A Cold Chain Equipment Control Method and System

Abstract

The present invention relates to a cold chain equipment control method and system, belonging to the field of cold chain equipment control, including: obtaining historical cold chain transportation data records, extracting cold chain temperature parameters recorded when product quality changes occur, and extracting a set of historical temperature fluctuation parameters; randomly generating first cold chain control parameters, and performing equipment control prediction by combining environmental parameters and aging parameters to obtain a first cold chain temperature sequence; using the set of historical temperature fluctuation parameters to retrieve within the first cold chain temperature sequence, and combining with the fluctuation characteristic parameters of the first cold chain temperature sequence, calculating to obtain a first control fitness, and continuing to optimize the parameters to obtain the optimal cold chain control parameters for control. Through the present invention, the technical problem that the traditional method cannot dynamically adjust the cold chain control strategy according to the actual transportation scenario, resulting in large temperature fluctuations during transportation and affecting the quality of transported products can be solved; the adverse impact of temperature fluctuations on product quality can be effectively reduced, ensuring product safety and stable quality.

Description

Technical Field

[0001] The present invention relates to the field of cold chain equipment control, and particularly to a cold chain equipment control method and system. Background Art

[0002] Cold chain transportation refers to the process of controlling temperature, humidity, and other environmental factors during transportation, storage, and distribution to ensure that goods are maintained under specific temperature control conditions throughout the transportation cycle to preserve their quality and safety. Cold chain transportation is widely used for temperature-sensitive goods such as pharmaceuticals, food (especially fresh food and frozen food), and biological samples.

[0003] Traditional cold chain equipment usually operates based on preset temperature ranges and control parameters, lacking a flexible dynamic adjustment mechanism and unable to perform dynamic optimization according to real-time environmental changes, transportation duration, external conditions, and cargo characteristics, resulting in temperature fluctuations during transportation and thus affecting the quality of transported products. Summary of the Invention

[0004] In view of the technical problem that the traditional cold chain equipment control method cannot dynamically adjust the cold chain control strategy according to the actual transportation scenario, resulting in temperature fluctuations during transportation and thus affecting the quality of transported products, the present invention provides a cold chain equipment control method and system to solve this problem.

[0005] The technical solution of the present invention to solve the above technical problem is as follows:

[0006] In a first aspect, the present invention provides a cold chain equipment control method, including: during the operation of the cold chain equipment, obtaining the target product for cold chain transportation, obtaining the cold chain control parameter space of the cold chain equipment, and collecting the environmental parameters and aging parameters of the cold chain equipment operation; obtaining the historical cold chain transportation data record of the target product, extracting the cold chain temperature parameters recorded when product quality changes, and extracting the historical temperature fluctuation parameter set; randomly generating a first cold chain control parameter within the cold chain control parameter space, combining the environmental parameters and aging parameters to perform cold chain equipment control prediction, obtaining a first cold chain temperature sequence; using the historical temperature fluctuation parameter set to retrieve within the first cold chain temperature sequence, and combining the fluctuation characteristic parameters of the first cold chain temperature sequence to calculate and obtain a first control fitness, and continuing to optimize the cold chain control parameters to obtain the optimal cold chain control parameter, and controlling the cold chain equipment.

[0007] Second aspect, the present invention provides a cold chain equipment control system, including: a cold chain transportation information collection module, configured to obtain a target product undergoing cold chain transportation and a cold chain control parameter space of the cold chain equipment during the operation of the cold chain equipment, and collect environmental parameters and aging parameters of the cold chain equipment operation; a historical transportation data extraction module, configured to obtain historical cold chain transportation data records of the target product, extract cold chain temperature parameters recorded when product quality changes, and extract a set of historical temperature fluctuation parameters; a cold chain equipment control prediction module, configured to randomly generate a first cold chain control parameter within the cold chain control parameter space, combine the environmental parameters and aging parameters, perform cold chain equipment control prediction, and obtain a first cold chain temperature sequence; a cold chain control parameter optimization module, configured to retrieve within the first cold chain temperature sequence using the set of historical temperature fluctuation parameters, and combine with the fluctuation characteristic parameters of the first cold chain temperature sequence, calculate to obtain a first control fitness, continue to optimize the cold chain control parameters, obtain optimal cold chain control parameters, and control the cold chain equipment.

[0008] Third aspect, the present invention further provides an electronic device, including:

[0009] at least one processor; a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the steps of the method described in the first aspect above.

[0010] Fourth aspect, a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed, implements the steps of the method described in the first aspect above.

