A precise control method and system for cold chain equipment

Abstract

The present invention discloses a precision control method and system for cold chain equipment, which belongs to the field of intelligent control, wherein the method includes: obtaining the type of target transported product and determining the product-temperature sensitivity; outputting the cold chain transport process, and monitoring the temperature of each cold chain transport node to obtain multiple storage temperature data; monitoring product circulation to obtain multiple circulation temperature data and multiple circulation durations; outputting multiple circulation loss densities; using product-temperature sensitivity as input quantification, and using multiple circulation loss densities and multiple circulation durations as input to train product failure prediction functions to obtain multiple circulation failure indicators; performing failure fusion, outputting a first fused failure indicator, and generating a first cold chain reminder message. This application solves the technical problem that the existing cold chain equipment monitoring and control are independent, resulting in insufficient accuracy and comprehensiveness of cold chain equipment control, and achieves the technical effect of improving the accuracy and comprehensiveness of cold chain equipment monitoring and control.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control, and in particular to a precise control method and system for cold chain equipment. Background Art

[0002] With the development of social economy, people have higher and higher requirements for food safety. For many perishable cold chain foods, such as fresh meat and quick-frozen foods, a strict cold chain system must be established to ensure the quality of the entire process from production to the table. However, in the existing cold chain transportation and storage process, each cold chain transportation node often operates independently, resulting in inaccurate monitoring of the entire cold chain circulation process, inaccurate and incomplete temperature control, and other problems, which leads to quality problems of cold chain products during transportation. Summary of the invention

[0003] The present application provides a precise control method and system for cold chain equipment, aiming to solve the technical problem that the existing cold chain equipment monitoring and control are independent, resulting in insufficient accuracy and comprehensiveness of cold chain equipment control.

[0004] In view of the above problems, the present application provides a method and system for precise control of cold chain equipment.

[0005] The first aspect disclosed in the present application provides a method for precise control of cold chain equipment, the method comprising: obtaining the type of target transport product, and determining the product-temperature sensitivity according to the type of target transport product; outputting the cold chain transport process according to the type of target transport product, and monitoring the temperature of each cold chain transport node to obtain multiple storage temperature data, wherein the multiple storage temperature data are obtained through multiple temperature sensing units, and the multiple temperature sensing units are integrated; by monitoring the product circulation between two adjacent cold chain transport nodes in each cold chain transport node, multiple circulation temperature data and multiple circulation durations are obtained; according to the multiple circulation temperature data and the storage temperature data of two adjacent cold chain transport nodes, multiple circulation loss densities are output; using the product-temperature sensitivity as the input quantity, and using the multiple circulation loss densities and the multiple circulation durations as input variables to train the product failure prediction function to obtain multiple circulation failure indicators; performing failure fusion according to the multiple circulation failure indicators, outputting a first fused failure indicator, and generating a first cold chain reminder information according to the first fused failure indicator.

