A cold storage material dispatching method and system based on the Internet of Things

Abstract

The present invention provides a cold storage material dispatching method and system based on the Internet of Things, which relates to the technical field of material analysis. The method includes: constructing a cold storage material dispatching framework based on the Internet of Things technology; obtaining real-time traffic information, location information, cargo information and temperature information; determining dispatching constraints according to the real-time traffic information, location information, cargo information and temperature information, wherein the dispatching constraints include material freshness constraints; under the constraints of the dispatching constraints, with the goal of reducing the total dispatching time and the total dispatching cost, determining the best cold storage material dispatching scheme through an ant colony algorithm; dispatching cold storage materials according to the best cold storage material dispatching scheme. The present invention can take the material freshness and the total dispatching time into consideration when determining the cold storage material dispatching scheme, thereby improving the quality of cold storage materials, optimizing the dispatching efficiency, reducing energy consumption and reducing the dispatching cost.

Description

Technical Field

[0001] The present invention relates to the technical field of material analysis, and in particular to a cold storage material dispatching method and system based on the Internet of Things. Background Art

[0002] Route planning has always been a key and hot issue in material dispatching. Optimizing the route can reduce the total distance traveled by vehicles, thereby reducing fuel consumption and directly reducing transportation costs.

[0003] Current path planning technology is mainly carried out through optimization algorithms such as particle swarm optimization algorithm, A* algorithm, genetic algorithm, etc. These optimization algorithms have been widely used due to their good global search capabilities and can quickly determine the best material scheduling plan.

[0004] The current path planning technology mainly aims to shorten the scheduling cost, so that the overall scheduling cost is minimized, which can reduce vehicle driving distance and fuel consumption, thereby reducing fuel costs, thereby reducing carbon emissions and environmental pollution, and promoting the development of green logistics.

[0005] However, as a mode of transportation with special requirements, cold chain transportation also places equal emphasis on time and the freshness of materials. Simply pursuing cost minimization may lead to longer delivery time and reduced freshness of goods. Therefore, current path planning technology is still difficult to apply to cold storage material scheduling. Summary of the invention

[0006] In order to solve the technical problems that the current path planning technology is difficult to apply to cold storage material scheduling, and the simple pursuit of cost minimization may lead to extended delivery time and reduced freshness of goods, the present invention provides a cold storage material scheduling method and system based on the Internet of Things.

[0007] The technical solution provided by the embodiment of the present invention is as follows:

[0008] First aspect:

[0009] An embodiment of the present invention provides a cold storage material dispatching method based on the Internet of Things, comprising:

[0010] S1: Build a cold storage material dispatching architecture based on IoT technology;

[0011] S2: Obtain real-time traffic information, location information, cargo information, and temperature information;

[0012] S3: Determine scheduling constraints based on real-time traffic information, location information, cargo information, and temperature information, where the scheduling constraints include material freshness constraints;

[0013] S4: Under the constraints of the scheduling constraints, with the goal of reducing the total scheduling time and the total scheduling cost, determine the optimal cold storage material scheduling plan through the ant colony algorithm;

[0014] S5: Dispatching cold storage materials according to the optimal cold storage material dispatching plan.

[0015] Second aspect:

[0016] An embodiment of the present invention provides a cold storage material dispatching system based on the Internet of Things, comprising:

[0017] processor;

[0018] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the cold storage material scheduling method based on the Internet of Things as described in the first aspect is implemented.

[0019] The third aspect:

[0020] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the cold storage material scheduling method based on the Internet of Things as described in the first aspect is implemented.

[0021] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0022] In the present invention, based on the Internet of Things technology, real-time traffic information, location information, cargo information and temperature information are obtained, the freshness of materials is monitored, and the freshness of materials is used as a constraint condition, with the goal of reducing the total scheduling time and the total scheduling cost. The ant colony algorithm is used to determine the best cold storage material scheduling plan. The freshness of materials and the total scheduling time can be taken into consideration when determining the cold storage material scheduling plan, thereby improving the quality of cold storage materials, optimizing scheduling efficiency, reducing energy consumption, and reducing scheduling costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0024] Figure 1 A schematic diagram of a flow chart of a cold storage material dispatching method based on the Internet of Things provided by an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of the structure of a cold storage material dispatching system based on the Internet of Things provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0027] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0028] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0029] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0030] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0031] Reference Manual Attached Figure 1 , showing a flow chart of a cold storage material scheduling method based on the Internet of Things provided by an embodiment of the present invention.

