Multi-cabin distributed collaborative interaction system based on intelligent cabin and method thereof

Abstract

The invention discloses a multi-cabin distributed collaborative interaction system based on smart cabins and a method thereof, and particularly relates to the field of management and control of camping smart cabins. The system is formed by integrating a plurality of smart cabin units, each smart cabin has independent sensing, control and communication capabilities, and collaborative work among the multiple cabins is realized through a distributed network architecture; the system collects cabin internal and external environment data and energy consumption data through a sensor to carry out intelligent cabin state division; and through cooperative linkage with other intelligent cabins or equipment through the Internet of Things technology, the user experience and the energy utilization efficiency are improved, intelligent distribution, cooperative interaction and self-adaptive adjustment among the intelligent cabins are realized, and an intelligent and comfortable living space is provided for camping people.

Description

Technical Field

[0001] The present invention relates to the field of management and control of camping smart cabins, and in particular to a multi-cabin distributed collaborative interaction system based on smart cabins and a method thereof. Background Art

[0002] Multi-cabin distributed collaborative interaction based on smart cabins refers to multiple smart cabin units connected to each other through a distributed network architecture to achieve data sharing and collaborative operation. Each smart cabin independently collects environmental data inside and outside the cabin, such as temperature, humidity, air quality, etc., and automatically adjusts the cabin environment according to these data and user preferences. At the same time, these smart cabins collaborate through communication modules to share resources and optimize energy utilization, thereby providing a consistent comfort experience and efficient resource management throughout the camping area. This interactive mode ensures intelligent control, resource sharing, and cross-device linkage management. With the increasing popularity of camping activities, modern campers' demand for intelligence and comfort has gradually increased. However, traditional camping cabins cannot meet users' requirements for environmental control, energy management, and cross-device collaboration. The multi-cabin distributed collaborative interaction system based on smart cabins and its method aim to solve the problems of inconvenient environmental control and low energy utilization during camping by using the collaborative work between smart cabins through the multi-cabin distributed collaborative interaction system, and provide campers with an intelligent camping experience. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-warehouse distributed collaborative interaction system and method based on a smart cabin to address the deficiencies in the background technology.

[0004] In order to achieve the above object, the present invention provides the following technical solution: a multi-warehouse distributed collaborative interaction method based on a smart cabin, comprising the following steps:

[0005] S1: Each smart cabin unit is equipped with several sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time;

[0006] S2: Connect with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing. The shared data includes the environmental parameters and energy consumption data of each cabin.

[0007] S3: Comprehensively analyze the environmental parameters and energy consumption data of each cabin, evaluate the stability of each cabin state, and divide it into stable state and unstable state;

[0008] S4: The system optimizes the energy distribution of the unstable smart cabin, and transmits the optimization effect to the information exchange among the smart cabins to achieve linkage control and status feedback.

[0009] Preferably, in S2, the shared data includes the environmental parameters and energy consumption data of each cabin, wherein the environmental parameters include the cabin internal and external temperature balance index, and the method for obtaining the cabin internal and external temperature balance index is: using a sensor to collect the cabin temperature T in real time in and the outside temperature T out , and record the current user-set comfort temperature T comfort , calculate the difference between the temperature inside the cabin and the temperature outside the cabin, ΔT in-out , used to indicate the influence of the external environment on the temperature inside the cabin, the expression is: ΔT in-out =T in -T out ; Calculate the difference between the cabin temperature and the comfort temperature set by the user, the expression is: ΔT in-comfort =T in -T comfort ; Calculate the difference between the outside temperature and the comfort temperature, the expression is: ΔT out-comfort =T out -T comfort ; Calculate the temperature balance index QS inside and outside the cabin, the expression is:

[0010] Preferably, the shared data includes environmental parameters and energy consumption data of each cabin, wherein the energy consumption data is an abnormal power supply index, and the method for obtaining the abnormal power supply index is:

[0011] Collect the real-time power consumption data of the smart cabin, and calculate the actual energy supply rate R of the smart cabin based on the collected data actual Calculate the power supply rate deviation value: Calculate the power supply anomaly index based on the power supply rate and the anomaly degree weight factor. The specific calculation expression is: Among them, pg x is the power supply anomaly index, R ideal represents the ideal power supply rate, w represents the abnormality weight factor; R actual Indicates the actual power supply rate of the smart cabin.