[0006] Another aspect disclosed in the present application provides a precision control system for cold chain equipment, the system comprising: a product type acquisition unit, for acquiring the type of a target transport product, and determining the product-temperature sensitivity according to the type of the target transport product; a node temperature monitoring unit, for outputting a cold chain transport process according to the type of the target transport product, and monitoring the temperature of each cold chain transport node to acquire a plurality of storage temperature data, wherein the plurality of storage temperature data are acquired through a plurality of temperature sensing units, and the plurality of temperature sensing units are integrated; a product circulation monitoring unit, for acquiring a plurality of circulation temperature data and a plurality of circulation durations by monitoring the product circulation between two adjacent cold chain transport nodes in each cold chain transport node; a circulation loss density unit, for outputting a plurality of circulation loss densities according to a plurality of circulation temperature data and the storage temperature data of two adjacent cold chain transport nodes; a circulation failure index unit, for training a product failure prediction function with product-temperature sensitivity as an input quantity, and with a plurality of circulation loss densities and a plurality of circulation durations as input variables to obtain a plurality of circulation failure indexes; a cold chain reminder information unit, for performing failure fusion according to a plurality of circulation failure indexes, outputting a first fused failure index, and generating a first cold chain reminder information according to the first fused failure index.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The method of obtaining the type of the target transport product is adopted, and the product-temperature sensitivity is determined according to the product type, so that the cold chain can be evaluated according to the sensitivity of different products; the corresponding cold chain transportation process is output according to the product type, and a temperature sensing module is set at each cold chain transportation node in the process to monitor the node temperature in real time and obtain the storage temperature data to monitor the temperature change of the whole process; the flow process of products between adjacent nodes is monitored, and the temperature data and duration data during the circulation are obtained to reflect the temperature change of the product during transportation; the temperature loss density of the flow process between nodes is calculated, and the impact of the flow process on product quality is evaluated in combination with the node storage temperature; the product failure prediction model is trained using data such as product sensitivity to obtain the failure index of each section of the flow process; the failure index of each section of the flow process is combined to obtain the first fusion failure index of the overall cold chain, and the corresponding cold chain reminder is generated to realize the technical solution of refined control of cold chain equipment, solve the technical problem that the existing cold chain equipment monitoring and control are independent, resulting in insufficient accuracy and comprehensiveness of cold chain equipment control, and achieve the technical effect of improving the accuracy and comprehensiveness of cold chain equipment monitoring and control.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a method for accurately controlling cold chain equipment is provided for the embodiment of the present application;

[0011] Figure 2 A schematic flow chart of obtaining multiple circulation loss densities in a precise control method of cold chain equipment is provided for an embodiment of the present application;

[0012] Figure 3 A structural schematic diagram of a precision control system for cold chain equipment is provided for an embodiment of the present application.

[0013] Explanation of the accompanying drawings: product type acquisition unit 11, node temperature monitoring unit 12, product circulation monitoring unit 13, circulation loss density unit 14, circulation failure index unit 15, cold chain reminder information unit 16. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiments of the present application provide a method and system for precise control of cold chain equipment. For the target transport product, first determine its type and temperature sensitivity, and output the corresponding cold chain transport process accordingly. Set a temperature sensing module at each cold chain transport node to monitor the node temperature in real time. At the same time, monitor the temperature and duration information of the product flow process between nodes, and calculate the loss density of the flow process. Then, combined with the product sensitivity, train the product failure prediction model, calculate the impact of the flow process between nodes on the product, and integrate the evaluation results of each section of the flow process to obtain the integrated failure index of the overall cold chain process, and generate cold chain reminder information. Starting from the correlation between cold chain nodes, the monitoring and control of the entire cold chain logistics chain from point to surface is realized, effectively improving the accuracy and comprehensiveness of cold chain equipment monitoring and control.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0017] Embodiment 1

[0018] like Figure 1 As shown, the embodiment of the present application provides a method for precise control of cold chain equipment, the method comprising:

[0019] Acquire the type of the target transport product, and determine the product-temperature sensitivity according to the type of the target transport product;

[0020] In the embodiment of the present application, the target transport product refers to various types of food, medicine and other products that need to be transported through the cold chain, such as fish, meat, eggs, vaccines, etc. The type of the target transport product is identified by product labels, barcodes, etc. Product-temperature sensitivity refers to the sensitivity of different categories of products to temperature changes. A product-temperature sensitivity database is established in advance, which contains temperature sensitivity parameters corresponding to different product categories. The temperature sensitivity parameters are the optimal storage temperature of the corresponding product, the allowable storage temperature range, etc.

[0021] Use radio frequency identification technology to read product labels or barcodes to obtain the type of target transport products. Store the product-temperature sensitivity database on the database server of the transportation management system. Implement database queries through standard structured query language. After obtaining the type of target transport products, call SQL statements to extract the temperature sensitivity parameters corresponding to the type of target transport products from the product-temperature sensitivity database, determine the product-temperature sensitivity, and provide basic data support for accurate temperature control in each link of cold chain transportation.

