Cold storage temperature intelligent monitoring system and method

Abstract

The invention discloses an intelligent monitoring system and method for the temperature of a refrigeration house, and relates to the technical field of refrigeration house temperature monitoring. Basic information data of warehousing articles are collected; searching for refrigeration house parameter data matched with the object target temperature in the basic information data of the warehouse-in and warehouse-out objects, and generating target refrigeration house parameter data; on the basis of the target refrigeration house parameter data and the basic information data of the stored articles, calculating the temperature variation of the refrigeration house after the articles are stored, and generating target refrigeration house temperature variation data; according to the intelligent monitoring system and method for the temperature of the refrigeration house, when articles cannot be stored at a time, the articles are divided into multiple parts, and each part of articles is stored in different refrigeration houses, so that the temperature rise caused by storing too many articles in the refrigeration houses at a time is reduced, and the articles are firstly stored in other refrigeration houses with higher temperature for stepped cooling; and the burden of refrigeration equipment of the refrigeration house can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cold storage temperature monitoring, and in particular to a cold storage temperature intelligent monitoring system and method. Background Art

[0002] Cold storage monitoring technology is the key to ensuring the stability of temperature and humidity environments in scenarios such as cold chain logistics, food and medicine warehousing, etc. Its core goal is to monitor environmental parameters (such as temperature, humidity, gas concentration, etc.) in the cold storage in real time and issue timely warnings of abnormal situations to ensure the quality and safety of stored items. The prior art with publication number CN119755876A discloses a cold storage fruit and vegetable preservation method, a cold storage control device, and cold chain transportation equipment. Among them, the method includes: detecting the maturity of fruits and vegetables in each storage area in the cold storage; adjusting the cold storage temperature according to the maturity of fruits and vegetables; judging whether to transfer the fruits and vegetables in the storage area to a low-temperature storage area according to the maturity of fruits and vegetables in each storage area; wherein a low-temperature storage area is provided in the cold storage, and the temperature of the low-temperature storage area is lower than the cold storage temperature. The present invention adjusts the temperature in the cold storage by real-time monitoring of the maturity of fruits and vegetables in the cold storage to suppress the growth of ethylene and realize dynamic regulation of ethylene concentration. Fruits and vegetables with higher maturity are transferred to the low-temperature storage area with lower temperature for low-temperature refrigeration storage. Thereby, fine regulation of the quality of fruits and vegetables is achieved, maintaining the optimal storage environment for fruits and vegetables, and reducing the loss of fruit and vegetable quality. Achieve efficient monitoring and processing of cold chain fruits and vegetables, reduce manual operations and improve work efficiency.

[0003] However, in actual cold storage work, it is usually necessary to store various types of items. Different types of items require different storage temperatures. If the storage volume is too large, one-time storage may cause the cold storage temperature to rise significantly, affecting the storage effect of other items in the cold storage. Summary of the Invention

[0004] The purpose of the present invention is to provide a cold storage temperature intelligent monitoring system and method to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a method for intelligently monitoring the temperature of a cold storage, comprising the following steps:

[0006] S1, collect cold storage parameter data;

[0007] S2. Collect basic information data of incoming items;

[0008] S3. Search the basic information data of the inbound and outbound items for cold storage parameter data that matches the target temperature of the items, and generate target cold storage parameter data;

[0009] S4. Based on the target cold storage parameter data and the basic information data of the stored items, calculate the temperature change of the cold storage after the items are stored to generate target cold storage temperature change data;

[0010] S5. Determine whether the target cold storage temperature change data is greater than a set cold storage temperature change threshold. If not, place the items corresponding to the basic information data of the incoming items into the cold storage corresponding to the target cold storage parameter data.

[0011] S6. If yes, the basic information data of the incoming items is divided into n parts corresponding to the items, and each part is placed in a different cold storage with a temperature not lower than the target temperature of the items. The item entry plan analysis is performed to generate item entry plan analysis data;

[0012] S7. Execute an item storage plan feedback task based on the item storage plan analysis data.

[0013] Furthermore, the S1 includes the following steps:

[0014] S11, collect cold storage parameter data, generate cold storage parameter data set A=(A1, ..., A m ,…,A ω ), m=1, 2, 3,…,ω,A m represents the cold storage parameter data of the mth cold storage, ω represents the maximum number of cold storages, b m,w Indicates A m The wth type of cold storage parameter data, Indicates the maximum number of cold storage parameter data categories. Cold storage parameter data includes cold storage temperature, cold storage humidity, cold storage gas concentration data (such as carbon dioxide, ethylene, etc.), temperature, humidity, gas concentration limit data, compressor real-time power, evaporation temperature, condensation temperature, refrigerant flow rate, etc.