[0011] The beneficial effects of the present invention are as follows: During the operation of the cold chain equipment, the target product for cold chain transportation is obtained, and the cold chain control parameter space of the cold chain equipment is acquired. The environmental parameters and aging parameters during the operation of the cold chain equipment are collected. Then, the historical cold chain transportation data records of the target product are obtained, the cold chain temperature parameters recorded when product quality changes are extracted, and the historical temperature fluctuation parameter set is extracted. Then, the first cold chain control parameter is randomly generated within the cold chain control parameter space. Combining the environmental parameters and aging parameters, cold chain equipment control prediction is carried out to obtain the first cold chain temperature sequence. Further, the historical temperature fluctuation parameter set is used to retrieve within the first cold chain temperature sequence, and combined with the fluctuation characteristic parameters of the first cold chain temperature sequence, the first control fitness is calculated, and the cold chain control parameter is continuously optimized to obtain the optimal cold chain control parameter. Finally, the cold chain equipment is controlled according to the optimal cold chain control parameter. That is to say, through the above steps, the matching cold chain control parameters can be set through comprehensive analysis of product characteristic differences, external environment fluctuations, and equipment operation states, significantly improving the accuracy and stability of cold chain control, thereby effectively reducing the adverse effects of temperature fluctuations on product quality and ensuring the safety and quality stability of products during transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic flowchart of a cold chain equipment control method provided by the present invention;

[0013] Figure 2 It is a schematic structural diagram of a cold chain equipment control system provided by the present invention;

[0014] Figure 3 It is a schematic structural diagram of an electronic device provided by the present invention;

[0015] Figure 4 It is a schematic structural diagram of a computer-readable storage medium provided by the present invention.

[0016] In the drawings, the components represented by each reference numeral are described as follows:

[0017] Cold chain transportation information collection module 01, historical transportation data extraction module 02, cold chain equipment control prediction module 03, cold chain control parameter optimization module 04, electronic device 500, memory 510, processor 520, first computer program 511, computer-readable storage medium 600, second computer program 611. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not described in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0021] Embodiment 1, as Figure 1 shown, the embodiment of the present invention provides a cold chain equipment control method, which specifically includes the following steps:

[0022] S100: During the operation of the cold chain equipment, obtain the target product for cold chain transportation, and obtain the cold chain control parameter space of the cold chain equipment, and collect the environmental parameters and aging parameters of the cold chain equipment operation.

[0023] Further, step S100 of the present invention further includes:

[0024] S110: During the operation of the cold chain equipment, obtain the product category for cold chain transportation as the target product; S120: Obtain the cold chain control parameter space for control adjustment of the cold chain equipment; S130: Collect the temperature parameter and humidity parameter of the current external environment as the environmental parameters, and collect the cumulative operation time of the cold chain equipment as the aging parameter.

[0025] Specifically, first, during cold chain transportation, clarify the target product categories for transportation, such as drugs, food, vaccines, etc. These products have different temperature and humidity requirements. Therefore, different temperature control strategies need to be set according to the characteristics of the products. For example, vaccines, insulin, etc. are extremely sensitive to temperature changes and need to be maintained within a narrow temperature control range; fresh and frozen foods generally require transportation at a lower temperature to avoid deterioration caused by temperature fluctuations; blood samples, cells, etc. require specific temperature and humidity conditions.

[0026] Next, obtain the cold chain control parameter space for controlling and adjusting the cold chain equipment. The cold chain control parameters include various parameters that affect the temperature adjustment of the equipment, such as compressor control parameters (including operating frequency, power, operating mode, etc.); the cold chain control parameter space refers to a set containing multiple cold chain control parameters. By adjusting these parameters, the dynamic optimization of the performance of the cold chain equipment can be achieved, thereby ensuring temperature control accuracy and stable product quality.

[0027] Then, collect the temperature parameters and humidity parameters of the current external environment. Among them, the change in ambient temperature will directly affect the cooling efficiency of the equipment. Especially when encountering high-temperature weather during transportation, the cold chain equipment needs to make adaptive adjustments according to the external environment; humidity has an important impact on some specific products (such as biological samples). Too high or too low humidity may have a negative impact on product quality. Therefore, it is necessary to monitor the environmental humidity and adjust the humidity control function of the equipment; and set the temperature parameters and humidity parameters as environmental parameters.

[0028] On the other hand, collect the cumulative operating time of the cold chain equipment and use the cumulative operating time as an aging parameter. Among them, as the equipment ages, the refrigeration efficiency may decline, which may lead to inaccurate temperature control. Therefore, it is necessary to consider the aging degree of the equipment and regularly evaluate the equipment performance. By obtaining the target product category, environmental parameters, and equipment aging parameters, it provides data support for the subsequent dynamic adjustment analysis of cold chain control parameters.

[0029] S200: Obtain the historical cold chain transportation data records of the target product, extract the cold chain temperature parameters recorded when product quality changes occur, and extract the historical temperature fluctuation parameter set.

[0030] Furthermore, step S200 of the present invention further includes:

[0031] S210: Obtain the historical cold chain transportation data records of the same type of products as the target product, screen the abnormal cold chain temperature parameter sequences and product quality change parameters recorded when the target product has quality changes, and obtain the abnormal cold chain temperature parameter sequence set and the product quality change parameter set; S220: Extract the maximum temperature fluctuation amplitude in multiple abnormal cold chain temperature parameter sequences in the abnormal cold chain temperature parameter sequence set to obtain the abnormal temperature fluctuation parameter set.