[0022] Outputting a cold chain transportation process according to the type of the target transportation product, and monitoring the temperature of each cold chain transportation node to obtain a plurality of storage temperature data, wherein the plurality of storage temperature data are obtained through a plurality of temperature sensing modules, and the plurality of temperature sensing modules are integrated;

[0023] In an embodiment of the present application, according to the temperature control requirements of the type of target transported product during the cold chain transportation process, the complete cold chain process path of the product from the starting point to the end point is planned, and the storage temperature of each cold chain transportation node is determined. For example, if vaccine drugs need to be transported in an environment of 2-8°C, the cold chain transportation process corresponding to the temperature requirements is output. Among them, the cold chain transportation node refers to the location where the product is transferred and stored during the transportation process from the starting point to the end point, such as a cold storage, transport vehicle, etc.

[0024] A temperature sensing module is integrated in each cold chain transport node to monitor the temperature of each cold chain transport node, thereby obtaining the storage temperature data corresponding to each cold chain transport node and forming multiple storage temperature data. Among them, the temperature sensing module is a temperature acquisition module that includes functions such as temperature probe, signal amplification, filtering, and analog-to-digital conversion; the storage temperature data is the real-time temperature value of the cold chain transport node collected by the temperature sensing module.

[0025] By monitoring the product circulation between two adjacent cold chain transportation nodes in each cold chain transportation node, multiple circulation temperature data and multiple circulation durations are obtained;

[0026] In the embodiment of the present application, the product circulation between two adjacent cold chain transport nodes refers to the movement process of the target transport product between two consecutive cold chain transport nodes, for example, the target transport product circulates from the cold storage to the transport vehicle, or from the transport vehicle to the cold storage, etc.

[0027] Attach an RFID tag containing a temperature sensor to the target transport product to continuously monitor the temperature of the target transport product during circulation. At the same time, an RFID reader / writer is set between two adjacent cold chain transport nodes. When the target transport product flows from node A to node B, the RFID reader / writer of node A will read and record the product number and outbound time of the RFID tag. When the same target transport product enters node B, the RFID reader / writer of node B will also read and record the product number and entry time. By matching the product entry and exit records of nodes A and B, the duration of the product flow from A to B is determined. At the same time, the RFID tag continuously detects and records the temperature during the entire circulation process and uploads it to the server. For each target transport product, the circulation temperature curve of the target transport product from node A to B is obtained based on its entry and exit time and the continuously recorded temperature data. Repeat the above process. When the product flows through all cold chain transport nodes, the temperature data and circulation duration of each circulation process are obtained to obtain multiple circulation temperature data and multiple circulation durations.

[0028] Outputting a plurality of circulation loss densities according to the plurality of circulation temperature data and the storage temperature data of the two adjacent cold chain transportation nodes;

[0029] Further, such as Figure 2 As shown, this step specifically includes:

[0030] Acquire first circulation temperature data, and storage temperature data of two adjacent cold chain transportation nodes corresponding to the first circulation temperature data;

[0031] Determine whether the storage temperature data of the two adjacent cold chain transportation nodes are the same, and when the storage temperature data of the two adjacent cold chain transportation nodes are the same, integrate the storage temperature data and the first circulation temperature data to output first integral data;

[0032] The circulation loss is identified using the first integral data, and a first circulation loss density is output, and so on, to obtain multiple circulation loss densities corresponding to multiple circulation nodes.

[0033] Furthermore, this step also includes:

[0034] When the storage temperature data of the two adjacent cold chain transportation nodes are different, obtaining the front storage temperature data of the front transportation node and the rear storage temperature data of the rear transportation node;

[0035] The smaller one of the front storage temperature data and the rear storage temperature data is obtained and integrated with the first circulation temperature data, and the first integrated data is output.

[0036] Furthermore, this step also includes:

[0037] The expression of the first flow loss density is as follows:

[0038] ;

[0039] in, Indicates flow temperature data Based on timing The change curve of The storage temperature data based on the smaller temperature data of two adjacent cold chain transportation nodes At the same time, Establishing a points cap gives The first integral data, is a random error.

