Disclosure of Invention
The invention aims to provide an air conditioner Internet of things control method, so that part of defects and shortcomings pointed out in the background art are overcome.
The invention solves the technical problems by adopting the following technical scheme that the air conditioner Internet of things control method comprises the following steps of S1, intelligent Internet of things interconnection of air conditioning equipment:
Integrating an internet of things (IoT) module in each air conditioner, and collecting environmental data including temperature, humidity and air quality of a room in real time through connection of an intelligent sensor and a cloud platform;
s2, intelligent cooperative control based on internet of things data:
s2.1, constructing an intelligent cooperative control algorithm based on communication between devices by utilizing the Internet of things intercommunication among air conditioning devices;
s2.2, each air conditioning equipment adjusts a working mode according to a plurality of sensor data in a room and change information of surrounding environment including weather and sunlight;
S3, intelligent environment prediction:
the air conditioning equipment detects environment data in real time through a local sensor, and accesses external environment prediction data comprising weather forecast and climate change trend, and the working mode is adjusted in advance by combining the self-learning function of the equipment;
S4, a multidimensional energy-saving optimization algorithm:
s4.1, adopting a multidimensional energy-saving optimization algorithm to carry out intelligent control, comprising respectively adjusting the refrigeration/heating intensity according to the temperature requirements of different areas in a room,
S4.2, simultaneously combining external weather change and equipment use frequency, and optimizing the whole energy consumption, wherein the optimization strategy comprises time period distribution and equipment priority control;
S5, personalized user control based on big data analysis:
S5.1, collecting environmental data and user behavior data through air conditioning equipment, and predicting living habits and comfort preferences of users by utilizing big data analysis and a machine learning model;
s5.2, the air conditioning system adjusts the control strategy according to the behavior mode of the user including the work and rest time and the indoor activity condition.
Further, the intelligent cooperative control method based on the internet of things data comprises the following steps:
The method comprises the steps of controlling data sharing and cooperative work among air conditioning equipment through a wireless communication protocol, enabling each air conditioning equipment to be provided with one of Internet of things modules including Wi-Fi, zigBee, loRa, enabling the air conditioning equipment to share environment data including temperature, humidity and CO2 concentration sensor data, adjusting a working mode in real time according to the sensor data and shared information, and controlling temperature adjustment of each equipment by the following formula:
The method comprises the steps of setting T (T) to be the target temperature of air conditioning equipment at a time T, setting Troom (T) to be the current temperature of a room where an air conditioner is located, indicating the perception of the equipment to the environment of the room where the air conditioner is located, setting alpha to be a control coefficient, indicating the response sensitivity of the equipment to the change of the environment temperature, setting the value range of alpha to be more than or equal to 0.1 and less than or equal to 1.0 according to the type and specific situation of the equipment, setting Tenv (i) to be the environment temperature of the ith equipment, enabling the air conditioning equipment to share information with other equipment through the Internet of things, and setting n to be the number of the air conditioning equipment sharing the information.
Further, the intelligent cooperative control method based on the internet of things data comprises the following steps:
the air conditioning equipment integrates external environment data, wherein the external environment data comprises weather, sunlight intensity and outdoor temperature and humidity information data, the information data is obtained in real time through an open API (application program interface) or an embedded sensor, the air conditioning equipment senses the current environment state and predicts future environment changes, the external environment data predicts the future environment changes by modeling a function of the weather and the sunlight intensity and utilizing the historical weather data, and an external temperature prediction formula is as follows:
Wherein Text (T) is an external temperature predicted value at a time T and represents the expected temperature of an external environment, fweather (x) is a weather change function and represents the change of weather data at time x, the calculation is performed according to the change rate of the weather temperature based on weather forecast data modeling, fsunlight (x) is a sunlight intensity function and represents the change of sunlight intensity within time x, and a sensor or a sunlight forecast system provides data; To represent integration of the external environment data from time 0 to time t, the cumulative effect is calculated.