[0032] Specifically, first, collect the historical cold chain transportation data records of similar products of the target product during cold chain transportation, including cold chain temperature data, product quality data, etc.; then, in the historical cold chain transportation data records, screen the abnormal cold chain temperature parameter sequences and product quality change parameters recorded when the quality of the target product changes (for example, at least one product is damaged such as spoilage), and the abnormal cold chain temperature parameter sequence contains temperature parameters at multiple consecutive timestamps; the product quality change parameter refers to the damage rate of the product, such as the damage rate of fresh chilled food is 3%; obtain the set of abnormal cold chain temperature parameter sequences and the set of product quality change parameters, where the abnormal cold chain temperature parameter sequences and the product quality change parameters correspond one by one.

[0033] Next, calculate the maximum temperature fluctuation amplitude in multiple abnormal cold chain temperature parameter sequences in the set of abnormal cold chain temperature parameter sequences respectively. The maximum temperature fluctuation amplitude refers to the temperature difference between the maximum temperature and the minimum temperature in each abnormal cold chain temperature parameter sequence, and set the maximum temperature fluctuation amplitude as the abnormal temperature floating parameter. For example, in a certain period of time, the temperature records are 4 degrees Celsius, 6 degrees Celsius, 3 degrees Celsius, 5 degrees Celsius and 7 degrees Celsius. Then the maximum temperature is 7 degrees Celsius, the minimum temperature is 3 degrees Celsius, and the maximum temperature fluctuation amplitude is 7 minus 3 equals 4 degrees Celsius. Then the abnormal temperature floating parameter is 4 degrees Celsius, and obtain the set of abnormal temperature floating parameters, where the abnormal temperature floating parameters and the abnormal cold chain temperature parameter sequences correspond one by one.

[0034] S300: Randomly generate a first cold chain control parameter in the cold chain control parameter space, combine the environmental parameter and the aging parameter, perform cold chain equipment control prediction, and obtain a first cold chain temperature sequence.

[0035] Furthermore, step S300 of the present invention further includes:

[0036] S310: According to the historical operation data of the cold chain equipment, pre-train the cold chain equipment control prediction path in advance, where the cold chain equipment control prediction path includes multiple cold chain equipment control prediction branches.

[0037] Furthermore, step S310 of the present invention further includes:

[0038] S311: According to the historical operation data of cold chain equipment of the same model, collect the sample cold chain control parameter set, sample environmental parameter set, sample aging parameter set, and collect the control temperature of the cold chain equipment, and label to obtain the sample cold chain temperature set; S312: Combine the sample cold chain control parameter set, sample environmental parameter set, sample aging parameter set and sample cold chain temperature set as cold chain supervision training data, and perform K-fold partitioning to obtain K cold chain supervision training data sets, where K is an integer greater than 1; S313: Respectively use the K cold chain supervision training data sets, adopt machine learning, train K cold chain equipment control prediction branches, and combine them after training to obtain the cold chain equipment control prediction path.

[0039] Specifically, first, according to the historical operation data of cold chain equipment of the same model, collect the sample cold chain control parameter (compressor power, operating frequency, etc.) set, sample environmental parameter (ambient temperature, ambient humidity) set, sample aging parameter (cumulative operating time) set, and collect the control temperature of the cold chain equipment under different sample cold chain control parameters, sample environmental parameters and sample aging parameters, and label it as the sample cold chain temperature to obtain the sample cold chain temperature set. Then combine the sample cold chain control parameter set, sample environmental parameter set, sample aging parameter set and sample cold chain temperature set as cold chain supervision training data, and perform K-fold partitioning on the cold chain supervision training data, that is, randomly divide the above cold chain supervision training data set into K non-overlapping subsets (each subset contains different sample data), where K is an integer greater than 1, and the value of K can be set according to actual needs, such as setting K to 10, to obtain K cold chain supervision training data sets.

[0040] Construct K control prediction branches for cold chain equipment based on the BP network. Among them, the cold chain equipment control prediction branch is a BP neural network model in machine learning that can be iteratively optimized. It is used to input cold chain equipment control parameters, environmental conditions, and aging parameters and output the predicted cold chain temperature. It includes an input layer, multiple hidden layers, and an output layer. The input layer is used to receive various input data of the cold chain equipment, including cold chain control parameters, environmental parameters, and aging parameters. The output layer is responsible for generating the prediction result, and the output data is the cold chain temperature. Then, using the sample cold chain control parameters, sample environmental parameters, and sample aging parameters as inputs and the sample cold chain temperature as supervision, the K cold chain equipment control prediction branches are respectively supervised and trained using the K cold chain supervision training data sets. First, the input data (cold chain control parameters, environmental parameters, aging parameters) is passed into the network, and after calculations in the input layer and hidden layers, the output result is finally obtained. Then, according to the difference between the prediction result and the actual result (real cold chain temperature data or target temperature), the error (such as the mean square error MSE) is calculated. Further, the error is used to calculate the weight update of each layer through the backpropagation algorithm, and the weight value is adjusted to reduce the prediction error. The purpose of backpropagation is to optimize the model parameters through multiple iterations to minimize the prediction error of the model. Finally, the gradient descent method or its variant (such as the Adam optimizer) is used to update the network parameters to minimize the loss function, and the iterative optimization continues until the loss function converges, obtaining the K trained cold chain equipment control prediction branches.

