Cold-resistant box-type house management system based on Internet of Things

Abstract

The invention relates to the technical field of box-type house management, in particular to a cold-resistant box-type house management system based on the Internet of Things, and the system comprises a box-type house data collection module which is used for collecting the indoor environment information, the outdoor environment information, the indoor equipment operation information and the volume of a cold-resistant box-type house in real time; the node construction module is used for constructing data nodes and numbering the data nodes to obtain data numbers; the equipment analysis module is used for analyzing the air exchange time and the equipment operation characteristics; the illumination analysis module is used for analyzing the illumination type; the environment analysis module is used for analyzing the environment characteristics and the internal and external difference characteristics; the feature storage module is used for analyzing node feature types; the matching analysis module is used for analyzing the indoor management parameters; and the box-type room management module is used for managing operation of heating equipment in the cold-resistant box-type room. According to the invention, accurate management of operation of the heating equipment in the box-type room is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of container house management, and in particular to a cold-resistant container house management system based on the Internet of Things. Background Art

[0002] Temperature management in box rooms mainly focuses on ensuring the stability and comfort of equipment or living environment. By installing temperature sensors, temperature controllers and cooling ventilation systems, the temperature in the box room can be monitored and adjusted in real time to cope with temperature changes under different climatic conditions, thereby ensuring stable operation of equipment or providing a comfortable living environment.

[0003] Chinese Patent Publication No.: CN113341844A discloses an intelligent container-type room control system based on the Internet of Things technology, including an application layer, a transmission layer, and a perception layer. The perception layer is an Internet of Things terminal device arranged in container-type rooms in different areas, which monitors various data of the container-type rooms in real time; the application layer and the perception layer realize data interaction through the transmission layer, and the cloud server in the perception layer receives the monitoring data and stores and processes it. The processing results are passed to the application layer, and the application layer issues control instructions to control the operation of the Internet of Things devices to realize remote centralized intelligent control of the container-type room. This invention only realizes the storage supervision of various collected data in the container-type room, and does not realize the precise management of the temperature in the container-type room by comprehensively collecting the equipment operation data and the environmental data inside and outside the container-type room. There are low efficiency in data analysis of cold-resistant container-type rooms and inaccurate temperature management in cold-resistant container-type rooms. Summary of the invention

[0004] The object of the present invention is to provide a cold-resistant container-type room 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 object, the present invention adopts the following technical solutions:

[0006] A cold-resistant container-type house management system based on the Internet of Things, comprising:

[0007] 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;

[0008] 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;

[0009] Equipment analysis module, used to analyze air exchange time and equipment operation characteristics based on indoor equipment operation information, box room volume and data number;

[0010] A light analysis module is used to analyze the light type according to indoor environment information, outdoor environment information and data number;

[0011] The environmental analysis module is used to analyze the environmental characteristics and the difference characteristics between inside and outside according to the indoor environmental information, outdoor environmental information and data number;

[0012] A feature storage module is used to store feature data to obtain historical feature information and analyze node feature types;

[0013] Matching analysis module, used to analyze indoor management parameters according to air exchange time, historical feature information, node feature type and lighting type;

[0014] 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.

[0015] Furthermore, 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);

[0016] 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).

[0017] Furthermore, 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.

[0018] Furthermore, the environment analysis module is provided with a first characteristic analysis unit, which is used to analyze the indoor environment characteristics according to the measured temperatures R1(j,i), R2(j,i), and R3(j,i) to obtain the indoor environment characteristics S(j,i);

[0019] The environmental analysis module is further 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);

[0020] The environmental analysis module is further provided with a third feature analysis unit, which is used to analyze the inside-outside difference feature according to the indoor temperature information and the outdoor temperature to obtain the inside-outside difference feature H(j,i).

[0021] Further, 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;

[0022] 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, and the node feature types include one category and two categories.

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

[0024] Furthermore, the matching analysis module is further provided with a node type analysis unit, which is used to extract the nodes satisfying 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.

[0025] Furthermore, 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).

[0026] Furthermore, 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).

