Fresh cold chain informatization level adaptive optimization method and device

Abstract

The invention provides a fresh cold chain informatization level adaptive optimization method and device, and relates to the technical field of logistics and supply chain management, and the method comprises the steps: determining a coupling correlation factor between correlation feature dimensions based on a plurality of correlation feature dimensions of a fresh cold chain informatization level; obtaining fresh cold chain multi-source data based on the coupling correlation factor; based on the fresh cold chain multi-source data, determining the change rate of the informatization level of the fresh cold chain at different time intervals; based on the fresh cold chain informatization level change rates at different time intervals, key influence factors of the fresh cold chain informatization level change rates are determined; and based on the key influence factors, the fresh cold chain informatization level dynamic prediction model and the particle swarm algorithm model, determining the input cost for optimizing the fresh cold chain informatization level and a corresponding time node. According to the fresh cold chain informatization level adaptive optimization method provided by the invention, adaptive optimization of the fresh cold chain informatization level is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and supply chain management, and in particular to a method and device for adaptively optimizing the information level of a fresh cold chain. Background Art

[0002] In the field of logistics and supply chain management technology, improving information technology is crucial for increasing logistics efficiency, reducing losses, and optimizing resource allocation. However, the level of information technology is often influenced by multiple factors, including information technology investment, management, service, innovation, security, and value-added. These factors are interrelated, forming a complex system, making the optimization of information technology particularly complex.

[0003] However, for the fresh food cold chain industry, existing information systems often rely on single-dimensional data and lack comprehensive evaluation and prediction mechanisms, resulting in a low level of informatization. Summary of the Invention

[0004] The present invention provides a method and device for adaptively optimizing the informationization level of a fresh produce cold chain, which are used to solve the technical problem of low informationization level of a fresh produce cold chain in the prior art.

[0005] The present invention provides a method for adaptively optimizing the information level of a fresh cold chain, comprising the following steps: Determine the coupling correlation factors between the multiple correlation feature dimensions based on the information level of the fresh cold chain; Based on the coupling correlation factor, obtaining multi-source data of fresh cold chain; Based on the multi-source data of the fresh cold chain, determining the rate of change of the information level of the fresh cold chain at different time intervals; Determining key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals; Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the investment cost and the corresponding time node for optimizing the fresh cold chain informationization level are determined.

[0006] According to a method for adaptively optimizing the informationization level of a fresh cold chain provided by the present invention, the method determines the investment cost and corresponding time nodes for optimizing the informationization level of the fresh cold chain based on the key influencing factors, the dynamic prediction model of the informationization level of the fresh cold chain, and the particle swarm algorithm model, including: Inputting the key influencing factors into the dynamic prediction model for the informationization level of fresh cold chain to obtain a predicted value of the informationization level of fresh cold chain output by the dynamic prediction model for the informationization level of fresh cold chain; Inputting the maximum value of the key influencing factor and the predicted value of the fresh cold chain informationization level into the particle swarm optimization model, and obtaining the investment cost of optimizing the fresh cold chain informationization level and the corresponding time node output by the particle swarm optimization model; Among them, the key influencing factor is the particle swarm individual in the particle swarm algorithm model, the maximum value of the predicted value of the fresh cold chain informatization level is the target value of the global optimization in the particle swarm algorithm model; the constraint conditions of the key influencing factor are the input cost and the corresponding time node.

[0007] According to a method for adaptively optimizing the informationization level of a fresh cold chain provided by the present invention, the dynamic prediction model for the informationization level of a fresh cold chain includes: An input module, used for obtaining the key influencing factors; A feature extraction module, configured to extract key features based on the key influencing factors; A prediction module, configured to predict the level of informationization of the fresh produce cold chain based on the key features; The output module is used to output the predicted value of the fresh cold chain informationization level.

[0008] According to a method for adaptively optimizing the informationization level of a fresh produce cold chain provided by the present invention, determining the rate of change of the informationization level of the fresh produce cold chain at different time intervals based on the multi-source data of the fresh produce cold chain, comprising: Determine the impact of each source of data on the level of informationization of the fresh cold chain; Based on the influence proportion of each source data on the information level of the fresh cold chain, the multi-source data of the fresh cold chain are weighted and summed to determine the change rate of the information level of the fresh cold chain at different time intervals.