[0040] In a preferred embodiment, the multiple circulation temperature data obtained are traversed, and a set of temperature curve data is selected as the first circulation temperature data. At the same time, it is found that the circulation between which two cold chain transport nodes corresponds to this set of temperature curve data, such as two adjacent cold chain transport nodes A and B. Then, the storage temperature data of the two cold chain transport nodes A and B are obtained from the multiple storage temperature data. Subsequently, it is determined whether the storage temperature data of the two cold chain transport nodes A and B are the same. When the storage temperature data of the two cold chain transport nodes are equal, the storage temperature value in the storage temperature data of any of the nodes is selected as the upper limit of the integral operation. Subsequently, the temperature values ​​at each time point in the first circulation temperature data and the selected upper limit of the integral operation are numerically integrated to obtain the first integral data, which reflects the total amount of the difference between the circulation temperature and the storage temperature. For example, the storage temperature data of two adjacent cold chain transport nodes when the target transport product is circulated are the same, both of which are 2°C; 2°C is used as the upper limit of the integral, and the total area between the circulation temperature curve and the upper limit is calculated as the first integral data.

[0041] When it is determined that the storage temperature data of two adjacent cold chain transport nodes are different, the storage temperature value of each cold chain transport node is obtained separately, wherein the storage temperature data of the previous node from which the target transport product flows out is identified as the front storage temperature data; the storage temperature data of the subsequent node from which the target transport product flows in is identified as the rear storage temperature data. Then, the front storage temperature data and the rear storage temperature data are compared, and the smaller temperature data is selected as the upper limit temperature of the integration. For example, if the front storage temperature data is 3°C and the rear storage temperature data is 5°C, 3°C is selected as the upper limit of the integration. Next, the upper limit temperature and the first circulation temperature data are numerically integrated to calculate the area between the circulation temperature curve and the upper limit as the first integral data.

[0042] The flow loss is identified by calculating the first integral data in the above manner to obtain a first flow loss density. Preferably, the expression of the first flow loss density is: ,in, represents the first flow loss density, Indicates flow temperature data Based on timing The change curve of, that is, the circulation temperature curve corresponding to the first circulation temperature data, The storage temperature data based on the smaller temperature data of two adjacent cold chain transportation nodes At the same time, Establishing a points cap gives The first integral data, is a random error. According to this expression, by obtaining the circulation temperature curve of the first circulation temperature data and determining the upper limit temperature of integration, the circulation loss amount is calculated by numerical integration, and then combined with the circulation time, the flow loss is identified and the circulation loss density is accurately obtained, providing a basis for accurate temperature control of cold chain transportation nodes. Repeat the above process to calculate the circulation loss density of all circulation processes and obtain multiple circulation loss densities.

[0043] Preferably, outputting the first flow loss density further includes:

[0044] Determine whether the temperature change rate of the first circulation temperature data is greater than or equal to a preset change rate, and when the temperature change rate of the first circulation temperature data is greater than or equal to the preset change rate, obtain a temperature difference curve;

[0045] Generate a K-segment characteristic temperature according to the temperature difference of the temperature difference curve;

[0046] Using the stored temperature data, the K-segment characteristic temperature is respectively integrated in sections, and the section integration data is output;

[0047] A first flow loss density is output according to the segmented integrated data.

[0048] In a preferred embodiment, the first circulation temperature data represents the temperature curve data collected during the circulation of the target transport product between two cold chain transport nodes. The temperature change rate refers to the slope of the first circulation temperature data changing over time, reflecting how fast the temperature rises or falls. The preset change rate is determined based on the product characteristics. When the temperature change rate is greater than or equal to the preset change rate, it indicates that the temperature changes too fast. Specifically, multiple sampling points on the temperature curve data are selected, and their slopes are calculated as the temperature change rate. If there is a temperature change rate that is greater than or equal to the preset change rate, the temperature change rate is abnormal. At this time, a complete temperature difference curve of the circulation process is obtained.