[0027] Furthermore, the container-type room management module controls the power of the heating equipment in the cold-resistant container-type room according to the indoor management parameters. If X(j,i)≤x, the container-type room management module adjusts the power of the heating equipment in the cold-resistant container-type room to NP(j,i); otherwise, the container-type room management module does not adjust the power of the heating equipment in the cold-resistant container-type room; wherein 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 indoor environmental information, outdoor environmental information, indoor equipment operation information and container-type room volume of the cold-resistant container-type room by the container-type room data collection module, and analysis of the collected data by other modules, a comprehensive analysis of the container-type room indoor environmental data, container-type room outdoor environmental data and container-type room equipment operation data on the container-type room temperature characteristics is achieved, and the relationship between the operation of the heating equipment in the container-type room and the temperature in the container-type room is judged by comprehensively analyzing various data to control the temperature in the container-type room to be stable and appropriate, thereby improving the efficiency of the system's data analysis of the cold-resistant container-type room and improving the accuracy of the temperature management in the cold-resistant container-type room. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 This is a schematic diagram of the structure of the cold-resistant container-type room management system based on the Internet of Things in this embodiment.

[0031] Figure 2 Schematic diagram of the structure of the equipment analysis module in this embodiment.

[0032] Figure 3 Schematic diagram of the structure of the environmental analysis module of this embodiment.

[0033] Figure 4 Schematic diagram of the structure of the matching analysis module in this embodiment. DETAILED DESCRIPTION

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

[0035] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, 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] See also Figure 1 As shown, it is a cold-resistant container-type house management system based on the Internet of Things in this embodiment, including:

[0037] The container-type house data acquisition module is used to collect indoor environmental information, outdoor environmental information, indoor equipment operation information and container-type house volume of the cold-resistant container-type house in real time. The indoor environmental information includes indoor temperature information and indoor light intensity. The indoor temperature information includes measured temperature and measurement position. The indoor temperature information should meet the measurement temperature of at least three measurement positions. The measurement position should meet the indoor door, indoor window and the top of the container-type house. At least one temperature sensor is set at each position to measure the indoor temperature data. The indoor light intensity is the light intensity of natural light. The outdoor environmental information includes outdoor temperature, outdoor wind speed and outdoor light intensity. The indoor light intensity and outdoor light intensity are both the light intensity of natural light. The indoor equipment operation information includes heating equipment operation information and total power of electrical equipment. The heating equipment operation information includes heating equipment temperature, heating equipment The power and 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, the electrical equipment is equipment that needs power supply to operate in the box-type room, the total power of the electrical equipment is the sum of the powers of all electrical equipment in the box-type room, the temperature of the heating equipment is the temperature of the air outlet of the heating equipment, the unit of the power of the heating equipment and the total power of the electrical equipment is watt, the unit of light intensity in this embodiment is lux, the unit of temperature is 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 indoor environmental information and the outdoor environmental information are collected and uploaded by sensors installed inside and outside the house, the indoor equipment information is collected by importing data from the box-type room electrical equipment operation management platform, and the volume of the box-type room is collected by user interactive input.

[0038] Specifically, this embodiment is applied to the cloud equipment management platform of the cold-resistant box-type room to control the operation of the heating equipment when the temperature difference between indoor and outdoor of the cold-resistant box-type room in cold areas is large, to ensure that the temperature in the cold-resistant box-type room is appropriate and meet user needs.

[0039] Please continue reading Figure 1 As shown, the cold-resistant container-type house management system based on the Internet of Things also includes:

[0040] The node construction module is used to construct data nodes according to a preset analysis cycle and number the data nodes to obtain a data number, wherein the data number includes a node number and a day number. The node construction module is connected to the container-type room data acquisition module.

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

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

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

[0044] Specifically, in this embodiment, the node construction module analyzes the preset analysis cycle to construct data nodes, and the data nodes are matched one by one with each time of the day to ensure the regularity of the collected data. When the collected data is subsequently analyzed, the data used is clearer and more specific, thereby improving the system's data analysis efficiency for cold-resistant container rooms and improving the accuracy of temperature management in cold-resistant container rooms.