[0009] According to a method for adaptively optimizing the informationization level of a fresh produce cold chain provided by the present invention, determining key influencing factors of the rate of change of the informationization level of the fresh produce cold chain based on the rate of change of the informationization level of the fresh produce cold chain at different time intervals includes: The rate of change of the informationization level of the fresh cold chain at different time intervals is input into the Monte Carlo simulation algorithm model to obtain the key influencing factors of the rate of change of the informationization level of the fresh cold chain output by the Monte Carlo simulation algorithm model.

[0010] According to a method for adaptively optimizing the informationization level of a fresh cold chain provided by the present invention, the associated characteristic dimensions include informationization investment level, informationization management level, informationization service level, informationization innovation level, informationization security level, and informationization value-added level; The level of informatization investment includes the proportion of hardware facility investment, the frequency of software system updates, and the density of IT staffing; The information management level includes data standardization coverage, process digitization rate and abnormal event closed-loop processing rate; The information service level includes order response time, customer information traceability completeness and intelligent early warning accuracy; The information technology innovation level includes the patent technology conversion rate, new technology application coverage rate and R&D investment growth rate; The information security level includes data encryption transmission rate, system vulnerability repair timeliness and disaster recovery success rate; The level of information value-added includes the proportion of data service revenue, resource optimization and saving rate, and market forecast matching degree.

[0011] The present invention also provides a device for adaptively optimizing the information level of a fresh cold chain, comprising the following modules: A first determination module is configured to determine a coupling correlation factor between multiple correlation feature dimensions based on the informationization level of the fresh cold chain; An acquisition module, configured to acquire multi-source data of the fresh cold chain based on the coupling correlation factor; A second determination module is configured to determine the rate of change of the information level of the fresh produce cold chain at different time intervals based on the multi-source data of the fresh produce cold chain; A third determining module is configured to determine a key influencing factor of the rate of change of the fresh cold chain informationization level based on the rate of change of the fresh cold chain informationization level at different time intervals; The fourth determination module is used to determine the investment cost and corresponding time node for optimizing the information level of the fresh cold chain based on the key influencing factors, the dynamic prediction model of the information level of the fresh cold chain and the particle swarm algorithm model.

[0012] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any of the above-mentioned methods for adaptive optimization of the informationization level of the fresh cold chain.

[0013] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for adaptively optimizing the information level of the fresh cold chain as described above is implemented.

[0014] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for adaptively optimizing the informationization level of the fresh cold chain.

[0015] The present invention provides an adaptive optimization method for the informationization level of fresh cold chain. The method determines the coupling correlation factors between the associated feature dimensions based on multiple associated feature dimensions of the informationization level of fresh cold chain, thereby accurately describing the multiple dimensional characteristics of the informationization level of fresh cold chain and avoiding the evaluation bias problem caused by the single dimension of traditional methods; based on the coupling correlation factors, multi-source data of fresh cold chain is obtained; based on the multi-source data of fresh cold chain, the change rate of the informationization level of fresh cold chain at different time intervals is determined, thereby accurately capturing the dynamic law of the evolution of the informationization level of fresh cold chain over time and improving the subsequent prediction accuracy; based on the change rate of the informationization level of fresh cold chain at different time intervals, the key influencing factors of the change rate of the informationization level of fresh cold chain are determined; based on the key influencing factors, the dynamic prediction model of the informationization level of fresh cold chain and the particle swarm algorithm model, the input cost and corresponding time nodes for optimizing the informationization level of fresh cold chain are determined, thereby realizing adaptive optimization of the informationization level of fresh cold chain, improving the informationization level of fresh cold chain, and forming a closed-loop adaptive optimization mechanism to meet the dual needs of timeliness and cost control in the fresh cold chain industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a flow chart of a method for adaptive optimization of the information level of the fresh cold chain provided by the present invention.

[0018] Figure 2 It is a schematic diagram of the structure of the dynamic prediction model of the information level of the fresh cold chain provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of a fresh cold chain information level adaptive optimization device provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In the field of fresh produce cold chain logistics management, improving information technology is crucial for improving logistics efficiency, reducing losses, and optimizing resource allocation. However, the level of information technology within fresh produce cold chain companies is often influenced by multiple factors, including information technology investment, management, service, innovation, security, and value-added. These factors are interrelated, forming a complex system, making the assessment and optimization of information technology levels particularly complex.