[0015] Furthermore, the S2 includes the following steps:

[0016] S21, collect basic information data of the warehoused items, and generate a basic information data set C of the warehoused items = (C1, ..., C p ,…,C τ ), p=1, 2, 3,..., τ, C p Indicates the basic information data of the p-th category of goods entering the warehouse, the maximum number of τ item types, C p =(d p,1 ,…,d p,o ,…,d p,σ ), o=1, 2, 3,…, σ, d p,o Indicates C pThe oth category of basic information data for incoming items, σ, is the maximum number of categories of basic information data for incoming items. The basic information data categories for incoming items may include item mass, incoming temperature, target temperature, moisture content, specific heat capacity, and phase transition temperature range. Item mass data can be obtained by weighing the item upon entry; incoming temperature data can be obtained using infrared thermometers or plug-in temperature probes; item moisture content data can be obtained using rapid moisture meters; and item specific heat capacity and phase transition temperature range can be obtained by querying relevant databases.

[0017] Furthermore, the S3 includes the following steps:

[0018] S31. Search for basic information data of inbound and outbound items based on regular expressions C p Cold storage parameter data A that matches the target temperature of the items m , generate target cold storage parameter data A mubiao ; When searching using regular expressions, you can do so by following these steps:

[0019] Preprocess the data, including cleaning noise data and structuring the data;

[0020] Compile regular expressions;

[0021] Read data by line or block, and apply regular expressions to extract data;

[0022] De-duplicate the extracted data, such as using Bloom filters or sets;

[0023] Create a hash index;

[0024] Compare the basic information data of incoming items C p And cold storage parameter data A m The target temperature is the same as the cold storage temperature, and the corresponding cold storage parameter data A is output m .

[0025] Furthermore, the S4 includes the following steps:

[0026] S41, based on target cold storage parameter data A mubiao And basic information data of incoming items C p Calculate the sensible heat load and latent heat load of items;

[0027] S42, combining the sensible heat load and the latent heat load to calculate the target cold storage temperature change data D during the process of cooling the items to the corresponding temperature, D = (sensible heat load + latent heat load) / (cold storage air specific heat capacity * air density * cold storage volume), where the cold storage volume can be obtained from the corresponding cold storage parameter data A m Obtained from.

[0028] Furthermore, the S5 includes the following steps:

[0029] S51, determining whether the target cold storage temperature change data D is greater than a set cold storage temperature change threshold D0;

[0030] S52, if not, then the basic information data C of the warehoused items p The corresponding items are placed in the target cold storage parameter data A mubiao In the corresponding cold storage;

[0031] S53: If yes, proceed to the next step.

[0032] Furthermore, the step S6 includes the following steps:

[0033] S61. Divide the items into n parts, and the mass of each part is (m1, ..., m u ,…,m n ), u=1, 2, 3, ..., n, and set pheromone for each item Right now

[0034] S62. For each item, the search ant starts from the initial cold storage (i.e., the cold storage with the highest temperature), selects the next cold storage j according to probability, and records the path L u , the probability selection formula is as follows:

[0035]

[0036] Among them, R represents the set of allowed nodes, α represents the pheromone weight factor, β represents the heuristic information weight factor, η ij Represents the heuristic information of edge i to j;

[0037] S63, calculate the refrigeration time t of each item in each cold storage iu ;

[0038] S64. Calculate the temperature change of each cold storage and discard the path where the temperature change of each cold storage is greater than the maximum temperature change of the cold storage. The formula is as follows:

[0039]

[0040] Among them, k m represents the temperature rise coefficient of the mth cold storage;

[0041] S65. Update the pheromones of all edges based on the volatilization mechanism. The formula is as follows:

[0042]

[0043] ρ is the volatility;

[0044] S66, feasible path L for each ant based on pheromone enhancement u According to the total average temperature rise △T of the path avg Update pheromone, the formula is as follows:

[0045]

[0046] Q is the pheromone intensity constant, ω is the maximum number of cold storages;

[0047] S67. Record the optimal path and item quality for each item in history, and enhance the pheromone of the optimal path. The formula is as follows:

[0048]

[0049] Among them, L best represents the optimal path, △T avg，best is the total average temperature rise of the historical optimal path, Q best is the pheromone intensity constant of the historical optimal path;

[0050] S68. Determine whether the set maximum number of iterations has been reached. If not, return to S62. If so, output the quality, cold storage location, and time of each item corresponding to the historical optimal path, and generate item storage plan analysis data E.