[0032] The embodiment of the present invention provides a cold storage material scheduling method based on the Internet of Things, which can be implemented by a cold storage material scheduling device based on the Internet of Things, and the cold storage material scheduling device based on the Internet of Things can be a terminal or a server. The processing flow of the cold storage material scheduling method based on the Internet of Things may include the following steps:

[0033] S1: Build a cold storage material dispatching architecture based on IoT technology.

[0034] Among them, the Internet of Things (IoT) refers to a network system that connects objects to the Internet through various information sensing devices (such as sensors, RFID, wireless communication equipment, etc.) to achieve intelligent identification, positioning, monitoring and management.

[0035] In a possible implementation, the cold storage material dispatching architecture includes a vehicle networking data acquisition system, a vehicle networking gateway, a cloud platform, and a dispatching center.

[0036] The Internet of Vehicles data collection system collects real-time data and transmits it to the Internet of Vehicles gateway. The Internet of Vehicles gateway uploads the real-time data to the cloud platform. The dispatch center obtains real-time data from the cloud platform to dispatch cold storage materials.

[0037] The Internet of Vehicles data acquisition system includes: camera module, positioning module, cargo volume detection module and temperature detection module.

[0038] The camera module is used to obtain real-time traffic information, the positioning module is used to obtain real-time location information, the cargo capacity detection module is used to obtain real-time cargo capacity information, and the temperature detection module is used to obtain real-time temperature information.

[0039] In the present invention, based on the Internet of Things technology, through the camera module, positioning module, cargo volume detection module and temperature detection module, key data in the transportation process can be obtained in real time to ensure the timeliness and accuracy of the data. The data storage and processing capabilities of the cloud platform are used to perform big data analysis and prediction, optimize the scheduling plan, and improve the scheduling efficiency. The scheduling center can make dynamic adjustments based on real-time data, improve the flexibility and response speed of scheduling, and avoid delays and unexpected situations.

[0040] S2: Obtain real-time traffic information, location information, cargo information, and temperature information.

[0041] S3: Determine scheduling constraints based on real-time traffic information, location information, cargo information, and temperature information.

[0042] In a possible implementation, the scheduling constraints specifically include:

[0043] Material freshness constraint: The freshness of the materials upon delivery is greater than the minimum freshness requirement.

[0044] Optionally, the freshness of the materials is:

[0045] δ=δ0e -εT

[0046] Among them, δ represents the freshness of materials, δ0 represents the initial freshness of materials, e represents the natural constant, ε represents the freshness attenuation coefficient, and T represents the total scheduling time.

[0047] Among them, those skilled in the art can set the minimum freshness requirement according to actual conditions, and the present invention does not limit it.

[0048] It should be noted that the material freshness constraints ensure that the freshness of the goods meets the minimum requirements during transportation and ensure the quality of the goods.

[0049] Vehicle capacity constraint: The total amount of customer demand must not exceed the maximum load capacity of the vehicle.

[0050] It should be noted that the vehicle capacity constraint ensures that the load of each vehicle does not exceed its maximum load to prevent overloading.

[0051] Total length of driving path constraint: The total length of the driving path cannot exceed the maximum distance that the vehicle can travel in one time.

[0052] It should be noted that the total length constraint of the driving route ensures that the vehicle can complete the task in one trip and will not fail to complete the delivery due to the route being too long.

[0053] Total driving time constraint: The total driving time of the route cannot exceed the driver’s fatigue driving time.

[0054] It should be noted that the total driving time constraint ensures that the driver's driving time is within a reasonable range, avoids fatigue driving, and improves safety.

[0055] Lane Width Constraint: The lane width on the path is greater than the vehicle width.