[0049] Then, according to the temperature difference distribution of the temperature difference curve, it is divided into K characteristic intervals. The maximum temperature difference ΔT on the temperature difference curve is calculated, and ΔT is divided into K segments, and the temperature difference of each segment is ΔT / K. Taking the minimum temperature on the curve as the starting temperature, K-segment characteristic temperatures are generated according to the increment of each temperature difference ΔT / K. After obtaining the K-segment characteristic temperature, these characteristic temperatures are integrated and calculated respectively according to the determined storage temperature data, and the segmented integral data is output to obtain the temperature control loss in different temperature intervals, that is, the temperature difference area between different temperature intervals and the storage temperature data, which accurately reflects the control situation of different temperature intervals, which is conducive to finding the weak links in the cold chain. Finally, according to the temperature difference of each segment, the weight of each segmented integral data is determined, and then the weighted calculation of each segmented integral data is performed to obtain the first circulation loss density L, and the circulation loss identification of the entire circulation process is accurately realized.

[0050] Using the product-temperature sensitivity as an input quantity and the multiple circulation loss densities and the multiple circulation durations as input variables to train a product failure prediction function to obtain multiple circulation failure indicators;

[0051] Furthermore, this step specifically includes:

[0052] Training a product failure prediction function, wherein the product failure prediction function is obtained through training using product-temperature sensitivity samples, circulation loss density samples, circulation time samples, and identification information identifying circulation failure indicators;

[0053] The multiple circulation loss densities and the multiple circulation durations corresponding to the multiple circulation nodes are input into the product failure prediction function to obtain the multiple circulation failure indicators, wherein the multiple circulation failure indicators are used to identify the risk indicators of product quality failure caused by the circulation environment at each circulation node.

[0054] In a preferred embodiment, the circulation failure index refers to the risk index of a certain degree of failure of product quality caused by the circulation environment conditions during the cold chain transportation and storage of the target transport product. In order to obtain the circulation failure index, it is necessary to train the product failure prediction function. First, collect the data of historical cold chain logistics, from which the circulation loss density samples and circulation time samples are collected, and then the expert group identifies the circulation failure index for each group of samples to form a training sample set. Then, linear regression, neural network, support vector machine, etc. are selected as prediction models, and a group of samples are repeatedly selected from the training sample set, with product-temperature sensitivity as the input quantification, and the circulation loss density and circulation time in the sample as input. According to the identification information of the circulation failure index, supervised learning is performed, and the model training effect is evaluated to ensure that its prediction performance is reliable. Through iterative training, the hyperparameters are adjusted to obtain the final product failure prediction model. Subsequently, the product failure prediction model is encapsulated as a software function and integrated into the cold chain control system as a product failure prediction function.

[0055] After obtaining multiple circulation loss densities and circulation durations corresponding to multiple circulation nodes, each circulation loss density and circulation duration is input into the trained product failure prediction function. This prediction function evaluates the risk probability of product quality loss under given circulation conditions, that is, the circulation failure index. After repeated predictions, the circulation failure index corresponding to each circulation node is obtained, that is, multiple circulation failure indexes, which identify the product quality loss risk in each circulation process.

[0056] Failure fusion is performed according to the multiple circulation failure indicators, a first fused failure indicator is output, and first cold chain reminder information is generated according to the first fused failure indicator.

[0057] Furthermore, the embodiment of the present application also includes:

[0058] When the first fusion failure index is greater than a preset fusion failure index, locating an abnormal circulation node based on the multiple circulation failure indexes;

[0059] A temperature feedback control parameter is generated according to the abnormal circulation node, and the abnormal circulation node of the cold chain is controlled according to the temperature feedback control parameter.

[0060] In a preferred embodiment, after obtaining multiple circulation failure indicators, all circulation failure indicators are comprehensively considered, added up, and failure fusion is achieved to obtain the first fusion failure indicator of the entire cold chain circulation process.

[0061] At the same time, according to the characteristics of the target transport product, its quality and safety threshold in the entire cold chain circulation process is determined as the preset fusion failure index, and the preset circulation failure index in each circulation process is determined as the quality and safety threshold in each circulation process. When the calculated first fusion failure index is higher than the preset fusion failure index, it indicates that there is a high product quality risk in the entire cold chain transportation. At this time, the first cold chain reminder information is generated according to the first fusion failure index, which is used to remind the cold chain management personnel to carry out targeted optimization control of the control of the cold chain equipment and the cold chain circulation process. At the same time, the preset circulation failure index in each circulation process is compared with the corresponding circulation failure index, and the circulation node whose circulation failure index is higher than the preset circulation failure index is determined as the abnormal circulation node.

