An RFID-based steel material storage management system, characterized by, The application comprises: a steel material state analysis module, which is based on the historical use frequency of the incoming steel material, the steel material weight, and the steel material corrosion rate under the standard state to construct a steel material state model and output a steel material state coefficient; a storage location complexity analysis module, which is based on the storage location height, the storage location humidity, and the historical time required for taking out the steel material in the storage location to construct a storage location complexity model and output a storage location complexity coefficient; a storage location resource analysis module, which is based on the historical use frequency of the storage location and the storage location weight index to construct a storage location resource model and output a storage location resource coefficient; a storage location evaluation module, which is based on the storage location complexity coefficient and the storage location resource coefficient to construct a storage location evaluation model and output a storage location evaluation coefficient; a storage location-steel material adaptation analysis module, which is based on the storage location evaluation coefficient under the value of the incoming steel material and the steel material state coefficient to construct a storage location-steel material adaptation model and output a storage location-steel material adaptation coefficient; a transfer vehicle speed optimization module, which is based on the transfer vehicle load index under the storage location-steel material adaptation coefficient, the steel material incoming storage emergency index, and the transfer vehicle benchmark speed to construct a vehicle speed optimization model and output a transfer vehicle target speed.

2. The RFID-based steel material storage management system according to claim 1, characterized by, The application further comprises: a data acquisition module, which is used to acquire the historical use frequency of the incoming steel material, the steel material weight, the steel material corrosion rate under the standard state, the storage location height, the storage location humidity, and the historical time required for taking out the steel material in the storage location, the steel material value, the transfer vehicle load index, the steel material incoming storage emergency index, and the transfer vehicle benchmark speed, wherein the storage location weight index is the ratio of the incoming steel material weight to the maximum allowed storage weight, the vehicle load index refers to the ratio of the steel material weight to the rated load of the transfer vehicle, and the steel material incoming storage emergency index is the normalized value after the maximum-minimum normalization processing of the remaining incoming storage time.

3. The RFID-based steel material storage management system according to claim 2, characterized by, The transfer vehicle speed optimization model is represented as: v tar = v base (w1e ind +w2(1-r d,ind )+w3M) wherein v tar represents the target speed of the transfer vehicle, v base represents the reference vehicle speed of the transfer vehicle, e ind represents the steel storage emergency index, r d,ind represents the load index of the transfer vehicle, M represents the storage position-steel material matching coefficient, w i represents the weight coefficient and ∑w i = 1.

4. The RFID-based steel material storage management system according to claim 3, characterized by, The working steps of the storage location-steel material adaptation analysis module are: performing maximum-minimum normalization processing on the incoming steel material value to acquire an incoming steel material value index; constructing a storage location-steel material adaptation model based on the incoming steel material value index, the storage location evaluation coefficient of the storage location, and the steel material state coefficient to output a storage location-steel material adaptation coefficient, wherein the storage location-steel material adaptation model is represented as: M = V ind K s K z,l wherein M represents a storage position-steel material adaptation coefficient, V ind represents a storage position evaluation coefficient, K z,l represents a storage position evaluation coefficient, K s represents a steel material state coefficient, said M ∈ [0, 1] and a higher value indicates a better adaptation of the storage position to the steel material.

5. The RFID-based steel material storage management system according to claim 3, wherein The steps of the steel material state analysis module are: performing maximum-minimum normalization processing on the historical use frequency of the incoming steel material, the steel material weight, and the steel material corrosion rate to acquire a steel material use frequency index, a steel material weight index, and a corrosion rate index; constructing a steel material state model based on the steel material use frequency index, the steel material weight index, and the corrosion rate index, wherein the steel material state model is represented as: K s = a1f ind + a2w ind + a3c ind where K s represents a steel material state coefficient, f ind represents a steel material use frequency index, w ind represents a steel material weight index, c ind represents a corrosion index, a i represents a weight coefficient and ∑a i = 1; inputting the current steel material use frequency index, the current steel material weight index, and the current corrosion rate index into the steel material state model to output a current steel material state coefficient.

6. The RFID-based steel material storage management system according to claim 3, wherein The working steps of the storage location complexity analysis module are: performing maximum-minimum normalization processing on the storage location height, the storage location humidity, and the historical time required for taking out the steel material in the storage location to acquire a storage location height index, a storage location humidity index, and a taking-out time index; constructing a storage location complexity model based on the storage location height index, the storage location humidity index, and the taking-out time index, wherein the storage location complexity model is represented as: K l = b1h ind + b2d ind + b3t ind wherein K l represents a complexity coefficient of the bin position, h ind represents a height index of the bin position, d ind represents a humidity index of the bin position, t ind represents a retrieval time index, b i represents a weight coefficient and ∑b i = 1, said K l ∈ [0, 1] and the higher the value the more complex the bin. The current storage location height index, the current storage location humidity index and the current taking-out time index are introduced into the storage location complexity model to obtain a current storage location complexity coefficient.

7. The RFID-based steel material storage management system according to claim 3, wherein The working steps of the storage location resource analysis module are: The maximum-minimum normalization processing is performed on the storage location historical use frequency to obtain a storage location use frequency index; A storage location resource model is constructed based on the storage location use frequency index and the storage location weight index; The current storage location use frequency index and the current storage location weight index are introduced into the storage location resource model to output a current storage location resource coefficient.

8. The RFID-based steel material storage management system according to claim 3, characterized by, The working steps of the storage location evaluation module are: The current storage location complexity coefficient and the current storage location resource coefficient are introduced into the constructed storage location evaluation model to output a storage location evaluation coefficient; The storage location in which the maximum value of the storage location evaluation coefficients in all the storage locations is taken as a storage location.

9. The RFID-based steel material storage management system according to claim 7, wherein The storage location resource model is represented as: K z = 1 - (c1f l,ind + c2r ind ) wherein K z represents the bin resource coefficient, f l,ind represents the bin usage frequency index, r ind represents the bin weight index, c i represents the weight coefficient and ∑c i = 1, the K z ∈ [0, 1] and the higher the value, the more abundant the bin resource.

10. The RFID-based steel material storage management system according to claim 8, characterized by, The storage location evaluation model is represented as: K z,l = d1(1 - K l )+ d2K z wherein K z,l represents a current slot complexity coefficient, K l represents a current slot complexity coefficient, K z represents a current slot resource coefficient, d i represents a weight coefficient and ∑d i = 1, said K z,l ∈ [0, 1] and the higher the value the better the slot evaluation.

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