An Internet of Things-based management system for cold-resistant box houses

Abstract

The present invention relates to the technical field of container house management, and in particular to an Internet of Things-based cold-resistant container house management system, including: a container house data acquisition module for real-time acquisition of the indoor environment information, outdoor environment information, indoor equipment operation information and container house volume of the cold-resistant container house; a node construction module for constructing data nodes and numbering the data nodes to obtain data numbers; an equipment analysis module for analyzing the air exchange time and equipment operation characteristics; a lighting analysis module for analyzing the lighting type; an environment analysis module for analyzing the environmental characteristics and internal and external difference characteristics; a feature storage module for analyzing the node feature types; a matching analysis module for analyzing the indoor management parameters; and a container house management module for managing the operation of the heating equipment in the cold-resistant container house. The present invention realizes precise management of the operation of the heating equipment in the container house.

Description

Technical Field

[0001] The present invention relates to the technical field of container house management, and particularly to an anti-freezing container house management system based on the Internet of Things. Background Art

[0002] The temperature management in a container house mainly focuses on ensuring the stability and comfort of equipment or living environment. By installing temperature sensors, temperature controllers and cooling and ventilation systems, the temperature inside the container house is monitored and adjusted in real time to cope with temperature changes under different climatic conditions, so as to ensure the stable operation of equipment or provide a comfortable living environment.

[0003] Chinese Patent Publication No.: CN113341844A discloses an intelligent container house control and management system based on Internet of Things technology, including an application layer, a transmission layer and a perception layer. The perception layer is the Internet of Things terminal equipment arranged in container houses in different regions, which monitors various data of the container house in real time; data interaction is realized between the application layer and the perception layer through the transmission layer. The cloud server in the perception layer receives the monitoring data and stores and processes it. The processing result is transmitted to the application layer, and the application layer issues control instructions to control the operation of Internet of Things devices, realizing remote centralized intelligent control and management of the container house. This invention only realizes the storage supervision of various collected data in the container house, and does not realize the precise management of the temperature in the container house based on the comprehensively collected equipment operation data and the environmental data inside and outside the container house. There are problems such as low efficiency in data analysis of anti-freezing container houses and inaccurate temperature management in anti-freezing container houses. Summary of the Invention

[0004] The purpose of the present invention is to provide an anti-freezing container house management system based on the Internet of Things to solve at least one of the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] An anti-freezing container house management system based on the Internet of Things, comprising:

[0007] A container house data acquisition module for real-time acquisition of the indoor environmental information, outdoor environmental information, indoor equipment operation information and the volume of the container house of the anti-freezing container house;

[0008] A node construction module for constructing data nodes according to a preset analysis period and numbering the data nodes to obtain data numbers;

[0009] An equipment analysis module for analyzing the air exchange time and equipment operation characteristics according to the indoor equipment operation information, the volume of the container house and the data number;

[0010] A light analysis module for analyzing the light type according to the indoor environmental information, outdoor environmental information and the data number;

[0011] An environmental analysis module for analyzing environmental characteristics and internal and external difference characteristics based on indoor environmental information, outdoor environmental information, and data numbers;

[0012] A feature storage module for storing feature data to obtain historical feature information and analyzing node feature types;

[0013] A matching analysis module for analyzing indoor management parameters based on air exchange time, historical feature information, node feature types, and lighting types;

[0014] A container house management module for managing the operation of heating equipment in a cold-resistant container house based on indoor management parameters.

[0015] Further, the equipment analysis module is provided with an air exchange analysis unit for analyzing the air exchange time according to the volume V of the container house and the air volume C(j,i) of the heating equipment to obtain the air exchange time T(j,i);

[0016] The equipment analysis module is also provided with an equipment operation analysis unit for analyzing equipment operation characteristics according to the heating equipment temperature W(j,i), the heating equipment power P1(j,i), and the total power P2(j,i) of electrical equipment to obtain the equipment operation characteristics A(j,i).

[0017] Further, the lighting analysis module determines the lighting type of the current analysis data node according to the indoor lighting intensity L1(j,i) and the outdoor lighting intensity L2(j,i), and the lighting type includes type I and type II.

[0018] Further, the environmental analysis module is provided with a first feature analysis unit for analyzing indoor environmental characteristics according to the measured temperatures R1(j,i), R2(j,i), R3(j,i) to obtain the indoor environmental characteristics as S(j,i);

[0019] The environmental analysis module is also provided with a second feature analysis unit for analyzing outdoor environmental characteristics according to the outdoor temperature R4(j,i), the outdoor wind speed v(j,i), and the outdoor lighting intensity L2(j,i) to obtain the outdoor environmental characteristics G(j,i);

[0020] The environmental analysis module is also provided with a third feature analysis unit for analyzing internal and external difference characteristics according to indoor temperature information and outdoor temperature to obtain the internal and external difference characteristics H(j,i).