[0041] Finally, the K cold chain equipment control prediction branches are combined in parallel to construct a cold chain equipment control prediction path. By using the ensemble learning method of multiple BP neural network branches to construct the cold chain equipment control prediction path, since the training data of each cold chain equipment control prediction branch is different, multiple predictions can be made to obtain multiple predicted temperature results, so that the temperature fluctuations in different situations can be predicted more accurately.

[0042] S320: Randomly generate the first cold chain control parameter within the cold chain control parameter space; S330: Input the first cold chain control parameter, environmental parameter, and aging parameter into multiple cold chain equipment control prediction branches within the cold chain equipment control prediction path for cold chain equipment control prediction, and output to obtain multiple first cold chain temperatures; S340: Construct the first cold chain temperature sequence according to the multiple first cold chain temperatures.

[0043] Specifically, first, randomly select any cold chain control parameter in the cold chain control parameter space and set it as the first cold chain control parameter; then input the first cold chain control parameter, environmental parameters, and aging parameters into multiple cold chain equipment control prediction branches within the cold chain equipment control prediction path to perform cold chain equipment control prediction and output multiple first cold chain temperatures. Among them, each BP neural network branch performs forward propagation, combines the input control parameters, environmental conditions, and equipment aging status to perform temperature prediction, and each branch outputs a predicted temperature (the first cold chain temperature). By integrating the prediction results of multiple branches, it is possible to better consider the equipment operation status and environmental changes and predict the temperature fluctuations of cold chain equipment in different scenarios.

[0044] Finally, arrange the multiple first cold chain temperatures to construct a first cold chain temperature sequence, which represents the fluctuation range of the cold chain temperature under the first cold chain control parameter, environmental factors, and equipment aging status. For example, the temperature sequence that may be obtained by the first cold chain control parameter under multiple predictions may be: [4.5 degrees Celsius, 4.6 degrees Celsius, 4.4 degrees Celsius]. These values represent various possibilities of the cold chain temperature affected by factors such as the environment and equipment aging under the same control strategy.

[0045] S400: Use the historical temperature floating parameter set to retrieve within the first cold chain temperature sequence, and combine the fluctuation characteristic parameters of the first cold chain temperature sequence to calculate and obtain the first control fitness, continue to optimize the cold chain control parameters, obtain the optimal cold chain control parameter, and control the cold chain equipment.

[0046] Furthermore, step S400 of the present invention further includes:

[0047] S410: Calculate and obtain the first maximum cold chain temperature fluctuation amplitude within the first cold chain temperature sequence; S420: Traverse and retrieve whether multiple historical temperature floating parameters within the historical temperature floating parameter set fall within the first maximum cold chain temperature fluctuation amplitude to obtain a floating retrieval result set, where the floating retrieval result is 1 or 0, falling within is 1, and not falling within is 0.

[0048] Specifically, first, calculate and obtain the first maximum cold chain temperature fluctuation amplitude within the first cold chain temperature sequence. The first maximum cold chain temperature fluctuation amplitude is the temperature difference between the maximum temperature and the minimum temperature in the first cold chain temperature sequence. Then, traverse and retrieve whether multiple historical temperature fluctuation parameters in the historical temperature fluctuation parameter set fall within the first maximum cold chain temperature fluctuation amplitude. Among them, if the historical temperature fluctuation amplitude is less than or equal to the first maximum cold chain temperature fluctuation amplitude, record it as "1", indicating that the historical temperature fluctuation parameter falls within the first cold chain temperature fluctuation range; if the historical temperature fluctuation amplitude is greater than the first maximum cold chain temperature fluctuation amplitude, record it as "0", indicating that the historical temperature fluctuation parameter does not fall within the first cold chain temperature fluctuation range, thus obtaining a floating retrieval result set. By analyzing the floating retrieval result set, it is possible to evaluate whether the temperature fluctuation under the first cold chain control parameter conforms to the temperature control performance in historical data. If most historical temperature fluctuation parameters fall within the first cold chain temperature fluctuation amplitude range, that is, most retrieval results are "1", it indicates that the current control strategy may lead to excessive temperature fluctuations and the control parameters need to be further optimized; if most retrieval results are "0", it means that the temperature fluctuation under this control strategy is reasonable.