Further, the intelligent cooperative control method based on the internet of things data comprises the following steps:
Each air conditioning equipment is embedded into a machine learning algorithm, interaction between environment data and the air conditioning equipment is analyzed in real time, and optimization control is carried out; the life rule of the user is analyzed, including the time of returning home and sleeping, a personalized model is established according to historical data and real-time data, and the temperature and the wind speed are regulated, and the intelligent learning and optimizing process among the devices is expressed by an energy efficiency optimizing function:
wherein:
E (T) is the total energy efficiency of the air conditioning system at the time T, the energy efficiency of the equipment is comprehensively considered by indexes, Ci (T) is the power consumption of the ith air conditioning equipment at the time T and represents the real-time energy consumption of the equipment, deltaTi (T) is the temperature change of the ith air conditioning equipment at the time T and represents the temperature change quantity of the equipment after adjustment, alphai is the energy efficiency coefficient of the ith equipment and represents the relation between the energy consumption and the temperature adjustment of the equipment, and the energy efficiency coefficient of each equipment is adjusted according to the equipment type, the environmental load and other factors and has a value range of 1 to 5.
Further, the multi-dimensional energy-saving optimization algorithm comprises:
The system of the air conditioning equipment calculates the temperature difference (delta Ti) of each area in the room by the following formula, and dynamically adjusts the refrigerating or heating mode of each area according to the temperature difference:
The temperature difference between the kth region and the external environment temperature is represented by DeltaTk, tk is the actual temperature of the kth region and is detected by a temperature sensor of an air conditioning equipment system, Text is the external environment temperature and is obtained in real time through a weather API or an environment monitoring device, lambdak is the heat conduction coefficient of the region and represents the heat exchange efficiency of the region and the external environment, and T is time and represents the duration of a formula in the calculation process.
Further, the multi-dimensional energy-saving optimization algorithm comprises:
The air conditioning system predicts future environmental changes and adjusts the working mode in advance by introducing external environmental data, and predicts indoor temperature changes by combining external weather data through the following prediction formula:
ΔTpred＝α1Text(t)+α2Hext(t)-β·SolarRadiation(t)
the system comprises a delta Tpred, a Text (T), a Hext (T), a SolarRadiation (T), a alpha1、α2 and a beta, wherein the delta Tpred is used for indicating the indoor temperature change predicted by the system and used for adjusting the working mode of the air conditioner, the Text (T) is an external temperature and reflects the temperature change of an outdoor environment, the Hext (T) is an external humidity and influences the refrigerating or heating effect of the air conditioner, the SolarRadiation (T) is a sunlight intensity and determines the influence of sunlight on the room temperature, the alpha1、α2 and the beta are response coefficients of the change of the external environment of a region and can be adjusted according to specific conditions, and the influence degree of external environment parameters on the indoor temperature change is determined.
Further, the multi-dimensional energy-saving optimization algorithm comprises:
By monitoring the use frequency (U) and the load condition (Li) of each air conditioning device, the device dynamically adjusts the working state, and the calculation formula of the load balance of the air conditioning device is as follows:
The method comprises the steps of calculating a load factor of an ith air conditioner, wherein LoadFactori represents the load factor of the ith air conditioner, indicating the load duty ratio of equipment, Li is the current load of the ith air conditioner and is determined by power consumption, refrigerating or heating intensity parameters of the air conditioner, U is the use frequency of the ith air conditioner, the working condition of the air conditioner is reflected, load distribution is affected, thresholdi is the use frequency Threshold of the ith air conditioner and is used for determining whether the equipment enters an energy-saving mode or not, and N is the total number of the air conditioner and is used for calculating overall load balance.
Further, the multi-dimensional energy-saving optimization algorithm comprises:
Optimizing energy use through intelligent time period allocation and equipment priority control, ensuring regional comfort according to energy requirements of different time periods including workdays, rest days, daytime and night, and allocating priority to equipment according to energy efficiency and use frequency factors of the equipment, wherein the calculation method for optimizing the time period and the equipment priority is as follows:
Wherein Eopt represents the optimized energy consumption of the whole system in a specific time period, the total energy consumption is minimized by adjusting the equipment priority and the operation mode, Pi (t) represents the power requirement of the ith air conditioner in a certain time t and can be dynamically adjusted according to factors such as ambient temperature, set temperature and the like, etai represents the energy efficiency coefficient of the ith air conditioner in actual operation, a higher eta value represents the energy efficiency performance of the equipment in actual operation, fi (t) represents the priority function of the ith air conditioner, and the operation priority of the equipment is determined according to the factors such as the time period, the regional requirement, the equipment state and the like.