[0012] Preferably, in S3, a comprehensive analysis is performed on the environmental parameters and energy consumption data of each cabin to evaluate the stability of the state of each cabin, specifically:

[0013] The temperature balance index inside and outside the cabin and the power supply abnormality index are normalized, and the stability coefficient of the smart cabin state is calculated by the normalized temperature balance index inside and outside the cabin and the power supply abnormality index. The calculation expression is: Where MP is the stability coefficient of the smart cabin state, QS is the temperature balance index inside and outside the cabin, and pg xis the power supply abnormality index, a1 and a2 are the proportional coefficients of the temperature balance index inside and outside the cabin and the power supply abnormality index, and a2>a1>0.

[0014] Preferably, the acquired stability coefficient of the smart cabin state is compared with a reference threshold of the stability coefficient of the smart cabin state. If the stability coefficient of the smart cabin state is greater than or equal to the reference threshold of the stability coefficient of the smart cabin state, it indicates that the stability of the smart cabin state is high. At this time, no warning signal is generated, and it is classified as a stable state. If the stability coefficient of the smart cabin state is less than the reference threshold of the stability coefficient of the smart cabin state, it indicates that the stability of the smart cabin state is low. At this time, a warning signal is generated, and it is classified as an unstable state.

[0015] The present invention also provides a multi-warehouse distributed collaborative interaction system based on the smart cabin, including a data acquisition module, a data sharing module, a stability analysis module and an interaction optimization module;

[0016] Data acquisition module: Each smart cabin unit is equipped with a number of sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time; data sharing module: It connects with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, which is used for real-time data sharing among smart cabins. The shared data includes the environmental parameters and energy consumption data of each cabin; stability analysis module: It conducts a comprehensive analysis of the environmental parameters and energy consumption data of each cabin, evaluates the stability of each cabin state, and divides it into stable and unstable states; interactive optimization module: The system optimizes the energy distribution of smart cabins in unstable states, and exchanges the optimization results among the smart cabins to achieve linkage control and state feedback.

[0017] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0018] 1. Through the environment sensing network and wireless communication technology, each smart cabin can collect and share the environmental data and energy consumption data inside and outside the cabin in real time. Based on these data, the system conducts a comprehensive analysis of the status of each cabin, automatically evaluates its stability and optimizes energy allocation, thereby ensuring the environmental stability and energy efficiency of each smart cabin. This method solves the limitations of traditional camping cabins in environmental control and energy management through data sharing and collaborative work between cabins, providing campers with a more comfortable and intelligent experience.

[0019] 2. The present invention realizes accurate evaluation of the state of the smart cabin by calculating the temperature balance index inside and outside the cabin and the power supply abnormality index. When a smart cabin is in an unstable state, the system can automatically optimize energy distribution and feed back the optimized results to each smart cabin through a distributed network architecture to achieve inter-cabin linkage control and state feedback. This not only improves the efficiency of energy utilization, but also greatly enhances the stability and intelligence level of the system, effectively improving the user's living experience when camping. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0021] Figure 1 The present invention is a flow chart of the method.

[0022] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Example 1, please refer to Figure 1 As shown, the multi-warehouse distributed collaborative interaction method based on the smart cabin described in this embodiment includes the following steps:

[0025] S1: Each smart cabin unit is equipped with several sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time;

[0026] S2: Connect with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing. The shared data includes the environmental parameters and energy consumption data of each cabin.

[0027] S3: Comprehensively analyze the environmental parameters and energy consumption data of each cabin, evaluate the stability of each cabin state, and divide it into stable state and unstable state;

[0028] S4: The system optimizes the energy distribution of the unstable smart cabin, and transmits the optimization effect to the information exchange among the smart cabins to achieve linkage control and status feedback.

[0029] In S1, each smart cabin unit is equipped with several sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time, specifically:

[0030] According to the environmental monitoring requirements inside and outside the smart cabin, appropriate sensors are selected and reasonably arranged at key locations inside and outside the cabin.

[0031] Temperature sensors: Installed at different heights inside and outside the cabin to ensure that the difference in indoor and outdoor temperature can be detected. The outdoor sensor is placed away from direct sunlight and wind sources to obtain accurate ambient temperature.

[0032] Humidity sensor: Set up inside the cabin near the user's living area, and install one outside the cabin to monitor changes in humidity in the camping area. Make sure it is located away from water or heat sources to avoid errors.

[0033] Air quality sensor: installed in the air circulation position of the living area in the cabin to detect air pollutants such as PM2.5 and CO2, and provide timely feedback on air quality conditions. The air quality sensor outside the cabin is arranged in the open area of ​​the camping area to monitor the outside air conditions.