[0045] Please continue reading Figure 1 As shown, the cold-resistant container-type house management system based on the Internet of Things also includes:

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

[0047] See also Figure 2 As shown, the device analysis module includes:

[0048] The air exchange analysis unit 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.

[0049] Specifically, the air exchange analysis unit described in this embodiment analyzes the air exchange time according to the volume of the box-type room and the air volume of the heating equipment, sets the air exchange time to T(j,i), and sets T(j,i)=V / C(j,i), wherein V represents the volume of the box-type room, and C(j,i) represents the air volume of the heating equipment.

[0050] Specifically, in this embodiment, the air exchange analysis unit analyzes the volume of the box-type room and the air volume of the heating equipment to analyze the air exchange time. The air exchange time is used to represent the estimated time required to update the indoor air when the heating equipment is turned on, thereby increasing the diversity of system analysis and realizing analysis of indoor air exchange, thereby improving the system's data analysis efficiency for cold-resistant box-type rooms and improving the accuracy of temperature management in cold-resistant box-type rooms.

[0051] Please continue reading Figure 2 As shown, the device analysis module also includes:

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

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

[0054] Specifically, in this embodiment, the equipment operation information is analyzed by the equipment operation analysis unit to analyze the equipment operation characteristics, and the equipment operation characteristics are used to represent the characteristic relationship between the current heating equipment operation data and the comprehensive electricity consumption data in the container-type room, so as to achieve a comprehensive analysis of the characteristics of the electrical equipment in the container-type room, thereby improving the system's data analysis efficiency for cold-resistant container-type rooms and improving the accuracy of temperature management in cold-resistant container-type rooms.

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

[0056] The illumination analysis module is used to analyze the illumination type according to the indoor environment information, the outdoor environment information and the data number, and the illumination analysis module is connected with the node construction module.

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

[0058] Specifically, in this embodiment, the light analysis module analyzes the indoor light intensity and the outdoor light intensity to divide each data node into two categories according to the light intensity within its analysis period, thereby realizing the impact of direct natural light on the indoor and outdoor environment analysis, thereby improving the system's data analysis efficiency for cold-resistant container rooms and improving the accuracy of temperature management in cold-resistant container rooms.

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

[0060] The environment analysis module is used to analyze the environment characteristics and the internal and external difference characteristics according to the indoor environment information, the outdoor environment information and the data number. The environment characteristics include the indoor environment characteristics and the outdoor environment characteristics. The environment analysis module is connected with the node construction module.

[0061] See also Figure 3 As shown, the environmental analysis module includes:

[0062] The first characteristic analysis unit is used to analyze the indoor environment characteristics according to the indoor temperature information and the data number.

[0063] Specifically, in this embodiment, the first feature analysis unit analyzes the indoor environment characteristics according to the indoor temperature information, sets the indoor environment characteristics 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 temperature measured at the door of the house, R2(j,i) represents the temperature measured at the window of the house, and R3(j,i) represents the temperature measured at the top of the box-type house.

[0064] Specifically, in this embodiment, the indoor temperature information is analyzed by the first feature analysis unit to analyze the indoor environmental characteristics, and the indoor environmental characteristics are used to represent the temperature characteristic relationship between different temperature collection points in the box-type room, so as to realize the analysis of the difference between the temperature data of doors and windows where air exchange may exist in the box-type room and the overall temperature data of the box-type room, thereby improving the system's data analysis efficiency for cold-resistant box-type rooms and improving the accuracy of temperature management in cold-resistant box-type rooms.

[0065] Please continue reading Figure 3 As shown, the environmental analysis module also includes:

[0066] The second feature analysis unit is used to analyze the outdoor environment characteristics according to the outdoor environment 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 environment characteristics according to the outdoor temperature, outdoor wind speed and outdoor light intensity, and sets the outdoor environment characteristics 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 the calculation parameter, and 10<β≤13. It can be understood that the values ​​of the calculation parameters are not specifically limited in this embodiment, and those skilled in the art can freely set them as long as they meet the analysis of the outdoor environment characteristics. 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 characteristics, and uses the outdoor environmental characteristics to represent the environmental characteristics analysis realized by comprehensive outdoor environmental data, thereby increasing the diversity of system analysis, thereby improving the system's data analysis efficiency for cold-resistant container-type houses and improving the accuracy of temperature management in cold-resistant container-type houses.