[0051] A cold storage temperature intelligent monitoring system includes an interface, a storage, a processor, and a feedback module;

[0052] The interface is used to connect with external sensors, databases and input devices to collect cold storage parameter data and basic information data of stored items and input them into the storage;

[0053] The storage device is used to store computer programs;

[0054] The processor is used to execute the computer program to implement a method for intelligently monitoring the temperature of a cold storage and generate analysis data of an item entry plan;

[0055] The feedback module is used to feed back the item storage plan analysis data to the staff.

[0056] 1. Compared with the existing technology, the present invention provides an intelligent cold storage temperature monitoring system and method. By monitoring and analyzing the information of the cold storage and the items to be stored, it is determined whether storing the items in the cold storage at one time will cause the temperature of the cold storage to exceed the limit, so as to assist the management personnel in determining whether the items can be directly stored in the cold storage at the target temperature at one time.

[0057] 2. Compared with the existing technology, the present invention provides an intelligent cold storage temperature monitoring system and method. When the items cannot be stored at one time, the items are divided into multiple parts and each part is stored in a different cold storage to reduce the temperature rise caused by storing too many items in the cold storage at one time. The items are first stored in other cold storages with higher temperatures for step-by-step cooling, which can also reduce the burden on the cold storage refrigeration equipment and save more energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0059] Figure 1 A diagram of method steps provided by an embodiment of the present invention;

[0060] Figure 2 This is a system structure block diagram provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0062] Example embodiments will be described more fully hereinafter with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of this disclosure to those skilled in the art.

[0063] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0064] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0065] The terms used herein are used only to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a," "an," and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements, and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groups thereof is not excluded.

[0066] The embodiments described herein may be described with reference to plan views and / or cross-sectional views, with the aid of idealized schematic diagrams of the present disclosure. Thus, the example illustrations may be modified based on manufacturing techniques and / or tolerances. Therefore, the embodiments are not limited to the embodiments shown in the accompanying drawings, but include modifications of the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the accompanying drawings are schematic in nature, and the shapes of the regions shown in the drawings illustrate specific shapes of the regions of the elements, but are not intended to be limiting.

[0067] See also Figure 1 , a cold storage temperature intelligent monitoring method, comprising the following steps:

[0068] S1. Collect cold storage parameter data, including the following steps:

[0069] S11, collect cold storage parameter data, generate cold storage parameter data set A=(A1, ..., A m ,…,A ω ), m=1, 2, 3,…,ω,A m represents the cold storage parameter data of the mth cold storage, ω represents the maximum number of cold storages, b m,w Indicates A m The wth type of cold storage parameter data, Indicates the maximum number of cold storage parameter data categories. Cold storage parameter data includes cold storage temperature, cold storage humidity, cold storage gas concentration data (such as carbon dioxide, ethylene, etc.), temperature, humidity, gas concentration limit data, compressor real-time power, evaporation temperature, condensation temperature, refrigerant flow rate, etc.

[0070] S2. Collect basic information data of incoming items, including the following steps:

[0071] S21, collect basic information data of the warehoused items, and generate a basic information data set C of the warehoused items = (C1, ..., C p ,…,C τ ), p=1, 2, 3,..., τ, C p Indicates the basic information data of the p-th category of goods entering the warehouse, the maximum number of τ item types, C p =(d p,1 ,…,d p,o ,…,d p,σ ),o=1,2,3,…,σ,d p,o Indicates C pThe oth category of basic information data for incoming items, σ, is the maximum number of categories of basic information data for incoming items. The basic information data categories for incoming items may include item mass, incoming temperature, target temperature, moisture content, specific heat capacity, and phase transition temperature range. Item mass data can be obtained by weighing the item upon entry; incoming temperature data can be obtained using infrared thermometers or plug-in temperature probes; item moisture content data can be obtained using rapid moisture meters; and item specific heat capacity and phase transition temperature range can be obtained by querying relevant databases.

[0072] S3. Searching for cold storage parameter data that matches the target temperature of items in the basic information data of incoming and outgoing items, and generating target cold storage parameter data, including the following steps:

[0073] S31. Search for basic information data of inbound and outbound items based on regular expressions C p Cold storage parameter data A that matches the target temperature of the items m , generate target cold storage parameter data A mubiao .