[0034] Light sensor: Installed near the skylight outside the cabin to monitor the intensity of sunlight and help adjust the lighting intensity and energy efficiency indoors. Light sensors inside the cabin are set in the main activity area to adjust the indoor lighting.

[0035] Noise Sensor: Installed outside the cabin to detect ambient noise at the campsite and help adjust the noise isolation equipment inside the cabin. Cabin noise sensors can be placed on the ceiling or wall to monitor the noise level inside the cabin and provide feedback and adjustments.

[0036] Motion sensors: placed at the cabin door, main passages in the cabin and near windows, they are used to sense the activities of people inside and outside the cabin, helping the system to determine the usage status of the cabin and automatically start and stop related equipment.

[0037] The sensors of each smart cabin unit collect real-time data through its acquisition module.

[0038] Data collection frequency setting: The system sets the frequency of data collection according to the purpose and requirements of different sensors. For example, temperature and humidity sensors collect data every 5 minutes, light and air quality sensors collect data every 10 minutes, and motion sensors and noise sensors collect data based on event triggers.

[0039] Multi-point data collection: Through the arrangement of multiple sensors, the space is fully covered to ensure the comprehensiveness of the data. For example, temperature and humidity sensors can be set in different areas of the cabin to ensure that the environmental data of the entire cabin is accurately reflected.

[0040] Data redundancy mechanism: To ensure the accuracy of the data, the system will install multiple sensors of the same type for redundant data collection, and compare and calibrate with other data sources to filter out abnormal data.

[0041] The collected environmental data is transmitted in real time through sensors and the central control unit (CCU) of the smart cabin unit to complete preliminary data processing.

[0042] Connection between sensors and CCU: The sensors are connected to the central control system of the smart cabin via wired or wireless means, using low-power communication protocols (such as Zigbee, LoRa or Bluetooth) to reduce energy consumption.

[0043] Data denoising and cleaning: Data transmitted to the central control system undergoes preliminary cleaning by the processor to remove noise or erroneous readings generated by sensors in harsh environments. For example, if a temperature sensor detects a sudden extreme temperature fluctuation in a short period of time, the system will automatically identify the reading as abnormal and ignore it.

[0044] Multi-dimensional data fusion: The CCU integrates the multi-dimensional environmental data collected by various sensors to form a comprehensive perception of the environment inside and outside the cabin. For example, temperature and humidity data are combined to determine the comfort level inside the cabin.

[0045] S2: Connect with other smart cabins and control centers through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing. The shared data includes the environmental parameters and energy consumption data of each cabin.

[0046] First of all, the communication between smart cabins and between smart cabins and the control center is based on wireless communication technology. Common wireless technologies include Wi-Fi, LoRa, Zigbee, 5G, etc. The specific choice depends on the camping environment and the distance between cabins. Wi-Fi: Suitable for short-distance high-bandwidth data transmission, suitable for cabin units with a closer distance. LoRa: Suitable for long-distance low-power data transmission, suitable for connections between cabin units in a larger camping site. 5G: Suitable for scenarios requiring high bandwidth and low latency, especially suitable for real-time transmission of large amounts of data in multiple cabins. Zigbee: Used for short-distance communication between low-power devices, suitable for device connections within a local area network environment.

[0047] A wireless communication module is installed in each smart cabin unit, enabling it to exchange data with other smart cabins and the control center. In-cabin communication module: Each cabin is equipped with a wireless communication module, which is connected to the sensors and control system in the cabin and is responsible for transmitting the environmental data of the cabin (such as temperature, humidity, and energy consumption) to the network. Centralized control center communication: A control center is set up at the campsite, equipped with high-performance communication equipment to collect data from each smart cabin, manage and control it in a unified manner, and connect to the cloud system or external network.

[0048] A distributed network architecture is built through wireless communication technology. Each smart cabin is connected to each other and maintains communication with the control center. Mesh network architecture: Smart cabin units are connected to each other through a mesh network. Each cabin is both a data transmission node and a receiving node, ensuring that data can be transmitted between cabins. Even if some cabins are temporarily disconnected, data can be transmitted through other cabin routes. Master-slave architecture: A master-slave communication architecture can be used between the smart cabin and the central control center. The data of all cabins will first be transmitted to the central control center, and then sent back to each cabin after centralized processing for unified coordination and feedback.