[0021] Further, the feature storage module respectively extracts the current analysis data node and the first k data nodes before the current analysis data node as feature data nodes, and takes the set of device operation features corresponding to the feature data nodes as the device feature set, the set of indoor environment features corresponding to the feature data nodes as the indoor feature set, and the set of outdoor environment features corresponding to the feature data nodes as the outdoor feature set, where k represents an extraction parameter;

[0022] The feature storage module analyzes the historical fluctuation features based on the device feature set, the indoor feature set, and the outdoor feature set to obtain the historical fluctuation feature Q(j, i), and determines the node feature type of the current analysis data node according to the historical fluctuation feature. The node feature types include type one and type two.

[0023] Further, the matching analysis module is provided with a feature data analysis unit, which is used to analyze the data node features according to the air exchange time T(j, i), the device operation feature A(j, i), the indoor environment feature S(j, i), and the outdoor environment feature G(j, i) to obtain the data node feature F(j, i).

[0024] Further, the matching analysis module is also provided with a node type analysis unit, which is used to extract the non-current analysis internal and external difference features that satisfy h1 ≤ H(j, i) / H(j ∈ N + , n) ≤ h2 in the historical feature information as the type analysis difference features, and counts the number of the node feature types of type one corresponding to the day number and node number of the type analysis difference features as the first node type number, counts the number of the node feature types of type two corresponding to the day number and node number of the type analysis difference features as the second node type number, and adjusts the analysis process of the data node features according to the first node feature number N1(j, i) and the second node feature number N2(j, i). When N1(j, i) / [N1(j, i) + N2(j, i)] ≤ γ, the analysis process of the data node features is adjusted, and the adjusted data node feature is F1(j, i); where h1 represents the first historical extraction parameter, h2 represents the second historical extraction parameter, H(j ∈ N + , n) represents the non-current analysis internal and external difference features, n represents the non-current analysis node number, and γ represents the node type number threshold.

[0025] Further, the matching analysis module is also provided with a light feature analysis unit, which is used to optimize the adjustment process of the data node features according to the light type. When the light type is type one, the light feature analysis unit optimizes the adjustment process of the data node features, and the optimized data node feature is F2(j, i).

[0026] Further, the matching analysis module is also provided with a management parameter analysis unit, which is used to extract the node feature types and lighting types that are the same as the current analysis data node features and other stored data node features that satisfy h1 ≤ F(j,i) / F(j∈N + ,n) ≤ h2 as management node features, and analyze the indoor management parameters according to the management node features to obtain the indoor management parameter as X(j,i).

[0027] Further, the container house management module controls the power of the heating equipment in the cold-resistant container house according to the indoor management parameters. If X(j,i) ≤ x, the container house management module adjusts the power of the heating equipment in the cold-resistant container house to NP(j,i); otherwise, the container house management module does not adjust the power of the heating equipment in the cold-resistant container house; where x represents the management threshold and NP represents the power adjustment parameter.

[0028] The beneficial effects of the present invention are as follows: Through the collection of the indoor environment information, outdoor environment information, indoor equipment operation information, and the volume of the container house by the container house data collection module, and the analysis of the collected data by other modules, the comprehensive analysis of the indoor environment data, outdoor environment data, and indoor equipment operation data of the container house on the temperature characteristics of the container house is realized, and the relationship between the operation of the heating equipment in the container house and the temperature in the container house is judged by integrating various data to control the temperature in the container house to be stable and suitable, thereby improving the efficiency of the system's data analysis of the cold-resistant container house and improving the accuracy of the temperature management in the cold-resistant container house. Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0030] Figure 1 It is a schematic structural diagram of the cold-resistant container house management system based on the Internet of Things in this embodiment.

[0031] Figure 2 It is a schematic structural diagram of the equipment analysis module in this embodiment.

[0032] Figure 3 It is a schematic structural diagram of the environment analysis module in this embodiment.