[0023] Traditional methods for improving information technology often lack systematic evaluation and prediction mechanisms, resulting in high investment costs and difficult-to-quantify results. Therefore, achieving adaptive optimization of information technology levels in the fresh produce cold chain within cost constraints has become a pressing issue.

[0024] To this end, the present invention proposes an adaptive optimization method for the informationization level of fresh cold chain oriented to cost and time constraints. The method first establishes an identification model by quantitatively describing the correlation characteristics of the informationization level of fresh cold chain. Then, multi-source information acquisition technology and dynamic prediction model are used to determine the key influencing factors. Finally, based on the swarm intelligence algorithm-particle swarm algorithm, the minimum cost investment and time node for maximizing the improvement of the informationization level of fresh cold chain are determined, thereby realizing the adaptive optimization of the informationization level of fresh cold chain.

[0025] The following combination Figures 1 to 4 The present invention describes a method and device for adaptive optimization of the information level of the fresh cold chain.

[0026] Figure 1 This is a flow chart of a method for adaptive optimization of the information level of fresh cold chain provided by the present invention. Figure 1 As shown, the method includes the following steps: Step 101: Based on multiple correlation feature dimensions of the informationization level of the fresh cold chain, determine the coupling correlation factors between the correlation feature dimensions.

[0027] Specifically, we first determine the coupling correlation factors between the multiple correlation feature dimensions of the fresh cold chain informatization level by quantitatively describing them.

[0028] Optionally, the associated characteristic dimensions include information investment level, information management level, information service level, information innovation level, information security level, and information value-added level; The level of informatization investment includes the proportion of hardware facility investment, the frequency of software system updates, and the density of IT staffing; The information management level includes data standardization coverage, process digitization rate and abnormal event closed-loop processing rate; The information service level includes order response time, customer information traceability completeness and intelligent early warning accuracy; The information technology innovation level includes the patent technology conversion rate, new technology application coverage rate and R&D investment growth rate; The information security level includes data encryption transmission rate, system vulnerability repair timeliness and disaster recovery success rate; The level of information value-added includes the proportion of data service revenue, resource optimization and saving rate, and market forecast matching degree.

[0029] Specifically, the associated characteristic dimensions include six first-level dimensions: information investment level, information management level, information service level, information innovation level, information security level and information value-added level, among which each first-level dimension has multiple second-level dimensions.

[0030] For example, the level of informatization investment also includes secondary dimensions: the proportion of hardware facility investment, the frequency of software system updates, and the density of IT staffing. The level of information management also includes secondary dimensions: data standardization coverage, process digitization rate, and abnormal event closed-loop processing rate; The level of information service also includes secondary dimensions: order response time, customer information traceability completeness, and intelligent early warning accuracy; The level of informatization innovation also includes secondary dimensions: patent technology conversion rate, new technology application coverage rate, and R&D investment growth rate; The level of information security also includes secondary dimensions: data encryption transmission rate, system vulnerability repair timeliness, and disaster recovery success rate; The level of information technology value-added also includes secondary dimensions: the proportion of data service revenue, resource optimization and saving rate, and market forecast matching degree.

[0031] By quantitatively describing multiple related characteristic dimensions of the informationization level of the fresh cold chain, the coupling correlation factors between the related characteristic dimensions are determined. The coupling correlation factors include daily operation data, financial statement data, and after-sales service data.

[0032] Step 102: Based on the coupling correlation factor, obtain multi-source data of the fresh cold chain.

[0033] Specifically, based on the data from various sources such as daily operation data, financial statement data, and after-sales service data in the coupling correlation factors, a coupling correlation factor matrix is ​​constructed as the multi-source data of the fresh cold chain.

[0034] Step 103: Based on the multi-source data of the fresh cold chain, determine the rate of change of the information level of the fresh cold chain at different time intervals.

[0035] Optionally, determining the rate of change of the informationization level of the fresh produce cold chain at different time intervals based on the fresh produce cold chain multi-source data includes: Determine the impact of each source of data on the level of informationization of the fresh cold chain; Based on the influence proportion of each source data on the information level of the fresh cold chain, the multi-source data of the fresh cold chain are weighted and summed to determine the change rate of the information level of the fresh cold chain at different time intervals.