[0049] Through the distributed network architecture, the environmental data of each smart cabin (such as temperature, humidity, air quality, etc.) will be shared in real time between cabins and between cabins and the control center. Real-time data synchronization: When the sensor in a smart cabin collects new environmental data, the data is immediately uploaded to the network through the wireless communication module. Other smart cabins can receive this data in real time through the network to achieve collaborative operation. For example, when the temperature of a cabin rises, other smart cabins can adjust the air conditioning or ventilation equipment through shared data. Decentralized data exchange: In a distributed architecture, each cabin does not need to process data through a central server, but directly shares each other's data through point-to-point communication to ensure the timeliness and reliability of data transmission.

[0050] The shared data includes the environmental parameters and energy consumption data of each cabin. The environmental parameters include the temperature balance index inside and outside the cabin. The method for obtaining the temperature balance index inside and outside the cabin is:

[0051] Use sensors to collect cabin temperature T in real time in and the outside temperature T out , and record the current user-set comfort temperature T comfort , calculate the difference between the temperature inside the cabin and the temperature outside the cabin, ΔT in-out , used to indicate the influence of the external environment on the temperature inside the cabin, the expression is: ΔT in-out =T in -T out ; Calculate the difference between the cabin temperature and the comfort temperature set by the user. This part reflects whether the cabin needs to be adjusted to reach the temperature expected by the user. The expression is: ΔTin-comfort =T in -T comfort ; Calculate the difference between the outside temperature and the comfort temperature to indicate whether the outside temperature has a greater impact on the comfort level inside the cabin. The expression is: ΔT out-comfort =T out -T comfort ; Calculate the temperature balance index QS inside and outside the cabin, the expression is:

[0052] The greater the temperature balance index inside and outside the cabin, the worse the stability of the smart cabin. This means that the difference between the temperature inside and outside the cabin is large, or the temperature inside the cabin deviates greatly from the comfortable temperature set by the user, indicating that the current environmental conditions are not ideal and the cabin comfort level is low.

[0053] When the balance index increases, the smart cabin needs to adjust the air conditioning, ventilation and other control systems more frequently to maintain a comfortable environment in the cabin. This increase in adjustment frequency means that the system load is increased, and energy consumption may increase accordingly, thus affecting the stability and energy efficiency management of the cabin. Therefore, maintaining a smaller balance index can improve the stability and energy efficiency of the smart cabin.

[0054] The shared data includes the environmental parameters and energy consumption data of each cabin, where the energy consumption data is the power supply anomaly index. The method for obtaining the power supply anomaly index is:

[0055] Collect the real-time power consumption data of the smart cabin, and calculate the actual energy supply rate R of the smart cabin based on the collected data actual Calculate the power supply rate deviation value: Calculate the power supply anomaly index based on the power supply rate and the anomaly degree weight factor. The specific calculation expression is: Among them, pg x is the power supply anomaly index, R ideal represents the ideal power supply rate, w represents the abnormality weight factor; R actual Indicates the actual power supply rate of the smart cabin.

[0056] The larger the power supply anomaly index is, the worse the stability of the smart cabin is. The power supply anomaly index measures whether the power supply system of the smart cabin is stable and reliable. When the index is high, it means that the smart cabin faces unstable or frequent power supply interruptions, which may lead to the following situations:

[0057] The key functions of the smart cabin (such as air conditioning, lighting, ventilation, etc.) cannot operate normally, which directly affects the cabin environment adjustment and user experience. When the power is abnormal, the backup power supply or energy dispatch system will be frequently activated, increasing the complexity and burden of energy management. The unstable power supply may cause the equipment to start and stop repeatedly, thus shortening the equipment life and possibly causing failures.

[0058] Comprehensively analyze the environmental parameters and energy consumption data of each cabin, evaluate the stability of each cabin state, and divide it into stable state and unstable state, specifically:

[0059] The temperature balance index inside and outside the cabin and the power supply abnormality index are normalized, and the stability coefficient of the smart cabin state is calculated based on the normalized temperature balance index inside and outside the cabin and the power supply abnormality index.

[0060] For example, the present invention can use the following formula to calculate the stability coefficient of the smart cabin state, and the calculation expression is: Where MP is the stability coefficient of the smart cabin state, QS is the temperature balance index inside and outside the cabin, and pg x is the power supply abnormality index, a1 and a2 are the proportional coefficients of the temperature balance index inside and outside the cabin and the power supply abnormality index, and a2>a1>0.