[0069] Please continue reading Figure 3 As shown, the environmental analysis module also includes:

[0070] The third feature analysis unit is used to analyze the difference characteristics between the inside and outside according to the indoor temperature information and the outdoor temperature. The third feature analysis unit is connected to the second feature analysis unit.

[0071] Specifically, the third feature analysis unit described in this embodiment analyzes the inside-outside difference characteristics according to the indoor temperature information and the outdoor temperature, sets the inside-outside difference characteristics to 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 inside-outside difference characteristics, and uses the inside-outside difference characteristics to represent the temperature difference between the indoor and outdoor of the container-type room, thereby realizing the analysis of the relationship between indoor and outdoor temperature changes in a short period of time, thereby improving the system's data analysis efficiency for cold-resistant container-type rooms and improving the accuracy of temperature management in cold-resistant container-type rooms.

[0073] Please continue reading Figure 1 As shown, the cold-resistant container-type house management system based on the Internet of Things also includes:

[0074] The feature storage module is used to store feature data to obtain historical feature information and analyze node feature types. The feature data includes equipment operation features, indoor environment features, outdoor environment features and internal and external difference features. The feature storage module is connected to the equipment analysis module, the lighting analysis module and the environment analysis module.

[0075] Specifically, the feature storage module in this embodiment 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 the extraction parameter, 5≤k≤10. It can be understood that the value of the extraction parameter is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the extraction of the feature data node is satisfied. The optimal value of the extraction parameter is: k=6.

[0076] Specifically, the feature storage module in this embodiment analyzes the historical fluctuation characteristics according to the device feature set, the indoor feature set and the outdoor feature set, sets the historical fluctuation characteristics as Q(j,i), and sets 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, the feature storage module in this embodiment analyzes the node feature type according to the historical fluctuation characteristics. If Q(j,i)<q, the feature storage module sets the node feature type of the current analysis data node to the first category; otherwise, the feature storage module sets the node feature type of the current analysis data node to the second category; wherein q represents the fluctuation threshold, 0.05≤q≤0.1. It can be understood that the value of the fluctuation threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the node feature type. The optimal value of the fluctuation threshold is: q=0.08.

[0078] Specifically, in this embodiment, the feature storage module extracts feature data points to realize the analysis of the fluctuation of changes in various data features over a period of time, thereby dividing the node feature types into two categories according to the size of the fluctuation of feature data changes over a period of time, thereby increasing the diversity of system analysis, thereby improving the system's data analysis efficiency for cold-resistant container rooms, and improving the accuracy of temperature management in cold-resistant container rooms.

[0079] Please continue reading Figure 1 As shown, the cold-resistant container-type house management system based on the Internet of Things also includes:

[0080] The matching analysis module is used to analyze indoor management parameters according to air exchange time, historical feature information, node feature type and illumination type, and the matching analysis module is connected to the feature storage module.

[0081] See also Figure 4 As shown, the matching analysis module includes:

[0082] The characteristic data analysis unit is used to analyze the characteristics of the data node according to the air exchange time, the equipment operation characteristics, the indoor environment characteristics and the outdoor environment characteristics.

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

[0084] Specifically, in this embodiment, the characteristic data analysis unit analyzes the air exchange time, equipment operation characteristics, indoor environment characteristics and outdoor environment characteristics to analyze the data node characteristics, and uses the data node characteristics to represent the characteristic relationship between the indoor and outdoor environment of the container-type room and the equipment operation data, thereby improving the system's data analysis efficiency for cold-resistant container-type rooms and improving the accuracy of temperature management in cold-resistant container-type rooms.