[0036] Specifically, by adopting multi-source data fusion technology, we first calculate the coupling correlation factor matrix between each dimension based on the correlation feature dimension system, and then use the weighted sum algorithm to fuse the multi-source heterogeneous data. Among them, the weight coefficient is dynamically allocated according to the influence ratio of each data source on the informatization level of the fresh cold chain. The influence ratio is used to establish a dynamic weight allocation model through the entropy method, thereby ensuring that the data fusion results can accurately reflect the spatiotemporal evolution characteristics of the informatization level.

[0037] On this basis, the embodiment of the present invention uses a time series analysis method to calculate the rate of change of the fresh cold chain informationization level at different time intervals (day / week / month), and constructs a dynamic evaluation index system to accurately reflect the changes in the fresh cold chain informationization level.

[0038] Step 104: Determine key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals.

[0039] Optionally, determining the key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals includes: The rate of change of the informationization level of the fresh cold chain at different time intervals is input into the Monte Carlo simulation algorithm model to obtain the key influencing factors of the rate of change of the informationization level of the fresh cold chain output by the Monte Carlo simulation algorithm model.

[0040] Specifically, the rate of change of the informationization level of the fresh cold chain at different time intervals is input into the Monte Carlo simulation algorithm model, and the key influencing factors of the rate of change of the informationization level of the fresh cold chain output by the Monte Carlo simulation algorithm model are obtained.

[0041] The embodiment of the present invention establishes a model for identifying key influencing factors through the Monte Carlo simulation algorithm, takes the sequence of the rate of change of the informationization level as the input parameter, and outputs a set of key influencing factors that have a significant impact on the system status through millions of random sampling simulations, thereby determining the key influencing factors that have a significant impact on the rate of change of the informationization level of the fresh cold chain.

[0042] Step 105: Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level, and the particle swarm optimization model, determine the investment cost and corresponding time node for optimizing the fresh cold chain informationization level.

[0043] Optionally, determining the investment cost and corresponding time node for optimizing the informationization level of the fresh cold chain based on the key influencing factors, the dynamic prediction model of the informationization level of the fresh cold chain, and the particle swarm optimization model includes: Inputting the key influencing factors into the dynamic prediction model for the informationization level of fresh cold chain to obtain a predicted value of the informationization level of fresh cold chain output by the dynamic prediction model for the informationization level of fresh cold chain; Inputting the maximum value of the key influencing factor and the predicted value of the fresh cold chain informationization level into the particle swarm optimization model, and obtaining the investment cost of optimizing the fresh cold chain informationization level and the corresponding time node output by the particle swarm optimization model; Among them, the key influencing factor is the particle swarm individual in the particle swarm algorithm model, the maximum value of the predicted value of the fresh cold chain informatization level is the target value of the global optimization in the particle swarm algorithm model; the constraint conditions of the key influencing factor are the input cost and the corresponding time node.

[0044] Specifically, after determining the key influencing factors, the key influencing factors are input into the dynamic prediction model of the informationization level of the fresh cold chain. The model consists of a four-layer architecture: the input module is used to receive multi-dimensional feature data, the feature extraction module uses the principal component analysis method to reduce the dimension and extract key features, the prediction module integrates the convolutional neural network (CNN) and the long short-term memory network (LSTM) in the recurrent neural network (RNN) to process the input key features and predict the predicted value of the fresh cold chain informationization level; the output module generates and outputs the informationization level prediction curve of multiple time scales based on the predicted fresh cold chain informationization level.

[0045] Based on the above embodiment, at the highest point of the prediction curve, which corresponds to the maximum value of the predicted value of the fresh cold chain informationization level, the maximum value of the key influencing factor and the predicted value of the fresh cold chain informationization level is input into the particle swarm algorithm model to obtain the investment cost and corresponding time node of the optimized fresh cold chain informationization level output by the particle swarm algorithm model. Among them, the key influencing factor is the particle swarm individual in the particle swarm algorithm model, and the maximum value of the predicted value of the fresh cold chain informationization level is the target value of the global optimization in the particle swarm algorithm model; the constraint condition of the key influencing factor is set as the investment cost and the corresponding time node.