[0062] Subsequently, temperature feedback control parameters are generated for each abnormal circulation node, that is, the temperature control measures that need to be adjusted are determined based on the environmental conditions of the abnormal circulation node, the temperature changes of the target transport product at the circulation node, etc. For example, lowering the storage temperature setting value of the node, shortening the circulation process time, etc., aiming to reduce the negative impact of the node on product quality. After the temperature feedback control parameters are generated, they are sent to the temperature control system of the corresponding cold chain equipment, such as the cold storage temperature control system, the vehicle cooling system, etc. The cold chain equipment optimizes the temperature and humidity environment of the node based on the received temperature feedback control parameters, and completes the control of the abnormal circulation node. Through targeted node control, the quality risk of the product in the circulation link can be effectively reduced, and the reliability of the overall cold chain and the product preservation quality can be improved.

[0063] In summary, the precise control method of cold chain equipment provided in the embodiment of the present application has the following technical effects:

[0064] Obtain the type of target transport product, determine the product-temperature sensitivity according to the type of target transport product, determine the temperature tolerance of different products, and establish a basis for product failure assessment. Output the cold chain transportation process according to the type of target transport product, monitor the temperature of each cold chain transportation node, obtain multiple storage temperature data, and grasp the temperature change of each node in the cold chain in real time. By monitoring the product circulation between two adjacent cold chain transportation nodes in each cold chain transportation node, obtain multiple circulation temperature data and multiple circulation durations to reflect the changes of products during transportation. According to multiple circulation temperature data and storage temperature data of two adjacent cold chain transportation nodes, output multiple circulation loss densities to evaluate the impact of the circulation process on the product. Take product-temperature sensitivity as the input quantity, and use multiple circulation loss densities and multiple circulation durations as input variables to train the product failure prediction function, obtain multiple circulation failure indicators, and establish the ability to predict product quality changes under complex conditions. Perform failure fusion according to multiple circulation failure indicators, output the first fusion failure indicator, generate the first cold chain reminder information according to the first fusion failure indicator, and obtain the quality monitoring results of the entire cold chain process, thereby improving the accuracy and comprehensiveness of cold chain equipment monitoring and control.

[0065] Embodiment 2

[0066] Based on the same inventive concept as the precise control method of cold chain equipment in the above embodiment, Figure 3 As shown, the embodiment of the present application provides a precise control system for cold chain equipment, the system comprising:

[0067] A product type acquisition unit 11, used to acquire the type of the target transport product and determine the product-temperature sensitivity according to the type of the target transport product;

[0068] A node temperature monitoring unit 12, used to output a cold chain transportation process according to the type of the target transportation product, and monitor the temperature of each cold chain transportation node to obtain a plurality of storage temperature data, wherein the plurality of storage temperature data are obtained through a plurality of temperature sensing modules, and the plurality of temperature sensing modules are integrated;

[0069] The product circulation monitoring unit 13 is used to monitor the product circulation between two adjacent cold chain transportation nodes in each cold chain transportation node to obtain multiple circulation temperature data and multiple circulation durations;

[0070] A circulation loss density unit 14, configured to output a plurality of circulation loss densities according to the plurality of circulation temperature data and the storage temperature data of the two adjacent cold chain transportation nodes;

[0071] A circulation failure index unit 15 is used to train a product failure prediction function using the product-temperature sensitivity as an input quantity and the plurality of circulation loss densities and the plurality of circulation durations as input variables to obtain a plurality of circulation failure indexes;

[0072] The cold chain reminder information unit 16 is used to perform failure fusion according to the multiple circulation failure indicators, output a first fused failure indicator, and generate first cold chain reminder information according to the first fused failure indicator.