[0056] It should be noted that the lane width constraint ensures that vehicles can safely pass through all paths and will not be unable to pass due to insufficient lane width.

[0057] Lane weight constraint: The maximum load-bearing weight of the lane on the path is greater than the vehicle loading capacity.

[0058] It should be noted that lane weight constraints ensure that vehicles are not overloaded on the driving path, prevent overloading of roads and bridges, and ensure safety.

[0059] In the present invention, by setting and following these constraints, the safety, efficiency and quality of goods in the cold chain transportation process can be ensured, while the scheduling plan can be optimized and the overall transportation cost can be reduced.

[0060] S4: Under the constraints of scheduling constraints, with the goal of reducing the total scheduling time and total scheduling cost, the ant colony algorithm is used to determine the optimal cold storage material scheduling plan.

[0061] In a possible implementation, S4 specifically includes:

[0062] S401: Construct a cold storage material scheduling objective function with the goal of reducing the total scheduling time and the total scheduling cost.

[0063] Optionally, the cold storage material scheduling objective function is specifically:

[0064] f(X)=λ1C+λ2T

[0065] Among them, f represents the objective function of cold storage material scheduling, C represents the total scheduling cost, T represents the total scheduling time, λ1 represents the weight coefficient of the total scheduling cost, and λ2 represents the weight coefficient of the total scheduling time.

[0066] Among them, those skilled in the art can set the weight coefficient λ1 of the total scheduling cost and the weight coefficient λ2 of the total scheduling time according to actual conditions, and the present invention does not limit this.

[0067] In the present invention, the cold storage material dispatch objective function is set as the weighted sum of the total dispatch cost and the total time, and multi-objective optimization is achieved by adjusting the weight coefficient. The benefits of this include improving dispatch efficiency, reducing operating costs, reducing energy consumption, and improving service quality.

[0068] Optionally, the total scheduling cost is specifically:

[0069] C=C1+C2+C3+C4+C5

[0070] Among them, C represents the total dispatch cost, C1 represents the fixed cost, C2 represents the transportation cost, C3 represents the cargo damage cost, C4 represents the refrigeration cost, and C5 represents the penalty cost.

[0071] In the present invention, the total dispatch cost is concretized and decomposed into multiple sub-cost items, which helps to comprehensively evaluate and make costs transparent, improve cost control and resource allocation efficiency, optimize transportation routes and cargo quality, and promote energy conservation, emission reduction and social responsibility.

[0072] Optionally, fixed costs include driver wages and vehicle wear and tear costs, and fixed costs are fixed values.

[0073] Optionally, the shipping costs are specified as:

[0074]

[0075] Among them, x ij represents the path decision parameter between the i-th path node and the j-th path node. The path nodes include: the starting point representing the dispatch center, the road intersection, and the end point representing the customer. When x ij =1, it means the path passes through the i-th path node and the j-th path node. ij = 0, indicating that the path between the i-th path node and the j-th path node is not passed, s ij represents the distance between the i-th path node and the j-th path node, a represents the transportation cost per unit driving distance, and n represents the total number of path nodes.

[0076] Optionally, the cargo damage cost is specifically:

[0077]

[0078] Among them, p represents the unit price of materials, V represents the loading capacity of the cold storage transport vehicle, e represents the natural constant, ε represents the freshness attenuation coefficient, and t ij represents the travel time between the i-th path node and the j-th path node, t j represents the stay time at the jth path node.

[0079] Optionally, the cooling cost is specifically:

[0080]

[0081] Among them, G1 represents the refrigerant consumption per unit time during transportation, G2 represents the refrigerant consumption per unit time when opening the door for unloading, and b represents the unit price of refrigerant.

[0082] Optionally, the refrigerant consumption per unit time during transportation is specifically:

[0083] G1=γ1ηSΔT

[0084] Among them, γ1 represents the first refrigerant consumption coefficient, η represents the heat transfer coefficient of the cold storage, S represents the heat transfer surface area of ​​the cold storage, and ΔT represents the temperature difference.

[0085] Optionally, the refrigerant consumption per unit time of opening the door for unloading is specifically:

[0086] G2=γ2S d ΔT

[0087] Where γ2 represents the second refrigerant consumption coefficient, S d Indicates the door area.