[0046] The embodiment of the present invention constructs a particle swarm algorithm model based on the particle swarm optimization algorithm, maps key influencing factors into dimensional parameters of the particle swarm space, takes the maximization of the predicted value of the fresh cold chain informatization level as the target value of the global optimization of the particle swarm algorithm, and at the same time sets the input cost and the corresponding time node as the constraint conditions, and encodes the constraint conditions as the boundary conditions of the individual motion trajectory of the particle swarm, so as to obtain the optimal solution set through iterative calculation, output an optimization plan containing the three-dimensional constraints of cost-benefit-time, realize dynamic planning of the path to improve the fresh cold chain informatization level, and effectively solve the technical problems existing in the traditional cold chain logistics informatization construction, such as single evaluation dimension, delayed dynamic response, and insufficient multi-objective coordination.

[0047] The present invention provides an adaptive optimization method for the informationization level of fresh cold chain. The method determines the coupling correlation factors between the associated feature dimensions based on multiple associated feature dimensions of the informationization level of fresh cold chain, thereby accurately describing the multiple dimensional characteristics of the informationization level of fresh cold chain and avoiding the evaluation bias problem caused by the single dimension of traditional methods; based on the coupling correlation factors, multi-source data of fresh cold chain is obtained; based on the multi-source data of fresh cold chain, the change rate of the informationization level of fresh cold chain at different time intervals is determined, thereby accurately capturing the dynamic law of the evolution of the informationization level of fresh cold chain over time and improving the subsequent prediction accuracy; based on the change rate of the informationization level of fresh cold chain at different time intervals, the key influencing factors of the change rate of the informationization level of fresh cold chain are determined; based on the key influencing factors, the dynamic prediction model of the informationization level of fresh cold chain and the particle swarm algorithm model, the input cost and corresponding time nodes for optimizing the informationization level of fresh cold chain are determined, thereby realizing adaptive optimization of the informationization level of fresh cold chain, improving the informationization level of fresh cold chain, and forming a closed-loop adaptive optimization mechanism to meet the dual needs of timeliness and cost control in the fresh cold chain industry.

[0048] Optionally, the dynamic prediction model for the informationization level of the fresh cold chain includes: An input module, used for obtaining the key influencing factors; A feature extraction module, configured to extract key features based on the key influencing factors; A prediction module, configured to predict the level of informationization of the fresh produce cold chain based on the key features; The output module is used to output the predicted value of the fresh cold chain informationization level.

[0049] Specifically, Figure 2 This is a schematic diagram of the structure of the dynamic prediction model for the information level of the fresh cold chain provided by the present invention. Figure 2 As shown in the figure, the dynamic prediction model of the information level of fresh cold chain contains a four-layer architecture: the input module is used to receive multi-dimensional feature data, obtain key influencing factors (such as Figure 2The feature extraction module uses principal component analysis to reduce the dimensionality and extract key features. The prediction module integrates CNN and LSTM in RNN to process the input key features and predict the informationization level of the fresh food cold chain. The output module generates and outputs the informationization level prediction curve under multiple time scales t according to the predicted value of the fresh food cold chain informationization level. It accurately, efficiently and intuitively describes the changes in the fresh food cold chain informationization level under the conditions of multi-dimensional key influencing factors, thereby providing a basis for determining the optimization target value of the fresh food cold chain informationization level.

[0050] The following describes a device for adaptively optimizing the information level of a fresh cold chain provided by the present invention. The device for adaptively optimizing the information level of a fresh cold chain described below and the method for adaptively optimizing the information level of a fresh cold chain described above can be referenced to each other.

[0051] Based on any of the above embodiments, Figure 3 This is a structural diagram of a fresh cold chain information level adaptive optimization device provided by the present invention, such as Figure 3 The embodiment of the present invention provides a device for adaptive optimization of the information level of fresh cold chain, including a first determination module 301, an acquisition module 302, a second determination module 303, a third determination module 304 and a fourth determination module 305, wherein: The first determination module 301 is used to determine the coupling correlation factor between the associated feature dimensions based on multiple associated feature dimensions of the fresh cold chain informatization level; the acquisition module 302 is used to acquire the fresh cold chain multi-source data based on the coupling correlation factor; the second determination module 303 is used to determine the change rate of the fresh cold chain informatization level at different time intervals based on the fresh cold chain multi-source data; the third determination module 304 is used to determine the key influencing factors of the change rate of the fresh cold chain informatization level based on the change rate of the fresh cold chain informatization level at different time intervals; the fourth determination module 305 is used to determine the investment cost and corresponding time nodes for optimizing the fresh cold chain informatization level based on the key influencing factors, the fresh cold chain informatization level dynamic prediction model and the particle swarm algorithm model.