[0049] S430: Analyze and obtain the fluctuation characteristic parameters of the first cold chain temperature sequence, and calculate and obtain the first control fitness of the first cold chain control parameter in combination with the floating retrieval result set and the product quality change parameter set.

[0050] Furthermore, step S430 of the present invention further includes:

[0051] S431: Randomly select multiple groups of first cold chain temperatures within the first cold chain temperature sequence, where each group of first cold chain temperatures includes two first cold chain temperatures; S432: Calculate the temperature fluctuation amplitudes of the multiple groups of first cold chain temperatures to obtain multiple temperature fluctuation amplitudes, and calculate the mean value to obtain the first average temperature fluctuation amplitude; S433: Calculate and obtain the first control fitness of the first cold chain control parameter according to the first average temperature fluctuation amplitude, the floating retrieval result set, and the product quality change parameter set, as shown in the following formula: ; where CTFIT is the control fitness, is the average temperature fluctuation amplitude, M is the number of floating retrieval results in the floating retrieval result set, is the i-th floating retrieval result, and each floating retrieval result is 1 or 0, is the product quality change parameter corresponding to the i-th floating retrieval result.

[0052] Specifically, first, any two first cold-chain temperatures are randomly selected from the first cold-chain temperature sequence and set as a group of cold-chain temperatures. This is done randomly multiple times to obtain multiple groups of first cold-chain temperatures. The number of selections can be set according to the calculation accuracy. The more selections are made, the higher the calculation accuracy. Then, the temperature fluctuation amplitudes of the multiple groups of first cold-chain temperatures are calculated respectively. The temperature fluctuation amplitude is the absolute value of the temperature difference between two first cold-chain temperatures, resulting in multiple temperature fluctuation amplitudes. Further, the average value of the multiple temperature fluctuation amplitudes is calculated to obtain the first average temperature fluctuation amplitude.

[0053] Construct a control fitness evaluation function: ; In the control fitness evaluation function, CTFIT is the control fitness. The larger the control fitness, the more stable the temperature control effect, and the more conducive it is to maintaining the product quality; is the average temperature fluctuation amplitude. The average temperature fluctuation amplitude is negatively correlated with the control fitness, that is, the larger the average temperature fluctuation amplitude, the smaller the control fitness, and the smaller the average temperature fluctuation amplitude, the smaller the control fitness; M is the number of floating search results in the floating search result set, is the i-th floating search result, and each floating search result is 1 or 0. Among them, the more "1"s in the floating search results, the smaller the control fitness, and the more "0"s, the larger the control fitness; is the product quality change parameter corresponding to the i-th floating search result. The larger the product quality change parameter, the smaller the control fitness, and vice versa, the smaller the product quality change parameter, the larger the control fitness. By constructing the control fitness evaluation function, the influence of each cold-chain control parameter on the temperature control stability can be quantified, the control fitness of each cold-chain control parameter can be accurately evaluated, and the advantages and disadvantages of the cold-chain control parameters can be intuitively reflected.

[0054] Furthermore, using the fitness evaluation function, based on the first average temperature fluctuation amplitude, the floating search result set, and the product quality change parameter set, the first control fitness of the first cold-chain control parameter is calculated and obtained.

[0055] S440: Continue to optimize the cold-chain control parameters in the cold-chain control parameter space until convergence, and output the cold-chain control parameter with the largest control fitness to obtain the optimal cold-chain control parameter.

[0056] Specifically, continue to randomly select a second cold chain control parameter within the cold chain control parameter space, and calculate the second control fitness of the second cold chain control parameter; continue to perform iterative selection and fitness evaluation of cold chain control parameters within the cold chain control parameter space using the same method until a predetermined number of selections is reached (which can be set according to the optimization accuracy, the more selections, the higher the optimization accuracy), output multiple cold chain control parameters and multiple control fitnesses; then output the cold chain control parameter with the maximum control fitness as the optimal cold chain control parameter, and control the cold chain equipment according to the optimal cold chain control parameter.

[0057] The cold chain equipment control method provided by the embodiment of the present invention has at least the following technical effects:

[0058] During the operation of the cold chain equipment, obtain the target product for cold chain transportation, and obtain the cold chain control parameter space of the cold chain equipment, and collect the environmental parameters and aging parameters of the cold chain equipment operation; then obtain the historical cold chain transportation data record of the target product, extract the cold chain temperature parameters recorded when the product quality changes, and extract the historical temperature fluctuation parameter set; then randomly generate a first cold chain control parameter within the cold chain control parameter space, combine the environmental parameters and aging parameters, perform cold chain equipment control prediction, and obtain a first cold chain temperature sequence; further use the historical temperature fluctuation parameter set to retrieve within the first cold chain temperature sequence, and combine the fluctuation characteristic parameters of the first cold chain temperature sequence, calculate to obtain a first control fitness, and continue to optimize the cold chain control parameter to obtain the optimal cold chain control parameter; finally, control the cold chain equipment according to the optimal cold chain control parameter; that is to say, through the above steps, the matching cold chain control parameters can be set after comprehensive analysis of product characteristic differences, external environment fluctuations and equipment operation states, significantly improving the accuracy and stability of cold chain control, thereby effectively reducing the adverse effects of temperature fluctuations on product quality and ensuring the safety and quality stability of products during transportation.