[0073] Furthermore, the product circulation monitoring unit 13 includes the following execution steps:

[0074] Acquire first circulation temperature data, and storage temperature data of two adjacent cold chain transportation nodes corresponding to the first circulation temperature data;

[0075] Determine whether the storage temperature data of the two adjacent cold chain transportation nodes are the same, and when the storage temperature data of the two adjacent cold chain transportation nodes are the same, integrate the storage temperature data and the first circulation temperature data to output first integral data;

[0076] The circulation loss is identified using the first integral data, and a first circulation loss density is output, and so on, to obtain multiple circulation loss densities corresponding to multiple circulation nodes.

[0077] Furthermore, the product circulation monitoring unit 13 further includes the following execution steps:

[0078] When the storage temperature data of the two adjacent cold chain transportation nodes are different, obtaining the front storage temperature data of the front transportation node and the rear storage temperature data of the rear transportation node;

[0079] The smaller one of the front storage temperature data and the rear storage temperature data is obtained and integrated with the first circulation temperature data, and the first integrated data is output.

[0080] Furthermore, the product circulation monitoring unit 13 further includes the following execution steps:

[0081] The expression of the first flow loss density is as follows:

[0082] ;

[0083] in, Indicates flow temperature data Based on timing The change curve of The storage temperature data based on the smaller temperature data of two adjacent cold chain transportation nodes At the same time, Establishing a points cap gives The first integral data, is a random error.

[0084] Furthermore, the circulation failure indicator unit 15 includes the following execution steps:

[0085] Training a product failure prediction function, wherein the product failure prediction function is obtained through training using product-temperature sensitivity samples, circulation loss density samples, circulation time samples, and identification information identifying circulation failure indicators;

[0086] The multiple circulation loss densities and the multiple circulation durations corresponding to the multiple circulation nodes are input into the product failure prediction function to obtain the multiple circulation failure indicators, wherein the multiple circulation failure indicators are used to identify the risk indicators of product quality failure caused by the circulation environment at each circulation node.

[0087] Furthermore, the product circulation monitoring unit 13 further includes the following execution steps:

[0088] Determine whether the temperature change rate of the first circulation temperature data is greater than or equal to a preset change rate, and when the temperature change rate of the first circulation temperature data is greater than or equal to the preset change rate, obtain a temperature difference curve;

[0089] Generate a K-segment characteristic temperature according to the temperature difference of the temperature difference curve;

[0090] Using the stored temperature data, the K-segment characteristic temperature is respectively integrated in sections, and the section integration data is output;

[0091] A first flow loss density is output according to the segmented integrated data.

[0092] Further, the cold chain reminder information unit 16 includes the following execution steps:

[0093] When the first fusion failure index is greater than a preset fusion failure index, locating an abnormal circulation node based on the multiple circulation failure indexes;

[0094] A temperature feedback control parameter is generated according to the abnormal circulation node, and the abnormal circulation node of the cold chain is controlled according to the temperature feedback control parameter.

[0095] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0096] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for precise control of cold chain equipment, characterized in that: The method comprises: Acquire the type of the target transport product, and determine the product-temperature sensitivity according to the type of the target transport product; Outputting a cold chain transportation process according to the type of the target transportation product, and monitoring the temperature of each cold chain transportation node to obtain a plurality of storage temperature data, wherein the plurality of storage temperature data are obtained by a plurality of temperature sensing units, and the plurality of temperature sensing units are integrated; By monitoring the product circulation between two adjacent cold chain transportation nodes in each cold chain transportation node, multiple circulation temperature data and multiple circulation durations are obtained; Outputting a plurality of circulation loss densities according to the plurality of circulation temperature data and the storage temperature data of the two adjacent cold chain transportation nodes; Using the product-temperature sensitivity as an input quantity and the multiple circulation loss densities and the multiple circulation durations as input variables to train a product failure prediction function to obtain multiple circulation failure indicators; Perform failure fusion according to the multiple circulation failure indicators, output a first fused failure indicator, and generate first cold chain reminder information according to the first fused failure indicator; Outputting a plurality of circulation loss densities according to the plurality of circulation temperature data and the storage temperature data of the two adjacent cold chain transportation nodes, the method further comprising: Acquire first circulation temperature data, and storage temperature data of two adjacent cold chain transportation nodes corresponding to the first circulation temperature data; Determine whether the storage temperature data of the two adjacent cold chain transportation nodes are the same, and when the storage temperature data of the two adjacent cold chain transportation nodes are the same, integrate the storage temperature data and the first circulation temperature data to output first integral data; Using the first integral data to identify circulation loss, output a first circulation loss density, and so on, obtain multiple circulation loss densities corresponding to multiple circulation nodes; Determining whether the storage temperature data of the two adjacent cold chain transport nodes are the same, the method further includes: When the storage temperature data of the two adjacent cold chain transportation nodes are different, obtaining the front storage temperature data of the front transportation node and the rear storage temperature data of the rear transportation node; obtaining a smaller one of the front storage temperature data and the rear storage temperature data and integrating it with the first circulation temperature data, and outputting the first integrated data; The expression of the first flow loss density is as follows: ； in, Indicates flow temperature data Based on timing The change curve of The storage temperature data based on the smaller temperature data of two adjacent cold chain transportation nodes The integral data of Establishing a points cap gives The first integral data, is a random error.