The air conditioner internet of things control method disclosed by the invention has remarkable beneficial effects by combining intelligent control and real-time data feedback through a multi-dimensional energy-saving optimization algorithm, and mainly comprises the following steps:
1. The invention can monitor and dynamically adjust the working state of the air conditioner in real time by introducing information of multiple dimensions such as external environment data, indoor area temperature difference, equipment use frequency and the like, ensures that the air conditioner operates in an optimal energy efficiency zone, and avoids energy waste.
2. The method is used for intelligently adjusting the working state of the air conditioner by calculating the load factor of each air conditioner and comprehensively considering the use frequency, the current load and the energy efficiency coefficient of each air conditioner. The system dynamically adjusts according to the load conditions of different air conditioners, realizes reasonable distribution of the load, and avoids the condition that some air conditioners work excessively and other air conditioners are idle, thereby reducing the overload operation of equipment, prolonging the service life of the equipment and reducing the maintenance cost.
3. The air conditioner Internet of things control method is capable of improving comfort and health environment, not only focusing on energy optimization, but also dynamically adjusting refrigeration or heating intensity according to temperature requirements of different areas in a room. The system can automatically adjust the air conditioning modes of different areas in the areas close to the window and the direct sunlight area and the areas close to the inner wall and the lower temperature, so that the temperature of each area is ensured to be maintained in a comfortable range, and the comfort experience of a user is improved. According to the method, through accurate control and prediction of future temperature change, indoor temperature fluctuation caused by response lag of an air conditioning system is avoided, and comfort level is further improved.
4. The invention can dynamically adjust the working state, priority and energy efficiency of the air conditioner according to the real-time data and the change of the external environment, so that the system has self-adaption capability. The system reasonably distributes equipment priority and operation modes by analyzing the energy demands of different time periods, workdays, rest days and daytime and night, ensures the efficient operation of each air conditioner in different environments, and improves the overall performance of the system.
Detailed Description
The following describes the embodiments of the present invention in detail with reference to the drawings.
With reference to fig. 1, the core of the control method for the air conditioner Internet of things is that intelligent interconnection and intercommunication of air conditioning equipment are realized through the technology of Internet of things, so that more efficient energy management and comfort control are achieved. Firstly, the key of the step S1 is that the intelligent internet of things of air conditioning equipment is interconnected, each air conditioner needs to integrate an internet of things IoT module, and the module is connected with a cloud platform through an intelligent sensor to collect and transmit environmental data in a room in real time, including temperature, humidity, air quality and the like. The data are transmitted to the cloud platform in real time through the wireless network, and the cloud platform gathers and processes the information to form a centralized equipment state and environment information management system. Each air conditioning equipment is in continuous two-way communication with the system through the Internet of things module, and the working state and the environment information of the air conditioner can be dynamically updated and adjusted. The air conditioning equipment feeds back the running states of the air conditioning equipment, including a working mode, a set temperature, a refrigerating/heating mode and the like, to the cloud platform in real time, so that the cloud platform can acquire accurate equipment data. Meanwhile, the cloud platform can regulate and control the air conditioner according to the data of environmental change, for example, when the temperature of a certain room is too high, the cloud platform can regulate the temperature to be within a set range by controlling the air conditioner, so that accurate control is realized, and the energy waste caused by excessive refrigeration or heating is avoided.