[0059] Embodiment 2, as Figure 2 shown, based on the same inventive concept as the cold chain equipment control method provided in Embodiment 1, the embodiment of the present invention also provides a cold chain equipment control system, including:

[0060] The cold-chain transportation information collection module 01 is used to obtain the target products undergoing cold-chain transportation and the cold-chain control parameter space of the cold-chain equipment during the operation of the cold-chain equipment, and collect the environmental parameters and aging parameters of the cold-chain equipment operation; the historical transportation data extraction module 02 is used to obtain the historical cold-chain transportation data records of the target products, extract the cold-chain temperature parameters recorded when product quality changes, and extract the historical temperature fluctuation parameter set; the cold-chain equipment control prediction module 03 is used to randomly generate the first cold-chain control parameter within the cold-chain control parameter space, combine the environmental parameters and aging parameters, perform cold-chain equipment control prediction, and obtain the first cold-chain temperature sequence; the cold-chain control parameter optimization module 04 is used to retrieve within the first cold-chain temperature sequence using the historical temperature fluctuation parameter set, and combine with the fluctuation characteristic parameters of the first cold-chain temperature sequence, calculate and obtain the first control fitness, continue to optimize the cold-chain control parameters, obtain the optimal cold-chain control parameters, and control the cold-chain equipment.

[0061] Further, the cold-chain equipment control system is also used to: during the operation of the cold-chain equipment, obtain the product category undergoing cold-chain transportation as the target product; obtain the cold-chain control parameter space for the cold-chain equipment to perform control adjustment; collect the temperature parameter and humidity parameter of the current external environment as the environmental parameters, and collect the cumulative operation time of the cold-chain equipment as the aging parameter.

[0062] Further, the cold-chain equipment control system is also used to: obtain the historical cold-chain transportation data records of the same-category products of the target product, screen the abnormal cold-chain temperature parameter sequence and product quality change parameters recorded when the target product has quality changes, and obtain the abnormal cold-chain temperature parameter sequence set and the product quality change parameter set; extract the maximum temperature fluctuation amplitude among multiple abnormal cold-chain temperature parameter sequences within the abnormal cold-chain temperature parameter sequence set to obtain the abnormal temperature fluctuation parameter set.

[0063] Further, the cold-chain equipment control system is also used to: pre-train the cold-chain equipment control prediction path according to the historical operation data of the cold-chain equipment, where the cold-chain equipment control prediction path includes multiple cold-chain equipment control prediction branches; randomly generate the first cold-chain control parameter within the cold-chain control parameter space; input the first cold-chain control parameter, environmental parameters, and aging parameters into multiple cold-chain equipment control prediction branches within the cold-chain equipment control prediction path for cold-chain equipment control prediction, and output and obtain multiple first cold-chain temperatures; construct the first cold-chain temperature sequence according to the multiple first cold-chain temperatures.

[0064] Further, the cold chain equipment control system is further configured to: collect a sample cold chain control parameter set, a sample environmental parameter set, and a sample aging parameter set according to the historical operation data of cold chain equipment of the same model, and collect the control temperature of the cold chain equipment, and label to obtain a sample cold chain temperature set; combine the sample cold chain control parameter set, the sample environmental parameter set, the sample aging parameter set, and the sample cold chain temperature set as cold chain supervision training data, and perform K-fold partitioning to obtain K cold chain supervision training data sets, where K is an integer greater than 1; respectively use the K cold chain supervision training data sets and machine learning to train K cold chain equipment control prediction branches, and combine them after training to obtain a cold chain equipment control prediction path.

[0065] Further, the cold chain equipment control system is further configured to: calculate and obtain the first maximum cold chain temperature fluctuation amplitude in the first cold chain temperature sequence; traverse and retrieve whether multiple historical temperature fluctuation parameters in the historical temperature fluctuation parameter set fall into the first maximum cold chain temperature fluctuation amplitude to obtain a floating retrieval result set, where the floating retrieval result is 1 or 0, falling into it is 1, and not falling into it is 0; analyze and obtain the fluctuation characteristic parameters of the first cold chain temperature sequence, and combine the floating retrieval result set and the product quality change parameter set to calculate and obtain the first control fitness of the first cold chain control parameter; continue to optimize the cold chain control parameter in the cold chain control parameter space until convergence, output the cold chain control parameter with the largest control fitness, and obtain the optimal cold chain control parameter.