[0052] The present invention provides an adaptive optimization device for the informationization level of fresh cold chain. The device determines the coupling correlation factors between the associated feature dimensions based on multiple associated feature dimensions of the fresh cold chain informationization level, thereby accurately describing the multiple dimensional characteristics of the fresh cold chain informationization level and avoiding the evaluation bias problem caused by the single dimension of the traditional method; based on the coupling correlation factors, multi-source data of the fresh cold chain is obtained; based on the multi-source data of the fresh cold chain, the change rate of the fresh cold chain informationization level at different time intervals is determined, thereby accurately capturing the dynamic law of the evolution of the fresh cold chain informationization level over time and improving the subsequent prediction accuracy; based on the change rate of the fresh cold chain informationization level at different time intervals, the key influencing factors of the change rate of the fresh cold chain informationization level are determined; based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the input cost and corresponding time nodes for optimizing the fresh cold chain informationization level are determined, thereby realizing adaptive optimization of the fresh cold chain informationization level, improving the fresh cold chain informationization level, and forming a closed-loop adaptive optimization mechanism to meet the dual needs of the fresh cold chain industry for timeliness and cost control.

[0053] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4 As shown, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. The processor 410, the communications interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the method for adaptive optimization of the information level of the fresh cold chain, which includes: Determine the coupling correlation factors between the multiple correlation feature dimensions based on the information level of the fresh cold chain; Based on the coupling correlation factor, obtaining multi-source data of fresh cold chain; Based on the multi-source data of the fresh cold chain, determining the rate of change of the information level of the fresh cold chain at different time intervals; Determining key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals; Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the investment cost and the corresponding time node for optimizing the fresh cold chain informationization level are determined.

[0054] Furthermore, the logic instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or 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 capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0055] On the other hand, the present invention further provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the adaptive optimization method for the information level of the fresh cold chain provided by the above methods, which includes: Determine the coupling correlation factors between the multiple correlation feature dimensions based on the information level of the fresh cold chain; Based on the coupling correlation factor, obtaining multi-source data of fresh cold chain; Based on the multi-source data of the fresh cold chain, determining the rate of change of the information level of the fresh cold chain at different time intervals; Determining key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals; Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the investment cost and the corresponding time node for optimizing the fresh cold chain informationization level are determined.

[0056] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for adaptively optimizing the information level of the fresh cold chain provided by the above methods is implemented. The method includes: Determine the coupling correlation factors between the multiple correlation feature dimensions based on the information level of the fresh cold chain; Based on the coupling correlation factor, obtaining multi-source data of fresh cold chain; Based on the multi-source data of the fresh cold chain, determining the rate of change of the information level of the fresh cold chain at different time intervals; Determining key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals; Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the investment cost and the corresponding time node for optimizing the fresh cold chain informationization level are determined.

[0057] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0058] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0059] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0060] In the embodiments of the present application, "determine B based on A" means that the factor A must be considered when determining B. It is not limited to "B can be determined based on A alone", and should also include: "determine B based on A and C", "determine B based on A, C and E", "determine C based on A, and further determine B based on C", etc. It can also include taking A as a condition for determining B, for example, "when A meets the first condition, use the first method to determine B"; for example, "when A meets the second condition, determine B", etc.; for example, "when A meets the third condition, determine B based on the first parameter", etc. Of course, it can also be a condition that takes A as a factor in determining B, for example, "when A meets the first condition, use the first method to determine C, and further determine B based on C", etc.

[0061] In the present invention, the term "plurality" refers to two or more than two, and other quantifiers are similar to it.

[0062] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for adaptive optimization of the information level of fresh cold chain, characterized by: include: Determine the coupling correlation factors between the multiple correlation feature dimensions based on the information level of the fresh cold chain; Based on the coupling correlation factor, obtaining multi-source data of fresh cold chain; Based on the multi-source data of the fresh cold chain, determining the rate of change of the information level of the fresh cold chain at different time intervals; Determining key influencing factors of the rate of change of the fresh produce cold chain informationization level based on the rate of change of the fresh produce cold chain informationization level at different time intervals; Based on the key influencing factors, the dynamic prediction model of the fresh cold chain informationization level and the particle swarm algorithm model, the investment cost and the corresponding time node for optimizing the fresh cold chain informationization level are determined.