[0074] In one embodiment, when searching using a regular expression, the following steps may be performed:

[0075] (1) Preprocess the data, including cleaning noise data and structuring the data;

[0076] (2) Compile regular expressions;

[0077] (3) Read data by line or block and apply regular expressions to extract data;

[0078] (4) Deduplication of the extracted data, such as using Bloom filters or set deduplication;

[0079] (5) Create a hash index;

[0080] (6) Compare the basic information data of the incoming items C p And cold storage parameter data A m The target temperature is the same as the cold storage temperature, and the corresponding cold storage parameter data A is output m .

[0081] S4. Based on the target cold storage parameter data and the basic information data of the stored items, the temperature change of the cold storage after the items are stored is calculated and processed to generate the target cold storage temperature change data, including the following steps:

[0082] S41, based on target cold storage parameter data A mubiao And basic information data of incoming items C p Calculate the sensible heat load and latent heat load of items;

[0083] S42, combining the sensible heat load and the latent heat load to calculate the target cold storage temperature change data D during the process of cooling the items to the corresponding temperature, D = (sensible heat load + latent heat load) / (cold storage air specific heat capacity * air density * cold storage volume), where the cold storage volume can be obtained from the corresponding cold storage parameter data A m Obtained from.

[0084] S5: Determine whether the target cold storage temperature change data is greater than the set cold storage temperature change threshold. If not, place the items corresponding to the basic information data of the incoming items into the cold storage corresponding to the target cold storage parameter data, including the following steps:

[0085] S51, determining whether the target cold storage temperature change data D is greater than the set cold storage temperature change threshold D0;

[0086] S52, if not, then the basic information data of the incoming items C p The corresponding items are placed in the target cold storage parameter data A mubiao In the corresponding cold storage;

[0087] S53: If yes, proceed to the next step.

[0088] S6. If so, the basic information data of the incoming items is divided into n parts corresponding to the items, and each part is placed in a different cold storage with a temperature not lower than the target temperature of the items. The item entry plan analysis process is performed with the minimum average cold storage temperature change data to generate the item entry plan analysis data, including the following steps:

[0089] S61. Divide the items into n parts, and the mass of each part is (m1, ..., m u ,…,m n ), u=1, 2, 3, ..., n, and set pheromone for each item Right now

[0090] S62. For each item, the search ant starts from the initial cold storage (i.e., the cold storage with the highest temperature), selects the next cold storage j according to probability, and records the path L u , the probability selection formula is as follows:

[0091]

[0092] Among them, R represents the set of allowed nodes, α represents the pheromone weight factor, β represents the heuristic information weight factor, η ij Represents the heuristic information of edge i to j;

[0093] S63, calculate the refrigeration time t of each item in each cold storage iu ;

[0094] S64. Calculate the temperature change of each cold storage and discard the path where the temperature change of each cold storage is greater than the maximum temperature change of the cold storage. The formula is as follows:

[0095]

[0096] Among them, k m represents the temperature rise coefficient of the mth cold storage;

[0097] S65. Update the pheromones of all edges based on the volatilization mechanism. The formula is as follows:

[0098]

[0099] ρ is the volatility;

[0100] S66, feasible path L for each ant based on pheromone enhancement u According to the total average temperature rise △T of the path avg Update pheromone, the formula is as follows:

[0101]

[0102]

[0103] Q is the pheromone intensity constant, ω is the maximum number of cold storages;

[0104] S67. Record the optimal path and item quality for each item in history, and enhance the pheromone of the optimal path. The formula is as follows:

[0105]

[0106] Among them, L best represents the optimal path, △T avg，best is the total average temperature rise of the historical optimal path, Q best is the pheromone intensity constant of the historical optimal path;

[0107] S68. Determine whether the set maximum number of iterations has been reached. If not, return to S62. If so, output the quality, cold storage location, and time of each item corresponding to the historical optimal path, and generate item storage plan analysis data E.

[0108] S7. Execute feedback on the item warehousing plan based on the item warehousing plan analysis data.

[0109] See also Figure 2 ,The present invention also provides a cold storage temperature intelligent monitoring system, including an interface, a storage, a processor, and a feedback module;

[0110] The interface is used to connect with external sensors, databases and input devices to collect cold storage parameter data and basic information data of incoming items and input them into the storage;

[0111] The memory is used to store computer programs;

[0112] The processor is used to execute a computer program to implement a cold storage temperature intelligent monitoring method and generate item storage plan analysis data;

[0113] The feedback module is used to feed back the item warehousing plan analysis data to the staff.