S2, focusing on realizing intelligent cooperative control among air conditioning equipment through the technology of the Internet of things, and optimizing the overall efficiency and comfort of an air conditioning system through data sharing and mutual cooperation among the equipment. In S2.1, an intelligent cooperative control algorithm based on communication between devices is constructed through the Internet of things intercommunication between air conditioning devices. Specifically, the air conditioning device not only adjusts according to the environmental data of its own room, but also can share data with other devices through the internet of things module, including but not limited to the temperature, humidity, air quality, personnel activities, etc. of each room. Through the bidirectional communication between the devices, the air conditioning device can know the running state of other devices and the change of the surrounding environment in real time, and then the air conditioning device can cooperate with each other according to the information. On the algorithm level, the air conditioning equipment dynamically adjusts the operation mode based on temperature and humidity data collected in real time and external environment changes (such as weather, sunlight intensity and the like) and combined with respective operation requirements. The intelligent cooperative control can improve the operation accuracy and comfort of the air conditioner, and can avoid repeated refrigeration or heating of a plurality of air conditioning equipment in different rooms or areas, so that the energy consumption is optimized, and the overall operation efficiency of the system is improved. In S2.2, each air conditioning device adjusts its operation mode according to a plurality of sensor data (e.g. temperature, humidity, CO2 concentration, etc.) in the room, and in combination with information about changes in the surrounding environment, such as weather forecast, changes in sunlight intensity, etc. For example, if the air conditioner receives weather change data, the air conditioner can start a refrigerating mode in advance to prevent excessive energy consumption caused by overhigh indoor temperature, and can switch to a heating mode to avoid unnecessary energy consumption caused by environmental temperature change in winter or when the weather changes cold. Through intelligent control algorithm and data sharing, each air conditioning equipment not only adjusts according to own environmental data, but also adjusts correspondingly according to the state of surrounding air conditioning equipment and external environment changes, and cooperative work among the air conditioning equipment is realized, so that the overall performance of an air conditioning system is optimized, and the effects of more energy conservation and comfort are achieved.
S3, focusing on intelligent environment prediction, wherein the core is that environment data are detected in real time through a local sensor, and the environment change in the future is predicted by utilizing an intelligent algorithm in combination with external environment information, so that the working mode of the air conditioner is adjusted in advance. Firstly, the air conditioning equipment monitors environmental data of a room in which the air conditioning equipment is located in real time through built-in sensors such as a temperature sensor, a humidity sensor, a CO2 concentration sensor and the like. These data are transmitted in real time to a control system local to the cloud platform or device as a basis for adjusting the air conditioning mode of operation. Meanwhile, the air conditioning equipment is also connected with external environment data, including weather forecast, climate change trend and other information, so that the perception capability of environment change is further improved. Weather forecast provides data on temperature, humidity, wind speed, etc. in a short period (e.g., hours to days), while climate change trends can help the system analyze long-term environmental changes such as seasonal temperature fluctuations, climate anomalies, etc. The external data are transmitted to the air conditioning system through the Internet of things module, and a reference is provided for the decision of the air conditioner. By combining the real-time environment data and the external prediction data, the air conditioning equipment performs dynamic learning and adaptation according to historical data and user habits through an intelligent self-learning function. The self-learning algorithm can analyze the demand patterns of the user in different time periods and different environmental conditions, for example, the user usually adjusts the air conditioner to a certain temperature when getting up in the morning, or the refrigerating strength needs to be improved in the evening period, etc. By analyzing these behavior patterns, the air conditioning apparatus can predict the user's demand and actively adjust the air conditioning operation pattern before the user's demand appears. For example, if the weather forecast shows that the air temperature will rise rapidly within several hours in the future, the air conditioning system can be started in advance and adjusted to a refrigeration mode, so that the indoor temperature is prevented from being too high, and the energy waste caused by the early starting of the air conditioner is avoided. Similarly, if the external environment shows that the air temperature is about to be reduced, the air conditioner can be adjusted into an energy-saving heating mode in advance, so that excessive energy consumption is avoided. Through the intelligent environment prediction, the air conditioning equipment can react before the environment changes, and is ready in advance, so that the comfort level of a user is improved, and the energy use efficiency is improved.