[0088] Optionally, the penalty cost is specified as:

[0089]

[0090] Among them, ω1 represents the early penalty coefficient, ω2 represents the late penalty coefficient, and ea k represents the earliest arrival time specified by the path node represented by the kth customer, la k represents the latest arrival time specified by the path node of the k-th customer representative, s k It represents the time to reach the path node of the k-th customer representative, max represents the maximum value, min represents the minimum value, and K represents the total number of path nodes of the customer representative.

[0091] Optionally, the total scheduling time is specifically:

[0092]

[0093] Where T represents the total scheduling time, xij represents the path decision parameter between the i-th path node and the j-th path node. The path nodes include: the starting point representing the dispatch center, the road intersection, and the end point representing the customer. When x ij =1, it means the path passes through the i-th path node and the j-th path node. ij = 0, indicating that the path between the i-th path node and the j-th path node is not passed, t ij represents the travel time between the i-th path node and the j-th path node, t j represents the stay time at the jth path node.

[0094] S402: Under the constraints of the scheduling constraints, the optimal cold storage material scheduling plan is determined through the ant colony algorithm with the goal of minimizing the cold storage material scheduling objective function.

[0095] Among them, the Ant Colony Optimization (ACO) is an optimization algorithm based on simulating the foraging behavior of ants in nature. Applying the Ant Colony Algorithm to path optimization is a relatively mature prior art, and the present invention improves the existing Ant Colony Algorithm.

[0096] Specifically, set the starting point and end point of the ant colony algorithm.

[0097] Ant individuals traverse the path nodes in the scheduling network and select the next path node based on the state transition probability value.

[0098] Among them, the state transition probability value between each node is:

[0099]

[0100] in, represents the state transition probability value of the kth ant from node i to node j, τ ij (t) represents the pheromone concentration between node i and node j at the tth iteration, α represents the information heuristic factor, η ij (t) represents the expected degree of transfer from node i to node j in the tth iteration, β represents the expected heuristic factor, C represents the set of nodes that can be selected starting from node i, and s represents any node in the set of nodes that can be selected starting from node i.

[0101] It should be noted that pheromones reflect the empirical information of the path. After multiple iterations, the pheromone concentration on a good path will increase, attracting more ants to choose this path, thereby strengthening the optimal path. The expectation level is usually related to the path distance or cost, reflecting the intuitive quality of the path and directly affecting the ants' choice tendency. By combining pheromones and expectation levels, we can comprehensively utilize historical experience and the quality of the current path to make more effective path selection.

[0102] Among them, those skilled in the art can set the size of the information heuristic factor α and the expected heuristic factor β according to actual conditions, and the present invention does not limit this.

[0103] Among them, the expected degree η of transferring from node i to node j in the tth iteration is ij The specific calculation method of (t) is:

[0104]

[0105] Among them, d sj represents the distance between the starting point s and the node j, d ij represents the distance between node i and node j, d je represents the distance between node j and end point e, d si represents the distance between the starting point s and the node i, d ie represents the distance between node i and end point e, and ξ represents the balance factor.

[0106] It should be noted that, on the basis of introducing the estimated cost value, a balance factor is added to accelerate the convergence of the algorithm. The starting point, current point, next point, and target point are comprehensively considered to reduce the ant search space.

[0107] Search efficiency is improved, and path optimization capability is enhanced.

[0108] Among them, the balance factor ξ is specifically:

[0109]

[0110] Among them, T m represents the maximum number of iterations, and t represents the current number of iterations.

[0111] It should be noted that as the number of iterations increases, the balance factor ξ is dynamically adjusted, so that the algorithm pays more attention to local information (exploring new paths) in the early stage and more attention to global information (strengthening excellent paths) in the later stage.

[0112] Determine whether the ant colony individual has reached the end point. If so, proceed to the next step. Otherwise, continue traversing the nodes.