[0061] The obtained stability coefficient of the smart cabin state is compared with the reference threshold of the stability coefficient of the smart cabin state. If the stability coefficient of the smart cabin state is greater than or equal to the reference threshold of the stability coefficient of the smart cabin state, it means that the stability of the smart cabin state is high. At this time, no warning signal is generated, and it is classified as a stable state; if the stability coefficient of the smart cabin state is less than the reference threshold of the stability coefficient of the smart cabin state, it means that the stability of the smart cabin state is low. At this time, a warning signal is generated and it is classified as an unstable state.

[0062] S4: The system optimizes the energy distribution of the unstable smart cabin, and transmits the optimization effect to the information exchange among the smart cabins to achieve linkage control and status feedback.

[0063] Identify unstable smart cabins: The system automatically identifies smart cabins in an unstable state by monitoring key parameters such as the power supply anomaly index and the temperature balance index inside and outside the cabin. For example, when the power anomaly index is higher than the set threshold or the temperature balance index is too large, the system will mark the cabin as unstable.

[0064] Once the unstable state of the smart cabin is identified, the system optimizes the energy distribution of the entire smart cabin network through intelligent algorithms. The process includes: dynamically adjusting the energy flow: according to changes in the environment inside and outside the cabin, preferentially allocating excess electricity or backup energy (such as battery storage or solar energy) to unstable cabins; mobilizing energy from low-priority cabins (such as unmanned cabins or idle cabins) to ensure that the smart cabin in an unstable state resumes normal function. By analyzing the energy consumption data of each cabin, the system dynamically adjusts the priority of energy use, reduces unnecessary energy waste, and ensures that unstable cabins have continuous power supply.

[0065] The optimized energy distribution scheme is shared among the smart cabins through a distributed network architecture to ensure that all smart cabins obtain the latest energy status information. The information interaction process includes: Energy status synchronization: After the unstable state is restored, the system synchronizes the optimized energy configuration to all smart cabins, so that each cabin knows the current energy supply status and the operating status of the cabin equipment. Real-time data sharing: Key data in the optimization process (such as power demand and cabin temperature adjustment status) are shared with other cabins through the Internet of Things platform to help the system optimize energy scheduling in the future.

[0066] By sharing data, each smart cabin coordinates the operation of its own equipment according to the system optimization plan, automatically adjusts air conditioning, lighting, ventilation and other equipment, and realizes linkage control. For example, when one cabin is short of energy, other cabins automatically reduce energy consumption to cooperate with the energy balance of the entire system. After receiving the optimization plan, each smart cabin will feedback its own status (such as recovery degree, current power level, etc.) to the system. The system continuously adjusts the energy allocation strategy based on this feedback data to ensure that all cabins remain stable.

[0067] In this embodiment, each smart cabin unit is equipped with a number of sensors to form an environmental perception network for real-time monitoring of environmental data inside and outside the cabin; it is connected to other smart cabins and a control center through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing, and the shared data includes the environmental parameters and energy consumption data of each cabin; a comprehensive analysis is performed on the environmental parameters and energy consumption data of each cabin, and the stability of the state of each cabin is evaluated, and it is divided into a stable state and an unstable state; the system optimizes the energy distribution of the smart cabin in an unstable state, and the optimization effect is exchanged between the smart cabins to achieve linkage control and state feedback.

[0068] Example 2, please refer to Figure 2 As shown, the multi-warehouse distributed collaborative interaction system based on the smart cabin described in this embodiment includes a data acquisition module, a data sharing module, a stability analysis module and an interaction optimization module;

[0069] Data acquisition module: Each smart cabin unit is equipped with a number of sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time;

[0070] Data sharing module: Connect with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing. The shared data includes the environmental parameters and energy consumption data of each cabin.

[0071] Stability analysis module: conducts comprehensive analysis on the environmental parameters and energy consumption data of each cabin, evaluates the stability of each cabin state, and divides it into stable state and unstable state;

[0072] Interactive optimization module: The system optimizes the energy distribution of the unstable smart cabin, and transmits the optimization effect through information exchange between the smart cabins to achieve linkage control and status feedback.