[0033] Figure 4 It is a schematic structural diagram of the matching analysis module in this embodiment. Detailed Embodiments

[0034] To more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0035] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0036] Please refer to Figure 1 as shown, which is a cold-resistant box house management system based on the Internet of Things in this embodiment, including:

[0037] A box house data acquisition module for real-time acquisition of the indoor environment information, outdoor environment information, indoor equipment operation information, and box house volume of the cold-resistant box house. The indoor environment information includes indoor temperature information and indoor light intensity. The indoor temperature information includes the measured temperature and the measurement location. The indoor temperature information should satisfy that there are at least three measurement locations with measured temperatures. The measurement locations should satisfy that at least one temperature sensor is set at each of the indoor door, indoor window, and the top inside the box house to measure the indoor temperature data. The indoor light intensity is the light intensity of natural light. The outdoor environment information includes outdoor temperature, outdoor wind speed, and outdoor light intensity. The indoor light intensity and the outdoor light intensity are both the light intensity of natural light. The indoor equipment operation information includes the operation information of the heating equipment and the total power of the electrical equipment. The operation information of the heating equipment includes the temperature of the heating equipment, the power of the heating equipment, and the air volume of the heating equipment. The heating equipment includes, but is not limited to, electrical equipment such as air conditioners and hot air blowers for heating and warming. The electrical equipment is the equipment that needs to be powered on and operated inside the box house. The total power of the electrical equipment is the sum of the powers of all electrical equipment inside the box house. The temperature of the heating equipment is the temperature at the air outlet of the heating equipment. The units of the power of the heating equipment and the total power of the electrical equipment are watts. In this embodiment, the unit of light intensity is lux, the unit of temperature is degree Celsius, the unit of power is watt, the unit of wind speed is meter per second, the unit of volume is cubic meter, and the unit of air volume is cubic meter per minute. The acquisition methods of the indoor environment information and the outdoor environment information are obtained by collecting and uploading through sensors installed inside and outside the house. The acquisition method of the indoor equipment information is obtained by importing data from the operation management platform of the box house electrical equipment. The acquisition method of the box house volume is obtained through user interaction input.

[0038] Specifically, this embodiment is applied to the cloud of the equipment management platform for cold-resistant container houses to control the operation of heating equipment when there is a large temperature difference between the indoor and outdoor of the cold-resistant container house in cold regions, ensuring that the temperature inside the cold-resistant container house is appropriate and meeting the user's needs.

[0039] Please continue to refer to Figure 1 As shown, the Internet of Things-based cold-resistant container house management system further includes:

[0040] A node construction module for constructing data nodes according to a preset analysis period and numbering the data nodes to obtain a data number, where the data number includes a node number and a day number. The node construction module is connected to the container house data collection module.

[0041] Specifically, in this embodiment, the node construction module sets a data node every preset analysis period in a day and numbers the data nodes in chronological order to obtain a node number. The node number is set as i, i ∈ N + , i ≤ 1440 / η. The node construction module numbers the dates corresponding to the data nodes in chronological order to obtain a day number, and the day number is set as j, j ∈ N + , where η represents the duration of the preset analysis period. In this embodiment, the preset analysis period is set to 5 minutes. It can be understood that the setting of the preset analysis period is not specifically limited in this embodiment, and those skilled in the art can freely set it, such as it can also be set to 1 minute, 3 minutes, 10 minutes, etc.

[0042] Specifically, in this embodiment, the data nodes correspond one-to-one with the time points in a day. For example, when η = 5, the data node with node number 1 corresponds to 00:00 in a day, and the data node with node number 5 corresponds to 00:25 in a day.

[0043] Specifically, in this embodiment, when analyzing the data corresponding to the data nodes, the value of the data corresponding to the currently analyzed data node is set as the average value of the data between the currently analyzed node and the previous analyzed node.

[0044] Specifically, in this embodiment, through the analysis of the preset analysis period by the node construction module, data nodes are constructed, and the data nodes are made to correspond one-to-one with each moment in a day, ensuring the regularity of the collected data, making the data used more clear and definite when analyzing the collected data subsequently, thereby improving the data analysis efficiency of the system for the cold-resistant container house and enhancing the accuracy of the temperature management inside the cold-resistant container house.

[0045] Please continue to refer to Figure 1 As shown, the Internet of Things-based cold-resistant container house management system further includes:

[0046] An equipment analysis module is used to analyze the air exchange time and equipment operation characteristics according to the operation information of indoor equipment, the volume of the container house, and the data number. The equipment analysis module is connected to the node construction module.

[0047] Please refer to Figure 2 As shown, the equipment analysis module includes:

[0048] An air exchange analysis unit is used to analyze the air exchange time according to the volume of the container house, the air volume of the heating equipment, and the data number.

[0049] Specifically, in this embodiment, the air exchange analysis unit analyzes the air exchange time according to the volume of the container house and the air volume of the heating equipment, and sets the air exchange time as T(j,i), and sets T(j,i)=V / C(j,i), where V represents the volume of the container house, and C(j,i) represents the air volume of the heating equipment.

[0050] Specifically, in this embodiment, through the analysis of the volume of the container house and the air volume of the heating equipment by the air exchange analysis unit, the air exchange time is analyzed. The air exchange time represents the estimated time required for indoor air renewal when the heating equipment is turned on, increasing the diversity of system analysis, realizing the analysis of indoor air exchange, thereby improving the data analysis efficiency of the system for the cold-resistant container house and improving the accuracy of temperature management in the cold-resistant container house.