[0066] Further, the cold chain equipment control system is further configured to: randomly select multiple groups of first cold chain temperatures in the first cold chain temperature sequence, where each group of first cold chain temperatures includes two first cold chain temperatures; calculate the temperature fluctuation amplitudes of the multiple groups of first cold chain temperatures to obtain multiple temperature fluctuation amplitudes, and calculate the mean value to obtain the first average temperature fluctuation amplitude; calculate and obtain the first control fitness of the first cold chain control parameter according to the first average temperature fluctuation amplitude, the floating retrieval result set, and the product quality change parameter set, as shown in the following formula: ; where CTFIT is the control fitness, is the average temperature fluctuation amplitude, M is the number of floating retrieval results in the floating retrieval result set, is the i-th floating retrieval result, and each floating retrieval result is 1 or 0, is the product quality change parameter corresponding to the i-th floating retrieval result.

[0067] Embodiment 3, please refer to Figure 3 , Figure 3 is a schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. As Figure 3As shown in the figure, an embodiment of the present invention provides an electronic device 500, including a memory 510, a processor 520, and a first computer program 511 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the first computer program 511, the following steps are implemented: During the operation of the cold chain device, obtain the target product undergoing cold chain transportation, and obtain the cold chain control parameter space of the cold chain device, and collect the environmental parameters and aging parameters of the cold chain device operation; obtain the historical cold chain transportation data record of the target product, extract the cold chain temperature parameters recorded when the product quality changes, and extract the historical temperature fluctuation parameter set; randomly generate a first cold chain control parameter within the cold chain control parameter space, combine the environmental parameters and aging parameters, perform cold chain device control prediction, and obtain a first cold chain temperature sequence; use the historical temperature fluctuation parameter set to retrieve within the first cold chain temperature sequence, and combine the fluctuation characteristic parameters of the first cold chain temperature sequence, calculate and obtain a first control fitness, continue to optimize the cold chain control parameters, obtain the optimal cold chain control parameters, and control the cold chain device.

[0068] Embodiment 4, please refer to Figure 4 , Figure 4 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. As Figure 4 shown in the figure, this embodiment provides a computer-readable storage medium 600, on which a second computer program 611 is stored. When the second computer program 611 is executed by a processor, the following steps are implemented: During the operation of the cold chain device, obtain the target product undergoing cold chain transportation, and obtain the cold chain control parameter space of the cold chain device, and collect the environmental parameters and aging parameters of the cold chain device operation; obtain the historical cold chain transportation data record of the target product, extract the cold chain temperature parameters recorded when the product quality changes, and extract the historical temperature fluctuation parameter set; randomly generate a first cold chain control parameter within the cold chain control parameter space, combine the environmental parameters and aging parameters, perform cold chain device control prediction, and obtain a first cold chain temperature sequence; use the historical temperature fluctuation parameter set to retrieve within the first cold chain temperature sequence, and combine the fluctuation characteristic parameters of the first cold chain temperature sequence, calculate and obtain a first control fitness, continue to optimize the cold chain control parameters, obtain the optimal cold chain control parameters, and control the cold chain device.

[0069] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0070] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0071] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that realizes the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0073] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0074] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept.

[0075] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A cold chain equipment control method, characterized in that: Methods include: During the operation of the cold chain equipment, the target products for cold chain transportation are obtained, the cold chain control parameter space of the cold chain equipment is obtained, and the environmental parameters and aging parameters of the cold chain equipment operation are collected; Obtain historical cold chain transportation data records of the target product, extract cold chain temperature parameters recorded when product quality changes occur, and extract a set of historical temperature floating parameters; Randomly generate a first cold chain control parameter in the cold chain control parameter space, perform cold chain equipment control prediction in combination with the environmental parameter and the aging parameter, and obtain a first cold chain temperature sequence; The historical temperature floating parameter set is used to search in the first cold chain temperature sequence, and combined with the fluctuation characteristic parameters of the first cold chain temperature sequence, a first control fitness is calculated, and the cold chain control parameters are continuously optimized to obtain the optimal cold chain control parameters, and the cold chain equipment is controlled, including: Calculate and obtain a first maximum cold chain temperature fluctuation amplitude in the first cold chain temperature sequence; Traverse and search whether multiple historical temperature floating parameters in the historical temperature floating parameter set fall within the first maximum cold chain temperature fluctuation amplitude, and obtain a floating search result set, wherein the floating search result is 1 or 0, and the value is 1 if it falls within the range, and 0 if it does not fall within the range; Analyzing and obtaining the fluctuation characteristic parameters of the first cold chain temperature sequence, combining the floating search result set and the product quality change parameter set, and calculating and obtaining the first control fitness of the first cold chain control parameter, including: Randomly selecting multiple groups of first cold chain temperatures within the first cold chain temperature sequence, wherein each group of first cold chain temperatures includes two first cold chain temperatures; Calculating the temperature fluctuation amplitudes of the multiple groups of first cold chain temperatures to obtain multiple temperature fluctuation amplitudes, and calculating the average to obtain a first average temperature fluctuation amplitude; According to the first average temperature fluctuation amplitude, the floating search result set and the product quality change parameter set, the first control fitness of the first cold chain control parameter is calculated and obtained as follows: ； Among them, CTFIT is the control fitness, is the average temperature fluctuation amplitude, M is the number of floating search results in the floating search result set, is the i-th floating search result, each floating search result is 1 or 0, is the product quality change parameter corresponding to the i-th floating retrieval result; Continue to optimize the cold chain control parameters in the cold chain control parameter space until convergence, output the cold chain control parameters with the maximum control fitness, and obtain the optimal cold chain control parameters; During the operation of the cold chain equipment, the historical cold chain transportation data records of the target product are obtained, the cold chain temperature parameters when the product quality changes, and the historical temperature floating parameter set are extracted, including: Obtain historical cold chain transportation data records of similar products of the target product, screen abnormal cold chain temperature parameter sequences and product quality change parameters recorded when the target product undergoes quality changes, and obtain abnormal cold chain temperature parameter sequence sets and product quality change parameter sets; The maximum temperature fluctuation amplitude among multiple abnormal cold chain temperature parameter sequences in the abnormal cold chain temperature parameter sequence set is extracted to obtain an abnormal temperature floating parameter set.