2. The method for adaptive optimization of the information level of the fresh cold chain according to claim 1 is characterized in that: The step of determining the investment cost and corresponding time node for optimizing the informationization level of the fresh cold chain based on the key influencing factors, the dynamic prediction model of the informationization level of the fresh cold chain, and the particle swarm optimization model includes: Inputting the key influencing factors into the dynamic prediction model for the informationization level of fresh cold chain to obtain a predicted value of the informationization level of fresh cold chain output by the dynamic prediction model for the informationization level of fresh cold chain; Inputting the maximum value of the key influencing factor and the predicted value of the fresh cold chain informationization level into the particle swarm optimization model, and obtaining the investment cost of optimizing the fresh cold chain informationization level and the corresponding time node output by the particle swarm optimization model; Among them, the key influencing factor is the particle swarm individual in the particle swarm algorithm model, the maximum value of the predicted value of the fresh cold chain informatization level is the target value of the global optimization in the particle swarm algorithm model; the constraint conditions of the key influencing factor are the input cost and the corresponding time node.

3. The method for adaptive optimization of the information level of the fresh cold chain according to claim 2 is characterized in that: The dynamic prediction model for the informationization level of the fresh cold chain includes: An input module, used for obtaining the key influencing factors; A feature extraction module, configured to extract key features based on the key influencing factors; A prediction module, configured to predict the level of informationization of the fresh produce cold chain based on the key features; The output module is used to output the predicted value of the fresh cold chain informationization level.

4. The method for adaptive optimization of the information level of the fresh cold chain according to claim 1 is characterized in that: The determining, based on the fresh cold chain multi-source data, the rate of change of the fresh cold chain informationization level at different time intervals includes: Determine the impact of each source of data on the level of informationization of the fresh cold chain; Based on the influence proportion of each source data on the information level of the fresh cold chain, the multi-source data of the fresh cold chain are weighted and summed to determine the change rate of the information level of the fresh cold chain at different time intervals.

5. The method for adaptive optimization of the information level of the fresh cold chain according to claim 1 is characterized in that: Determining the key influencing factors of the rate of change of the fresh cold chain informationization level based on the rate of change of the fresh cold chain informationization level at different time intervals includes: The rate of change of the informationization level of the fresh cold chain at different time intervals is input into the Monte Carlo simulation algorithm model to obtain the key influencing factors of the rate of change of the informationization level of the fresh cold chain output by the Monte Carlo simulation algorithm model.

6. The method for adaptive optimization of the information level of the fresh cold chain according to claim 1 is characterized in that: The associated characteristic dimensions include information investment level, information management level, information service level, information innovation level, information security level and information value-added level; The level of informatization investment includes the proportion of hardware facility investment, the frequency of software system updates, and the density of IT staffing; The information management level includes data standardization coverage, process digitization rate and abnormal event closed-loop processing rate; The information service level includes order response time, customer information traceability completeness and intelligent early warning accuracy; The information technology innovation level includes the patent technology conversion rate, new technology application coverage rate and R&D investment growth rate; The information security level includes data encryption transmission rate, system vulnerability repair timeliness and disaster recovery success rate; The level of information value-added includes the proportion of data service revenue, resource optimization and saving rate, and market forecast matching degree.

7. A device for adaptive optimization of the information level of fresh cold chain, characterized in that: include: A first determination module is configured to determine a coupling correlation factor between multiple correlation feature dimensions based on the informationization level of the fresh cold chain; An acquisition module, configured to acquire multi-source data of the fresh cold chain based on the coupling correlation factor; A second determination module is configured to determine the rate of change of the information level of the fresh produce cold chain at different time intervals based on the multi-source data of the fresh produce cold chain; A third determining module is configured to determine a key influencing factor of the rate of change of the fresh cold chain informationization level based on the rate of change of the fresh cold chain informationization level at different time intervals; The fourth determination module is used to determine the investment cost and corresponding time node for optimizing the information level of the fresh cold chain based on the key influencing factors, the dynamic prediction model of the information level of the fresh cold chain and the particle swarm algorithm model.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for adaptively optimizing the information level of the fresh cold chain as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for adaptively optimizing the information level of the fresh cold chain as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for adaptively optimizing the information level of the fresh cold chain as claimed in any one of claims 1 to 6 is implemented.

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