[0051] Please continue to refer to Figure 2 As shown, the equipment analysis module further includes:

[0052] An equipment operation analysis unit is used to analyze the equipment operation characteristics according to the equipment operation information and the data number. The equipment operation analysis unit is connected to the air exchange analysis unit.

[0053] Specifically, in this embodiment, the equipment operation analysis unit analyzes the equipment operation characteristics according to the temperature of the heating equipment, the power of the heating equipment, and the total power of the electrical equipment, and sets the equipment operation characteristics as A(j,i), and sets A(j,i)=P1(j,i)×e W(j,i) / w / P2(j,i); where P1(j,i) represents the power of the heating equipment, P2(j,i) represents the total power of the electrical equipment, W(j,i) represents the temperature of the heating equipment, w represents the temperature threshold, and 23≤w≤27. It can be understood that in this embodiment, the value of the temperature threshold is not specifically limited, and those skilled in the art can freely set it as long as it satisfies the analysis of the equipment operation characteristics. The optimal value of the temperature threshold is: w = 25.

[0054] Specifically, in this embodiment, the device operation analysis unit analyzes the device operation information to analyze the device operation characteristics, and uses the device operation characteristics to represent the characteristic relationship between the current heating device operation data and the comprehensive power consumption data in the container house, so as to realize the comprehensive analysis of the characteristics of the electrical equipment in the container house, thereby improving the data analysis efficiency of the system for the cold-resistant container house and improving the accuracy of temperature management in the cold-resistant container house.

[0055] Please continue to refer to Figure 1 As shown, the cold-resistant container house management system based on the Internet of Things further includes:

[0056] A light analysis module for analyzing the light type according to the indoor environment information, outdoor environment information and data number, and the light analysis module is connected to the node construction module.

[0057] Specifically, in this embodiment, the light analysis module analyzes the light type according to the indoor light intensity and the outdoor light intensity. If L1(j,i) < L2(j,i) and (L1(j,i) + L2(j,i)) / 2 > α or L1(j,i) ≥ L2(j,i) and L2(j,i) > α, the light analysis module sets the light type of the current analysis data node to type one; otherwise, the light analysis module sets the light type of the current analysis data node to type two; where L1(j,i) represents the indoor light intensity, L2(j,i) represents the outdoor light intensity, and α represents the light intensity threshold, 10 ≤ α ≤ 50. It can be understood that in this embodiment, the value of the light intensity threshold is not specifically limited, and those skilled in the art can freely set it as long as it satisfies the judgment of the light type. The optimal value of the light intensity threshold is: α = 20.

[0058] Specifically, in this embodiment, through the analysis of the indoor light intensity and the outdoor light intensity by the light analysis module, each data node is divided into two categories according to the light intensity size within its analysis period, realizing the influence of natural light direct irradiation on the indoor and outdoor environment analysis, thereby improving the data analysis efficiency of the system for the cold-resistant container house and improving the accuracy of temperature management in the cold-resistant container house.

[0059] Please continue to refer to Figure 1 As shown, the cold-resistant container house management system based on the Internet of Things further includes:

[0060] An environment analysis module for analyzing the environmental characteristics and internal and external difference characteristics according to the indoor environment information, outdoor environment information and data number. The environmental characteristics include indoor environmental characteristics and outdoor environmental characteristics, and the environment analysis module is connected to the node construction module.

[0061] Please refer to Figure 3 As shown, the environment analysis module includes:

[0062] The first feature analysis unit is used to analyze the indoor environmental features according to the indoor temperature information and the data number.

[0063] Specifically, in this embodiment, the first feature analysis unit analyzes the indoor environmental features according to the indoor temperature information, sets the indoor environmental feature as S(j,i), and sets S(j,i)=[R1(j,i)×e R2(j,i) / R3(j,i) +R2(j,i)×e R1(j,i) / R3(j,i) / [2×R3(j,i)×e [R1(j,i)+R2(j,i)] / [2×R3(j,i)] ; where R1(j,i) represents the measured temperature at the indoor door, R2(j,i) represents the measured temperature at the indoor window, and R3(j,i) represents the measured temperature at the top inside the container house.

[0064] Specifically, in this embodiment, through the analysis of the indoor temperature information by the first feature analysis unit, the indoor environmental features are analyzed, and the indoor environmental features are used to represent the temperature feature relationship between different temperature acquisition points in the container house, so as to realize the analysis of the temperature data difference between the door and window positions where air exchange may exist in the container house and the overall temperature data in the container house, thereby improving the data analysis efficiency of the system for the cold-resistant container house and improving the accuracy of temperature management in the cold-resistant container house.