[0113] Save the optimal path in this cycle and update the pheromone concentration:

[0114] τ ij (t+1)=(1-ρ)τ ij (t)+ρΔτ ij (t)

[0115]

[0116] Among them, τ ij (t+1) represents the pheromone concentration between node i and node j in the t+1th iteration, ρ represents the pheromone volatility coefficient, Δτ ij (t) represents the pheromone increment between node i and node j in the tth iteration, It represents the amount of pheromone released between the kth ant node i and the node j in the tth iteration.

[0117] Among them, those skilled in the art can set the size of the pheromone volatility coefficient ρ according to actual conditions, and the present invention does not limit it.

[0118] The calculation method of pheromone increment is as follows:

[0119]

[0120] Among them, Q represents the total amount of pheromone, L k (t) represents the total path length of the kth ant in the tth iteration, F k (t) represents the number of turning points in the path traversed by the kth ant in the tth iteration, μ1 represents the impact factor of the path length term, and μ2 represents the impact factor of the path turning point term.

[0121] Among them, those skilled in the art can set the size of the path length item influence factor μ1 and the path inflection point item influence factor μ2 according to actual conditions, and the present invention does not limit this.

[0122] In the present invention, the pheromone increment is inversely proportional to the path length and the number of inflection points. High-quality paths (i.e., shorter paths with fewer inflection points) will receive more pheromone increments, which encourages ants to choose these high-quality paths in subsequent iterations. The pheromone concentration on high-quality paths increases, attracting more ants to choose this path, and gradually strengthening the selection of the optimal path.

[0123] Determine whether the number of iterations has reached the maximum number of iterations. If so, output the optimal cold storage material scheduling plan. Otherwise, return to continue iterating.

[0124] S5: Dispatch cold storage materials according to the best cold storage material dispatching plan.

[0125] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0126] In the present invention, based on the Internet of Things technology, real-time traffic information, location information, cargo information and temperature information are obtained, the freshness of materials is monitored, and the freshness of materials is used as a constraint condition. The goal is to reduce the total scheduling time and the total scheduling cost. The ant colony algorithm is used to determine the best cold storage material scheduling plan. The freshness of materials and the total scheduling time can be taken into consideration when determining the cold storage material scheduling plan, thereby improving the quality of cold storage materials, optimizing scheduling efficiency, and reducing scheduling costs.

[0127] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a cold storage material dispatching system based on the Internet of Things provided by the present invention.

[0128] The present invention also provides a cold storage material dispatching system 20 based on the Internet of Things, which is applied to the above-mentioned cold storage material dispatching method based on the Internet of Things, comprising:

[0129] Processor 201;

[0130] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the cold storage material scheduling method based on the Internet of Things as described in the method embodiment is implemented.

[0131] The cold storage material dispatching system 20 based on the Internet of Things provided by the present invention can execute the above-mentioned cold storage material dispatching method based on the Internet of Things and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0132] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0133] In the present invention, based on the Internet of Things technology, real-time traffic information, location information, cargo information and temperature information are obtained, the freshness of materials is monitored, and the freshness of materials is used as a constraint condition, with the goal of reducing the total scheduling time and the total scheduling cost. The ant colony algorithm is used to determine the best cold storage material scheduling plan. The freshness of materials and the total scheduling time can be taken into consideration when determining the cold storage material scheduling plan, thereby improving the quality of cold storage materials, optimizing scheduling efficiency, reducing energy consumption, and reducing scheduling costs.

[0134] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0135] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0136] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0137] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0138] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0139] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0140] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0143] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0144] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0145] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0146] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the cold storage material scheduling method based on the Internet of Things as described in the method embodiment is implemented.

[0147] A computer-readable storage medium provided by the present invention can implement the steps and effects of the cold storage material scheduling method based on the Internet of Things in the above method embodiment. To avoid repetition, the present invention will not go into details.

[0148] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0149] In the present invention, non-destructive testing based on X-rays is adopted to extract color features and texture features of X-ray images, and the abnormal values ​​of each pixel in the image are calculated based on the color features and texture features, and then based on threshold segmentation, the defect position of the industrial component to be inspected is determined. There is no need for load training, the calculation process is simple, time is saved, and the calculation cost is low, which is conducive to batch detection of industrial components and has high defect detection efficiency.