2. The method according to claim 1, characterized in that The method further comprises: taking the product-temperature sensitivity as an input quantity, taking the multiple circulation loss densities and the multiple circulation durations as input variables to train a product failure prediction function to obtain multiple circulation failure indicators. Training a product failure prediction function, wherein the product failure prediction function is obtained through training using product-temperature sensitivity samples, circulation loss density samples, circulation time samples, and identification information identifying circulation failure indicators; The multiple circulation loss densities and the multiple circulation durations corresponding to the multiple circulation nodes are input into the product failure prediction function to obtain the multiple circulation failure indicators, wherein the multiple circulation failure indicators are used to identify the risk indicators of product quality failure caused by the circulation environment at each circulation node.

3. The method according to claim 1, characterized in that The method further comprises: Determine whether the temperature change rate of the first circulation temperature data is greater than or equal to a preset change rate, and when the temperature change rate of the first circulation temperature data is greater than or equal to the preset change rate, obtain a temperature difference curve; Generate a K-segment characteristic temperature according to the temperature difference of the temperature difference curve; Using the stored temperature data, the K-segment characteristic temperature is respectively integrated in sections, and the section integration data is output; A first flow loss density is output according to the segmented integrated data.

4. The method according to claim 1, characterized in that The method of generating first cold chain reminder information according to the first fusion failure indicator includes: When the first fusion failure index is greater than a preset fusion failure index, locating an abnormal circulation node based on the multiple circulation failure indexes; A temperature feedback control parameter is generated according to the abnormal circulation node, and the abnormal circulation node of the cold chain is controlled according to the temperature feedback control parameter.

5. A precision control system for cold chain equipment, characterized in that: A method for implementing a precise control method of cold chain equipment according to any one of claims 1 to 4, the system comprising: A product type acquisition unit, the product type acquisition unit is used to acquire the type of the target transport product, and determine the product-temperature sensitivity according to the type of the target transport product; A node temperature monitoring unit, the node temperature monitoring unit is used to output the cold chain transportation process according to the type of the target transportation product, and monitor the temperature of each cold chain transportation node to obtain a plurality of storage temperature data, wherein the plurality of storage temperature data are obtained by a plurality of temperature sensing units, and the plurality of temperature sensing units are integrated; A product circulation monitoring unit, the product circulation monitoring unit is used to obtain multiple circulation temperature data and multiple circulation durations by monitoring the product circulation between two adjacent cold chain transportation nodes in each cold chain transportation node; a circulation loss density unit, the circulation loss density unit being used to output a plurality of circulation loss densities according to the plurality of circulation temperature data and the storage temperature data of the two adjacent cold chain transportation nodes; a circulation failure index unit, the circulation failure index unit being used to train a product failure prediction function using the product-temperature sensitivity as an input quantity and the plurality of circulation loss densities and the plurality of circulation durations as input variables to obtain a plurality of circulation failure indexes; A cold chain reminder information unit, wherein the cold chain reminder information unit is used to perform failure fusion according to the multiple circulation failure indicators, output a first fusion failure indicator, and generate first cold chain reminder information according to the first fusion failure indicator.

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