[0065] Please continue to refer to Figure 3 As shown, the environmental analysis module further includes:

[0066] The second feature analysis unit is used to analyze the outdoor environmental features according to the outdoor environmental information and the data number, and the second feature analysis unit is connected to the first feature analysis unit.

[0067] Specifically, in this embodiment, the second feature analysis unit analyzes the outdoor environmental features according to the outdoor temperature, outdoor wind speed, and outdoor light intensity, sets the outdoor environmental feature as G(j,i), and sets G(j,i)=e R4(j,i) / w ×lg[L2(j,i) / α] / lg[v(j,i)+β], where R4(j,i) represents the outdoor temperature, v(j,i) represents the outdoor wind speed, β represents a calculation parameter, and 10 < β ≤ 13. It can be understood that in this embodiment, the value of the calculation parameter is not specifically limited, and those skilled in the art can freely set it as long as it satisfies the analysis of the outdoor environmental features. The optimal value of the calculation parameter is: β = 11.

[0068] Specifically, in this embodiment, the second feature analysis unit analyzes the outdoor environmental information to analyze the outdoor environmental features, and uses the outdoor environmental features to represent the environmental feature analysis achieved by integrating various outdoor environmental data, increasing the diversity of system analysis, thereby improving the data analysis efficiency of the cold-resistant box room system and the accuracy of temperature management in the cold-resistant box room.

[0069] Please continue to refer to Figure 3 As shown, the environmental analysis module further includes:

[0070] A third feature analysis unit for analyzing the internal and external difference features based on the indoor temperature information and the outdoor temperature. The third feature analysis unit is connected to the second feature analysis unit.

[0071] Specifically, in this embodiment, the third feature analysis unit analyzes the internal and external difference features based on the indoor temperature information and the outdoor temperature, sets the internal and external difference feature as H(j,i), and sets H(j,i)=[R1(j,i)+R2(j,i)-2×R4(j,i)] / [R3(j,i)-R4(j,i)].

[0072] Specifically, in this embodiment, the third feature analysis unit analyzes the indoor temperature information and the outdoor temperature to analyze the internal and external difference features, and uses the internal and external difference features to represent the temperature difference between the inside and outside of the box room, realizing the analysis of the temperature change relationship between the inside and outside in a short time, thereby improving the data analysis efficiency of the cold-resistant box room system and the accuracy of temperature management in the cold-resistant box room.

[0073] Please continue to refer to Figure 1 As shown, the cold-resistant box room management system based on the Internet of Things further includes:

[0074] A feature storage module for storing feature data to obtain historical feature information and analyzing the node feature types. The feature data includes device operation features, indoor environmental features, outdoor environmental features, and internal and external difference features. The feature storage module is connected to the device analysis module, the light analysis module, and the environmental analysis module.

[0075] Specifically, in this embodiment, the feature storage module extracts the current analysis data node and the first k data nodes before the current analysis data node as feature data nodes respectively, and takes the set of device operation features corresponding to the feature data nodes as the device feature set, the set of indoor environment features corresponding to the feature data nodes as the indoor feature set, and the set of outdoor environment features corresponding to the feature data nodes as the outdoor feature set, where k represents an extraction parameter, and 5 ≤ k ≤ 10. It can be understood that in this embodiment, no specific limitation is imposed on the value of the extraction parameter, and those skilled in the art can freely set it as long as the extraction of the feature data nodes is satisfied. The optimal value of the extraction parameter is: k = 6.

[0076] Specifically, in this embodiment, the feature storage module analyzes the historical fluctuation features according to the device feature set, the indoor feature set, and the outdoor feature set. Set the historical fluctuation feature as Q(j, i), and set Q(j, i) = [σ(U1(j, i)) / avg(U1(j, i)) + σ(U2(j, i)) / avg(U2(j, i)) + σ(U3(j, i)) / avg(U3(j, i))] / 3, where U1(j, i) represents the device feature set, U2(j, i) represents the indoor feature set, U3(j, i) represents the outdoor feature set, σ(U1(j, i)) represents the standard deviation of the device feature set, avg(U1(j, i)) represents the average value of the device feature set, σ(U2(j, i)) represents the standard deviation of the indoor feature set, avg(U2(j, i)) represents the average value of the indoor feature set, σ(U3(j, i)) represents the standard deviation of the outdoor feature set, and avg(U3(j, i)) represents the average value of the outdoor feature set.