[0114] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

Claims

1. A method for intelligent monitoring of cold storage temperature, characterized by: The following steps are involved: S1, collect cold storage parameter data; S2. Collect basic information data of incoming items; S3. Search the basic information data of the inbound and outbound items for cold storage parameter data that matches the target temperature of the items, and generate target cold storage parameter data; S4. Based on the target cold storage parameter data and the basic information data of the stored items, calculate the temperature change of the cold storage after the items are stored to generate target cold storage temperature change data; S5. Determine whether the target cold storage temperature change data is greater than a set cold storage temperature change threshold. If not, place the items corresponding to the basic information data of the incoming items into the cold storage corresponding to the target cold storage parameter data. S6. If yes, the basic information data of the incoming items is divided into n parts corresponding to the items, and each part is placed in a different cold storage with a temperature not lower than the target temperature of the items. The item entry plan analysis is performed to generate item entry plan analysis data; S7. Execute an item storage plan feedback task based on the item storage plan analysis data.

2. A cold storage temperature intelligent monitoring method according to claim 1, characterized in that: Said S1 comprises the following steps: S11, collect cold storage parameter data, generate cold storage parameter data set A=(A1, ..., A m ,…,A ω ), m=1, 2, 3,…,ω,A m represents the cold storage parameter data of the mth cold storage, ω represents the maximum number of cold storages, b m,w Indicates A m The wth type of cold storage parameter data, Indicates the maximum number of cold storage parameter data categories.

3. A cold storage temperature intelligent monitoring method according to claim 2, characterized in that: The S2 comprises the following steps: S21, collect basic information data of the warehoused items, and generate a basic information data set C of the warehoused items = (C1, ..., C p ,…,C τ ), p=1, 2, 3,..., τ, C p Indicates the basic information data of the p-th category of goods entering the warehouse, the maximum number of τ item types, C p =(d p,1 ,…,d p,o ,…,d p,σ ), o=1, 2, 3,…, σ, d p,o Indicates C p The basic information data of the oth category of incoming items, the maximum number of categories of basic information data of σ incoming items.

4. A cold storage temperature intelligent monitoring method according to claim 3, characterized in that: The S3 includes the following steps: S31. Search for basic information data of inbound and outbound items based on regular expressions C p Cold storage parameter data A that matches the target temperature of the items m , generate target cold storage parameter data A mubiao .

5. The method for intelligently monitoring the temperature of a cold storage according to claim 4, characterized in that: The S4 comprises the following steps: S41, based on target cold storage parameter data A mubiao And basic information data of incoming items C p Calculate the sensible heat load and latent heat load of items; S42 , calculating target cold storage temperature change data D during the process of the target cold storage cooling the items to the corresponding temperature, based on the sensible heat load and the latent heat load.

6. A cold storage temperature intelligent monitoring method according to claim 5, characterized in that: The S5 comprises the following steps: S51, determining whether the target cold storage temperature change data D is greater than a set cold storage temperature change threshold D0; S52, if not, then the basic information data C of the warehoused items p The corresponding items are placed in the target cold storage parameter data A mubiao In the corresponding cold storage; S53: If yes, proceed to the next step.

7. A cold storage temperature intelligent monitoring method according to claim 6, characterized in that: The S6 comprises the following steps: S61. Divide the items into n parts, and the mass of each part is (m1, ..., m u ,…,m n ), u=1, 2, 3, ..., n, and set pheromone for each item S62. For each item, the search ant starts from the initial cold storage, selects the next cold storage j according to probability, and records the path L u ; S63, calculate the refrigeration time t of each item in each cold storage iu ; S64. Calculate the temperature change of each cold storage, and discard the path where the temperature change of each cold storage is greater than the maximum temperature change set for the cold storage; S65. Update the pheromones of all edges based on the volatilization mechanism; S66, feasible path L for each ant based on pheromone enhancement u According to the total average temperature rise △T of the path avg Update pheromones; S67. Record the optimal path and item quality corresponding to each historical item, and enhance the pheromone of the optimal path; S68. Determine whether the set maximum number of iterations has been reached. If not, return to S62. If so, output the quality, cold storage location, and time of each item corresponding to the historical optimal path, and generate item storage plan analysis data E.

8. A cold storage temperature intelligent monitoring system, used to implement the cold storage temperature intelligent monitoring method according to any one of claims 1 to 7, characterized in that: Including interface, storage, processor, feedback module; The interface is used to connect with external sensors, databases and input devices to collect cold storage parameter data and basic information data of stored items and input them into the storage; The storage device is used to store computer programs; The processor is configured to execute the computer program to implement the method for intelligently monitoring the temperature of a cold storage as claimed in any one of claims 1 to 7, and generate analysis data of an item entry plan; The feedback module is used to feed back the item storage plan analysis data to the staff.

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