[0085] Please continue reading Figure 4 As shown, the matching analysis module also includes:

[0086] The node type analysis unit is used to adjust the analysis process of the data node characteristics according to the node characteristic type and the internal and external difference characteristics. The node type analysis unit is connected to the characteristic data analysis unit.

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

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

[0089] Specifically, in this embodiment, the node type analysis unit analyzes the node feature type and the internal and external difference features to extract the internal and external difference features similar to the current analysis data in the historical feature information, and analyzes the first node type number and the second node type number, so as to adjust the analysis process of the data node features, so that the adjusted data node features are related to the internal and external difference features of other similar data nodes. When the difference in internal and external difference features is small, the data node features are adjusted to improve the system's data analysis efficiency for cold-resistant container rooms and improve the accuracy of temperature management in cold-resistant container rooms.

[0090] Please continue reading Figure 4 As shown, the matching analysis module also includes:

[0091] The illumination feature analysis unit is used to optimize the adjustment process of the data node features according to the illumination type, and the illumination feature analysis unit is connected to the node type analysis unit.

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

[0093] Specifically, in this embodiment, the lighting type is analyzed by the lighting feature analysis unit to optimize the node feature adjustment process, so that the optimized data node features are related to the indoor and outdoor light intensity, reducing the impact of solar thermal radiation on indoor temperature, thereby improving the system's data analysis efficiency for cold-resistant container rooms and improving the accuracy of temperature management in cold-resistant container rooms.

[0094] Please continue reading Figure 4 As shown, the matching analysis module also includes:

[0095] The management parameter analysis unit is used to store data node characteristics and analyze indoor management parameters according to the data node characteristics. The management parameter analysis unit is connected to the illumination characteristic analysis unit.

[0096] Specifically, the node feature type and illumination type extracted by the management parameter analysis unit in this embodiment are the same as the node feature of the current analysis data or satisfy h1≤F(j,i) / F(j∈N + ,n)≤h2 other stored data node features as management node features, and analyze the indoor management parameters according to the management node features, set the indoor management parameters to X(j,i), set , 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 data node features to extract data that is similar to the current analysis data node feature data or environmental data, thereby analyzing indoor management parameters, and using indoor management parameters to represent the fluctuation characteristic relationship between the current indoor data and the extracted data to achieve data similarity analysis, thereby improving the system's data analysis efficiency for cold-resistant container rooms and improving the accuracy of temperature management in cold-resistant container rooms.

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

[0099] The container-type room management module is used to manage the operation of the heating equipment in the cold-resistant container-type room according to the indoor management parameters. The container-type room management module is connected to the matching analysis module.

[0100] Specifically, the box-type room management module in this embodiment 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, 0.1≤x≤0.2, 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 limitations on the embodiments of the present invention. For those skilled 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 embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still 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; Equipment analysis module, used to analyze air exchange time and equipment operation characteristics based on indoor equipment operation information, box room volume and data number; A light analysis module is used to analyze the light type according to indoor environment information, outdoor environment information and data number; The environmental analysis module is used to analyze the environmental characteristics and the difference characteristics between inside and outside according to the indoor environmental information, outdoor environmental information and data number; A feature storage module is used to store feature data to obtain historical feature information and analyze node feature types; Matching analysis module, used to analyze indoor management parameters according to air exchange time, historical feature information, node feature type and lighting type; 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); 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 further 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); The environmental analysis module is further provided with a third feature analysis unit, which is used to analyze the inside-outside difference feature according to the indoor temperature information and the outdoor temperature to obtain the inside-outside difference feature H(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, and the node feature types include one category and two categories.

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 provided with a characteristic data analysis unit, which is used to analyze the data node characteristics according to the air exchange time T(j,i), the equipment operation characteristics A(j,i), the indoor environment characteristics S(j,i) and the outdoor environment characteristics G(j,i) to obtain the data node characteristics F(j,i).

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 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.

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 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).

9. The cold-resistant container-type house management system based on the Internet of Things according to claim 8 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).

10. The cold-resistant container-type house management system based on the Internet of Things according to claim 9 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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