[0077] Specifically, in this embodiment, the feature storage module analyzes the node feature type according to the historical fluctuation feature. If Q(j, i) < q, the feature storage module sets the node feature type of the current analysis data node to type one; otherwise, the feature storage module sets the node feature type of the current analysis data node to type two; where q represents the fluctuation threshold, and 0.05 ≤ q ≤ 0.1. It can be understood that in this embodiment, no specific limitation is imposed on the value of the fluctuation threshold, and those skilled in the art can freely set it as long as the analysis of the node feature type is satisfied. The optimal value of the fluctuation threshold is: q = 0.08.

[0078] Specifically, in this embodiment, through the extraction of the feature data points by the feature storage module, the analysis of the change fluctuations of various data features within a period of time is realized, so that the node feature type is divided into two categories according to the magnitude of the change fluctuations of the feature data within a period of time, increasing the diversity of system analysis, thereby improving the data analysis efficiency of the cold-resistant box room and improving the accuracy of temperature management in the cold-resistant box room.

[0079] Please continue to refer to Figure 1 As shown, the cold-resistant box house management system based on the Internet of Things further includes:

[0080] A matching analysis module for analyzing indoor management parameters according to the air exchange time, historical feature information, node feature type, and light type. The matching analysis module is connected to the feature storage module.

[0081] Please refer to Figure 4 As shown, the matching analysis module includes:

[0082] A feature data analysis unit for analyzing data node features according to the air exchange time, device operation characteristics, indoor environment characteristics, and outdoor environment characteristics.

[0083] Specifically, in this embodiment, the feature data analysis unit analyzes the data node features according to the air exchange time, device operation characteristics, indoor environment characteristics, and outdoor environment characteristics, sets the data node feature as F(j,i), and sets , where m represents the feature analysis parameter, m ∈ N, m ≤ M, M represents the feature range parameter, M = T(j,i) / η, and M is rounded down.

[0084] Specifically, in this embodiment, through the analysis of the air exchange time, device operation characteristics, indoor environment characteristics, and outdoor environment characteristics by the feature data analysis unit, the data node features are analyzed, and the feature relationship between the indoor and outdoor environments of the box house and the device operation data is represented by the data node features, thereby improving the data analysis efficiency of the system for the cold-resistant box house and improving the accuracy of temperature management in the cold-resistant box house.

[0085] Please continue to refer to Figure 4 As shown, the matching analysis module further includes:

[0086] A node type analysis unit for adjusting the analysis process of data node features according to the node feature type and internal and external difference features. The node type analysis unit is connected to the feature data analysis unit.

[0087] Specifically, in this embodiment, the node type analysis unit extracts the historical feature information that satisfies h1 ≤ H(j,i) / H(j ∈ N +,n) ≤ h2 of the non-current analysis internal and external difference features as the type analysis difference features, and count the number of the node feature types corresponding to the day numbers and node numbers of the type analysis difference features as one category as the first node type quantity, count the number of the node feature types corresponding to the day numbers and node numbers of the type analysis difference features as two categories as the second node type quantity, and adjust the analysis process of the data node features according to the first node feature quantity and the second node feature quantity. If N1(j,i) / [N1(j,i) + N2(j,i)] ≤ γ, the node type analysis unit adjusts the analysis process of the data node features, and the adjusted data node feature is F1(j,i), and set F1(j,i) = F(j,i) × AH; otherwise, the node type analysis unit does not adjust the analysis process of the data node features; where, h1 represents the first historical extraction parameter, 0.8 ≤ h1 < 1, h2 represents the second historical extraction parameter, 1 < h2 ≤ 1.2, H(j ∈ N + ,n) represents the non-current analysis internal and external difference features, n represents the non-current analysis node number, n ∈ N + , n ≤ 1440 / η, N1(j,i) represents the first node type quantity, N2(j,i) represents the second node type quantity, γ represents the node type quantity threshold, 0.5 ≤ γ ≤ 0.8, AH represents the adjustment parameter, , UH represents the set of the day numbers and node numbers corresponding to the type analysis difference features. It can be understood that in this embodiment, the values of the historical extraction parameter and the node type quantity threshold are not specifically limited, and those skilled in the art can freely set them as long as the adjustment of the analysis process of the data node features is satisfied. The optimal values of the historical extraction parameter and the node type quantity threshold are: h1 = 0.9, h2 = 1.1, γ = 0.6.

[0088] Specifically, when the node type analysis unit extracts the type analysis difference features in this embodiment, it respectively compares and analyzes each item in the historical feature information with the internal and external difference features whose day numbers and node numbers are different from those of the current analysis internal and external difference features.

[0089] Specifically, in this embodiment, through the analysis of the node feature type and the internal and external difference features by the node type analysis unit, the internal and external difference features similar to the current analysis data in the historical feature information are extracted, and the first node type quantity and the second node type quantity are analyzed, so as to adjust the analysis process of the data node features, make the adjusted data node features related to the internal and external difference features of other data nodes similar to it, and when the internal and external difference features are less different, adjust the data node features, thereby improving the data analysis efficiency of the system for the cold-resistant box room and improving the accuracy of the temperature management in the cold-resistant box room.