[0073] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0074] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0075] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0076] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0077] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0078] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0079] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0080] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0081] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0083] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0084] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-warehouse distributed collaborative interaction method based on a smart cabin, characterized by: The steps include: S1: Each smart cabin unit is equipped with several sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time; S2: Connect with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, so that each smart cabin can realize real-time data sharing. The shared data includes the environmental parameters and energy consumption data of each cabin. S3: Comprehensively analyze the environmental parameters and energy consumption data of each cabin, evaluate the stability of each cabin state, and divide it into stable state and unstable state; S4: The system optimizes the energy distribution of the smart cabin in an unstable state, and transmits the optimization effect through information exchange between the smart cabins to achieve linkage control and status feedback.

2. The multi-warehouse distributed collaborative interaction method based on the smart cabin according to claim 1 is characterized by: In S2, the shared data includes the environmental parameters and energy consumption data of each cabin, where the environmental parameters include the cabin internal and external temperature balance index. The method for obtaining the cabin internal and external temperature balance index is: using sensors to collect the cabin temperature T in real time. in and the outside temperature T out , and record the current user-set comfort temperature T comfort , calculate the difference ΔT between the temperature inside and outside the cabin in-out , used to indicate the influence of the external environment on the temperature inside the cabin, the expression is: ΔT in-out =T in -T out ; Calculate the difference between the cabin temperature and the comfort temperature set by the user, the expression is: ΔT in-comfort =T in -T comfort ; Calculate the difference between the outside temperature and the comfort temperature, the expression is: ΔT out-comfort =T out -T comfort ; Calculate the temperature balance index QS inside and outside the cabin, the expression is:

3. The multi-warehouse distributed collaborative interaction method based on the smart cabin according to claim 2 is characterized by: The shared data includes the environmental parameters and energy consumption data of each cabin, where the energy consumption data is the power supply anomaly index. The method for obtaining the power supply anomaly index is: Collect the real-time power consumption data of the smart cabin, and calculate the actual energy supply rate R of the smart cabin based on the collected data actual Calculate the power supply rate deviation value: Calculate the power supply anomaly index based on the power supply rate and the anomaly degree weight factor. The specific calculation expression is: Among them, pg x is the power supply anomaly index, R ideal represents the ideal power supply rate, w represents the abnormality weight factor; R actual Indicates the actual power supply rate of the smart cabin.

4. The multi-cabin distributed collaborative interaction method based on the smart cabin according to claim 3 is characterized by: In S3, the environmental parameters and energy consumption data of each cabin are comprehensively analyzed to evaluate the stability of each cabin state, specifically: The temperature balance index inside and outside the cabin and the power supply abnormality index are normalized, and the stability coefficient of the smart cabin state is calculated by the normalized temperature balance index inside and outside the cabin and the power supply abnormality index. The calculation expression is: Where MP is the stability coefficient of the smart cabin state, QS is the temperature balance index inside and outside the cabin, and pg x is the power supply abnormality index, a1 and a2 are the proportional coefficients of the temperature balance index inside and outside the cabin and the power supply abnormality index, and a2>a1>0.

5. The multi-warehouse distributed collaborative interaction method based on the smart cabin according to claim 4 is characterized by: The obtained stability coefficient of the smart cabin state is compared with the reference threshold of the stability coefficient of the smart cabin state. If the stability coefficient of the smart cabin state is greater than or equal to the reference threshold of the stability coefficient of the smart cabin state, it means that the stability of the smart cabin state is high. At this time, no warning signal is generated, and it is classified as a stable state; if the stability coefficient of the smart cabin state is less than the reference threshold of the stability coefficient of the smart cabin state, it means that the stability of the smart cabin state is low. At this time, a warning signal is generated and it is classified as an unstable state.

6. A multi-warehouse distributed collaborative interaction system based on a smart cabin, used to implement the multi-warehouse distributed collaborative interaction method based on a smart cabin as described in any one of claims 1 to 5, characterized in that: It includes data acquisition module, data sharing module, stability analysis module and interactive optimization module; Data acquisition module: Each smart cabin unit is equipped with a number of sensors to form its environmental perception network, which is used to monitor the environmental data inside and outside the cabin in real time; data sharing module: It connects with other smart cabins and the control center through wireless communication technology to build a distributed network architecture, which is used for real-time data sharing among smart cabins. The shared data includes the environmental parameters and energy consumption data of each cabin; stability analysis module: It conducts a comprehensive analysis of the environmental parameters and energy consumption data of each cabin, evaluates the stability of each cabin state, and divides it into stable and unstable states; interactive optimization module: The system optimizes the energy distribution of smart cabins in unstable states, and exchanges the optimization results among the smart cabins to achieve linkage control and state feedback.

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