[0150] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0151] There are a few points to note:

[0152] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0153] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0154] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0155] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A cold storage material dispatching method based on the Internet of Things, characterized in that: include: S1: Build a cold storage material dispatching architecture based on IoT technology; S2: Obtain real-time traffic information, location information, cargo information, and temperature information; S3: Determine scheduling constraints based on real-time traffic information, location information, cargo information, and temperature information, where the scheduling constraints include material freshness constraints; S4: Under the constraints of the scheduling constraints, with the goal of reducing the total scheduling time and the total scheduling cost, determine the optimal cold storage material scheduling plan through the ant colony algorithm; S5: Dispatching cold storage materials according to the optimal cold storage material dispatching plan; Wherein, the S4 specifically includes: S401: constructing a cold storage material scheduling objective function with the goal of reducing the total scheduling time and total scheduling cost; S402: Under the constraints of the scheduling constraints, with the goal of minimizing the cold storage material scheduling objective function, determine the best cold storage material scheduling plan through an ant colony algorithm; The cold storage material scheduling objective function is specifically: f(X)=λ1C+λ2T Among them, f represents the cold storage material scheduling objective function, C represents the total scheduling cost, T represents the total scheduling time, λ1 represents the weight coefficient of the total scheduling cost, and λ2 represents the weight coefficient of the total scheduling time; The total scheduling cost is specifically: C=C1+C2+C3+C4+C5 Among them, C represents the total dispatch cost, C1 represents the fixed cost, C2 represents the transportation cost, C3 represents the cargo damage cost, C4 represents the refrigeration cost, and C5 represents the penalty cost; Wherein, the fixed costs include driver wages and vehicle wear and tear costs, and the fixed costs are fixed values; The transportation costs are specifically: Among them, x ij represents the path decision parameter between the i-th path node and the j-th path node, wherein the path nodes include: the starting point representing the dispatch center, the road intersection, and the end point representing the customer. ij =1, it means the path passes through the i-th path node and the j-th path node. ij = 0, indicating that the path between the i-th path node and the j-th path node is not passed, s ij represents the distance between the i-th path node and the j-th path node, a represents the transportation cost per unit travel distance, and n represents the total number of path nodes; The cargo damage costs are specifically: Among them, p represents the unit price of materials, V represents the loading capacity of the cold storage transport vehicle, e represents the natural constant, ε represents the freshness attenuation coefficient, and t ij represents the travel time between the i-th path node and the j-th path node, t j represents the stay time at the jth path node; The refrigeration cost is specifically: Among them, G1 represents the refrigerant consumption per unit time during transportation, G2 represents the refrigerant consumption per unit time when opening the door for unloading, and b represents the unit price of refrigerant; The refrigerant consumption per unit time during transportation is specifically: G1=γ1ηSΔT Among them, γ1 represents the first refrigerant consumption coefficient, η represents the heat transfer coefficient of the cold storage, S represents the heat transfer surface area of ​​the cold storage, and ΔT represents the temperature difference; The refrigerant consumption per unit time of opening the door for unloading is specifically: G2=γ2S d ΔT Where γ2 represents the second refrigerant consumption coefficient, S d Indicates the door area; The penalty costs are specifically: Among them, ω1 represents the early penalty coefficient, ω2 represents the late penalty coefficient, and ea k represents the earliest arrival time specified by the path node represented by the kth customer, la k represents the latest arrival time specified by the path node of the k-th customer representative, s k represents the time to reach the path node of the k-th customer representative, max represents the maximum value, min represents the minimum value, and K represents the total number of path nodes of the customer representative; The total scheduling time is specifically: Where T represents the total scheduling time, x ij represents the path decision parameter between the i-th path node and the j-th path node, wherein the path nodes include: the starting point representing the dispatch center, the road intersection, and the end point representing the customer. ij =1, it means the path passes through the i-th path node and the j-th path node. ij = 0, indicating that the path between the i-th path node and the j-th path node is not passed, t ij represents the travel time between the i-th path node and the j-th path node, t j represents the stay time at the jth path node; The S402 specifically includes: Set the starting and ending points of the ant colony algorithm; The ant individuals traverse the path nodes in the scheduling network and select the next path node according to the state transition probability value; Among them, the state transition probability value between each node is: in, represents the state transition probability value of the kth ant from node i to node j, τ ij (t) represents the pheromone concentration between node i and node j at the tth iteration, α represents the information heuristic factor, η ij (t) represents the expected degree of transfer from node i to node j in the tth iteration, β represents the expected heuristic factor, C represents the set of nodes that can be selected starting from node i, and s represents any node in the set of nodes that can be selected starting from node i; Among them, the expected degree η of transferring from node i to node j in the tth iteration is ij The specific calculation method of (t) is: Among them, d sj represents the distance between the starting point s and the node j, d ij represents the distance between node i and node j, d je represents the distance between node j and end point e, d si represents the distance between the starting point s and the node i, d ie represents the distance between node i and end point e, ξ represents the balance factor; Among them, the balance factor ξ is specifically: Among them, T m represents the maximum number of iterations, and t represents the current number of iterations; Determine whether the ant colony individual has reached the end point; if so, proceed to the next step; otherwise, continue traversing the nodes; Save the optimal path in this cycle and update the pheromone concentration: t ij (t+1)=(1-ρ)τ ij (t)+ρΔτ ij (t) Among them, τ ij (t+1) represents the pheromone concentration between node i and node j in the t+1th iteration, ρ represents the pheromone volatility coefficient, Δτ ij (t) represents the pheromone increment between node i and node j in the tth iteration, represents the number of pheromones released between the kth ant node i and the node j in the tth iteration; The calculation method of pheromone increment is as follows: Among them, Q represents the total amount of pheromone, L k (t) represents the total path length of the kth ant in the tth iteration, F k (t) represents the number of turning points in the path traversed by the kth ant in the tth iteration, μ1 represents the impact factor of the path length term, and μ2 represents the impact factor of the path turning point term; Determine whether the number of iterations has reached the maximum number of iterations; if so, output the optimal cold storage material scheduling plan; otherwise, return to continue iterating.