[0090] Please continue to refer to Figure 4 As shown, the matching analysis module further includes:

[0091] A lighting feature analysis unit for optimizing the adjustment process of data node features according to the lighting type. The lighting feature analysis unit is connected to the node type analysis unit.

[0092] Specifically, in this embodiment, the lighting feature analysis unit optimizes the adjustment process of data node features according to the lighting type. If the lighting type is of one category, the lighting feature analysis unit optimizes the adjustment process of data node features, and the optimized data node feature is F2(j, i). It is set that F2(j, i) = F1(j, i) × e lgL1(j,i) / α ; if the lighting type is of two categories, the lighting feature analysis unit does not optimize the adjustment process of data node features.

[0093] Specifically, in this embodiment, through the analysis of the lighting type by the lighting feature analysis unit, the adjustment process of node features is optimized, so that the optimized data node features are related to the indoor and outdoor lighting intensities, reducing the influence of solar heat radiation on the indoor temperature, thereby improving the data analysis efficiency of the system for cold-resistant box houses and improving the accuracy of indoor temperature management for cold-resistant box houses.

[0094] Please continue to refer to Figure 4 As shown, the matching analysis module further includes:

[0095] A management parameter analysis unit for storing data node features and analyzing indoor management parameters according to the data node features. The management parameter analysis unit is connected to the lighting feature analysis unit.

[0096] Specifically, in this embodiment, the management parameter analysis unit extracts the node feature types and lighting types that are the same as or satisfy h1 ≤ F(j, i) / F(j ∈ N + , n) ≤ h2 of the currently analyzed data node features as management node features, and analyzes the indoor management parameters according to the management node features, and sets the indoor management parameter as X(j, i). It is set that , where UF represents the set of day numbers and node numbers corresponding to the management node features, and NUF represents the number of management node features.

[0097] Specifically, in this embodiment, the management parameter analysis unit extracts the characteristics of the data nodes to extract data similar to the current analyzed data node characteristic data or environmental data, so as to analyze the indoor management parameters, and uses the indoor management parameters to represent the fluctuation characteristic relationship between the current indoor data and the extracted data, realizing the analysis of data similarity, thereby improving the data analysis efficiency of the system for cold-resistant container houses and improving the accuracy of temperature management in cold-resistant container houses.

[0098] Please continue to refer to Figure 1 As shown, the cold-resistant container house management system based on the Internet of Things further includes:

[0099] A container house management module for managing the operation of the heating equipment in the cold-resistant container house according to the indoor management parameters. The container house management module is connected to the matching analysis module.

[0100] Specifically, in this embodiment, the container house management module controls the power of the heating equipment in the cold-resistant container house according to the indoor management parameters. If X(j,i) ≤ x, the container house management module adjusts the power of the heating equipment in the cold-resistant container house to NP(j,i); otherwise, the container house management module does not adjust the power of the heating equipment in the cold-resistant container house; where x represents the management threshold, 0.1 ≤ x ≤ 0.2, and NP represents the power adjustment parameter. 。

[0101] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. A cold-resistant container-type house management system based on the Internet of Things, characterized in that: include: The container house data collection module is used to collect the indoor environment information, outdoor environment information, indoor equipment operation information and container house volume of the cold-resistant container house in real time; A node construction module is used to construct data nodes according to a preset analysis period and number the data nodes to obtain data numbers; The equipment analysis module is used to analyze the air exchange time according to the volume of the box room, the air volume of the heating equipment and the data number, and is also used to analyze the equipment operation characteristics according to the temperature of the heating equipment, the power of the heating equipment and the total power of the electrical equipment; Light analysis module, used to determine the light type based on the light intensity inside the house and the light intensity outside the house; An environmental analysis module, for analyzing indoor environmental characteristics according to indoor temperature information and data serial number, and for analyzing outdoor environmental characteristics according to outdoor temperature, outdoor wind speed and outdoor light intensity, and for analyzing indoor and outdoor difference characteristics according to indoor temperature information and outdoor temperature, wherein the indoor temperature information includes measured temperature and measurement position; A feature storage module is used to store feature data to obtain historical feature information and analyze node feature types, wherein the node feature types include one and two types, and the feature data include equipment operation features, indoor environment features, outdoor environment features, and internal and external difference features; A matching analysis module analyzes data node characteristics according to air exchange time, equipment operation characteristics, indoor environment characteristics and outdoor environment characteristics, and adjusts the analysis process of data node characteristics according to node characteristic type and internal and external difference characteristics, and optimizes the adjustment process of data node characteristics according to illumination type. It is also used to store data node characteristics and analyze indoor management parameters according to data node characteristics; The container room management module is used to manage the operation of heating equipment in the cold-resistant container room according to indoor management parameters.