2. The cold chain equipment control method according to claim 1, characterized in that: During the operation of the cold chain equipment, the target products for cold chain transportation are obtained, the cold chain control parameter space of the cold chain equipment is obtained, and the environmental parameters and aging parameters of the cold chain equipment operation are collected, including: During the operation of the cold chain equipment, the product categories for cold chain transportation are obtained as target products; Obtaining a cold chain control parameter space for controlling and adjusting the cold chain equipment; The temperature parameters and humidity parameters of the current external environment are collected as environmental parameters, and the accumulated operating time of the cold chain equipment is collected as aging parameters.

3. The cold chain equipment control method according to claim 1, characterized in that: Randomly generating a first cold chain control parameter in the cold chain control parameter space, combining the environmental parameter and the aging parameter, performing cold chain equipment control prediction, and obtaining a first cold chain temperature sequence, including: Pre-training a cold chain equipment control prediction path according to historical operation data of the cold chain equipment, wherein the cold chain equipment control prediction path includes a plurality of cold chain equipment control prediction branches; Randomly generating a first cold chain control parameter in the cold chain control parameter space; Inputting the first cold chain control parameter, environmental parameter and aging parameter into a plurality of cold chain equipment control prediction branches in the cold chain equipment control prediction path, performing cold chain equipment control prediction, and outputting a plurality of first cold chain temperatures; A first cold chain temperature sequence is constructed based on the multiple first cold chain temperatures.

4. The cold chain equipment control method according to claim 3, characterized in that: Based on the historical operation data of cold chain equipment, the cold chain equipment control prediction path is pre-trained, including: According to the historical operation data of the same type of cold chain equipment, collect the sample cold chain control parameter set, sample environmental parameter set, sample aging parameter set, and collect the control temperature of the cold chain equipment, and mark the sample cold chain temperature set; The sample cold chain control parameter set, the sample environment parameter set, the sample aging parameter set and the sample cold chain temperature set are combined as cold chain supervision training data, and K-fold division is performed to obtain K cold chain supervision training data sets, where K is an integer greater than 1; The K cold chain supervision training data sets are respectively used to train K cold chain equipment control prediction branches using machine learning. After the training is completed, the cold chain equipment control prediction path is obtained by combining them.

5. A cold chain equipment control system, characterized in that: The steps for implementing a cold chain equipment control method according to any one of claims 1 to 4 include: The cold chain transportation information collection module is used to obtain the target products for cold chain transportation during the operation of the cold chain equipment, obtain the cold chain control parameter space of the cold chain equipment, and collect the environmental parameters and aging parameters of the cold chain equipment operation; A historical transportation data extraction module is used to obtain the historical cold chain transportation data records of the target product, extract the cold chain temperature parameters recorded when the product quality changes, and extract the historical temperature floating parameter set; A cold chain equipment control prediction module, used to randomly generate a first cold chain control parameter in the cold chain control parameter space, and perform cold chain equipment control prediction in combination with the environmental parameter and the aging parameter to obtain a first cold chain temperature sequence; The cold chain control parameter optimization module is used to use the historical temperature floating parameter set to search within the first cold chain temperature sequence, and combine the fluctuation characteristic parameters of the first cold chain temperature sequence to calculate and obtain the first control fitness, continue to optimize the cold chain control parameters, obtain the optimal cold chain control parameters, and control the cold chain equipment.

6. An electronic device, characterized in that: include: Memory for storing computer software programs; A processor is used to read and execute the computer software program, thereby implementing the steps of a cold chain equipment control method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer software program, which, when executed by a processor, implements the steps of a cold chain equipment control method as described in any one of claims 1 to 4.

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