2. According to the method for dispatching cold storage materials based on the Internet of Things according to claim 1, it is characterized in that: The cold storage material dispatching architecture includes a vehicle networking data acquisition system, a vehicle networking gateway, a cloud platform and a dispatching center; The Internet of Vehicles data collection system collects real-time data and transmits it to the Internet of Vehicles gateway, the Internet of Vehicles gateway uploads the real-time data to the cloud platform, and the dispatch center obtains real-time data from the cloud platform to dispatch cold storage materials; The Internet of Vehicles data acquisition system includes: a camera module, a positioning module, a cargo volume detection module and a temperature detection module; The camera module is used to obtain real-time road condition information, the positioning module is used to obtain real-time position information, the cargo volume detection module is used to obtain real-time cargo volume information, and the temperature detection module is used to obtain real-time temperature information.

3. The cold storage material dispatching method based on the Internet of Things according to claim 1 is characterized in that: The scheduling constraints specifically include: Material freshness constraint: the freshness of the materials upon delivery is greater than the minimum freshness requirement; Vehicle capacity constraint: The total amount of customer demand must not exceed the maximum load of the vehicle; Total length constraint of driving path: The total length of the driving path cannot exceed the maximum distance that the vehicle can travel at one time; Total driving time constraint: The total driving time of the route cannot exceed the driver's fatigue driving time; Lane width constraint: The lane width on the path is greater than the vehicle width; Lane weight constraint: The maximum load-bearing weight of the lane on the path is greater than the vehicle loading capacity.

4. The cold storage material dispatching method based on the Internet of Things according to claim 3 is characterized in that: The freshness of the materials is specifically as follows: δ=δ0e -εT Among them, δ represents the freshness of materials, δ0 represents the initial freshness of materials, e represents the natural constant, ε represents the freshness attenuation coefficient, and T represents the total scheduling time.

5. A cold storage material dispatching system based on the Internet of Things, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the cold storage material scheduling method based on the Internet of Things as described in any one of claims 1 to 4 is implemented.

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