2. The cold-resistant container-type house management system based on the Internet of Things according to claim 1 is characterized in that: The equipment analysis module is provided with an air exchange analysis unit, which is used to analyze the air exchange time according to the volume V of the box-type room and the air volume C(j,i) of the heating equipment to obtain the air exchange time T(j,i), where i represents the node number and j represents the day number; The equipment analysis module is also provided with an equipment operation analysis unit, which is used to analyze the equipment operation characteristics according to the heating equipment temperature W(j,i), the heating equipment power P1(j,i) and the total power of the electrical equipment P2(j,i) to obtain the equipment operation characteristics A(j,i).

3. The cold-resistant container-type house management system based on the Internet of Things according to claim 2 is characterized in that: The illumination analysis module determines the illumination type of the current analysis data node according to the indoor illumination intensity L1(j,i) and the outdoor illumination intensity L2(j,i), and the illumination type includes one type and two types.

4. The cold-resistant container-type house management system based on the Internet of Things according to claim 3 is characterized in that: The environmental analysis module is provided with a first characteristic analysis unit, which is used to analyze the indoor environmental characteristics according to the measured temperatures R1(j,i), R2(j,i), and R3(j,i) to obtain the indoor environmental characteristics S(j,i); The environmental analysis module is also provided with a second characteristic analysis unit, which is used to analyze the outdoor environmental characteristics according to the outdoor temperature R4(j,i), the outdoor wind speed v(j,i) and the outdoor light intensity L2(j,i) to obtain the outdoor environmental characteristics G(j,i).

5. The cold-resistant container-type house management system based on the Internet of Things according to claim 4 is characterized in that: The feature storage module extracts the current analysis data node and the k data nodes before the current analysis data node as feature data nodes, and uses the set of equipment operation features corresponding to the feature data nodes as the equipment feature set, the set of indoor environment features corresponding to the feature data nodes as the indoor feature set, and the set of outdoor environment features corresponding to the feature data nodes as the outdoor feature set, wherein k represents an extraction parameter; The feature storage module analyzes the historical fluctuation features according to the device feature set, the indoor feature set and the outdoor feature set to obtain the historical fluctuation features Q(j,i), and determines the node feature type of the current analysis data node according to the historical fluctuation features.

6. The cold-resistant container-type house management system based on the Internet of Things according to claim 5 is characterized in that: The matching analysis module is also provided with a node type analysis unit, which is used to extract the historical feature information that satisfies h1≤H(j,i) / H(j∈N + ,n)≤h2 as the type analysis difference feature, and count the number of node feature types corresponding to the day number and node number of the type analysis difference feature as the first node type number, and count the number of node feature types corresponding to the day number and node number of the type analysis difference feature as the second node type number, and adjust the analysis process of the data node feature according to the first node feature number N1(j,i) and the second node feature number N2(j,i), and when N1(j,i) / [N1(j,i)+N2(j,i)]≤γ, adjust the analysis process of the data node feature, and the adjusted data node feature is F1(j,i); where h1 represents the first historical extraction parameter, h2 represents the second historical extraction parameter, and H(j∈N + ,n) represents the difference characteristics between the internal and external aspects of the non-current analysis, n represents the node number of the non-current analysis, and γ represents the node type quantity threshold.

7. The cold-resistant container-type house management system based on the Internet of Things according to claim 6 is characterized in that: The matching analysis module is also provided with an illumination feature analysis unit, which is used to optimize the adjustment process of the data node features according to the illumination type. When the illumination type is one type, the illumination feature analysis unit optimizes the adjustment process of the data node features, and the optimized data node features are F2(j,i).

8. The cold-resistant container-type house management system based on the Internet of Things according to claim 7 is characterized in that: The matching analysis module is also provided with a management parameter analysis unit, which is used to extract node feature types and illumination types that are the same as the current analysis data node features and satisfy h1≤F(j,i) / F(j∈N + ,n)≤h2 are used as management node features, and the indoor management parameters are analyzed according to the management node features to obtain the indoor management parameters X(j,i).

9. The cold-resistant container-type house management system based on the Internet of Things according to claim 8 is characterized in that: The box-type room management module controls the power of the heating equipment in the cold-resistant box-type room according to the indoor management parameters. If X(j,i)≤x, the box-type room management module adjusts the power of the heating equipment in the cold-resistant box-type room to NP(j,i); otherwise, the box-type room management module does not adjust the power of the heating equipment in the cold-resistant box-type room; wherein x represents the management threshold and NP represents the power adjustment parameter.

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