S4, mainly surrounding the application of a multidimensional energy-saving optimization algorithm, and realizing the maximization of the energy-saving effect of the air conditioning system through intelligent control and data analysis. In S4.1, the working mode of the air conditioner is intelligently controlled by adopting a multidimensional energy-saving optimization algorithm, and the core purpose is to accurately adjust the refrigerating or heating intensity according to the temperature requirements of different areas in a room. In particular, there is a large difference in temperature requirements for different areas within a single room. For example, the area near the window is warmer due to direct sunlight, while the area away from the window is cooler. Conventional air conditioning systems typically only regulate the temperature of the entire room, resulting in excessive cooling or heating of certain areas, resulting in wasted energy. By introducing a multidimensional energy-saving optimization algorithm, the air conditioning system can accurately adjust the intensity of refrigeration or heating according to the temperature requirement and environmental change of each area, thereby realizing more efficient energy utilization and comfortable indoor environment. The algorithm dynamically adjusts the working mode of the air conditioner according to the real-time sensor data and the heat load distribution of different areas, avoids the waste of energy sources and ensures the temperature balance of each area. Then, in S4.2, the algorithm further optimizes the overall energy consumption by combining information such as external weather changes and equipment usage frequency. External weather changes (such as air temperature, humidity, sunlight intensity, etc.) directly affect the operating requirements of the air conditioner. For example, the cooling demand of an air conditioner increases when the air temperature increases, and the heating demand increases when the air temperature decreases. By combining these weather change data with the frequency of use of the air conditioning apparatus, the system is able to adjust the mode of operation of the air conditioner based on weather predictions, avoiding premature start or excessive operation, optimizing overall energy consumption. In addition, the optimization strategy also includes time period allocation and device priority control. Specifically, the air conditioning system can perform reasonable energy distribution according to the requirements of different time periods, for example, in daytime working time, the system can preferentially ensure the temperature control of an office or a high-flow area, and in evening or off-peak time, the air conditioning system can be adjusted according to the actual requirements of users, so that unnecessary energy consumption is avoided. Meanwhile, the priority control of the air conditioning equipment dynamically adjusts the running state of the equipment according to the energy consumption capacity, efficiency, load condition and other factors of the equipment. For high performance air conditioning units, the system will preferably regulate its operation, while for less efficient units, the system will reduce its load, even when not needed, ensuring that the overall energy efficiency of the air conditioning system is maximized.
S5, mainly focusing on personalized user control based on big data analysis, aiming at providing more intelligent and personalized air conditioner control experience through collection and analysis of behavior data and environment data of users. In S5.1, the air conditioning device collects environmental data including indoor temperature, humidity, CO2 concentration, and the like, and behavioral data of the user, such as temperature adjustment preference, frequency of use, air conditioning on-off time, and the like, in real time through integrated sensor and internet of things technology. And uploading the data to the cloud through the Internet of things platform to form a data pool. By using big data analysis and a machine learning model, the air conditioning system can perform deep analysis on the data, and life habits and comfort preferences of users can be extracted from the data, for example, a certain user prefers to adjust the temperature of the air conditioner to a specific value between 10 and 12 pm, or the set temperature is lower when a certain user works in daytime, and higher when the user has a rest in evening. With this information, the system can predict the future needs of the user and optimize the operation mode of the air conditioner. The machine learning model continuously adjusts the prediction algorithm by analyzing feedback and preference of the user in different time and different environments, and improves the accuracy of prediction and the effect of personalized recommendation. And S5.2, the air conditioning system adjusts according to the predicted user behavior mode and dynamically optimizes the control strategy. Specifically, the system can intelligently adjust the operation strategy of the air conditioner according to information such as the work and rest time of the user, indoor activity conditions and the like. For example, if the system obtains that a certain user gets up and starts the air conditioner every morning at 6 hours through data analysis, the system can adjust the preset temperature of the air conditioner in advance to provide a comfortable environment when the user arrives at a room, if the user has a rest, the air conditioner can reduce unnecessary refrigeration or heating according to the sleep mode of the user to ensure the lowest energy consumption and maintain the comfortable sleep temperature, and if the system monitors that the user is not at home for a long time, the air conditioner can be adjusted to be in an energy-saving mode or closed to avoid unnecessary energy waste. Through the personalized control, the air conditioner not only can adapt to the demands of users, but also can learn by itself according to the behavior mode of the users, and the service quality and the efficiency are continuously improved.
Example 1:
Referring to fig. 2, in this embodiment, a plurality of air conditioning devices are installed in an office building, and each air conditioner is equipped with an internet of things module, so as to support data sharing and cooperative work with other devices through Wi-Fi. When each air conditioning equipment actually operates, the working mode of the air conditioning equipment can be adjusted in real time according to the temperature, the humidity, the concentration of CO2 and the shared environmental data of other air conditioning equipment in different rooms, so that the most efficient, comfortable and energy-saving temperature adjustment of the whole building is ensured.
1. Internet of things module and data sharing
An office building has 5 air conditioning units, 4 of which are located in an employee work area and 1 of which are located in a public rest area. And each air conditioner is connected with other equipment and the cloud platform through the Wi-Fi module, collects environmental data such as temperature, humidity, CO2 concentration and the like in real time, and shares the data to other air conditioning equipment. For example, the temperature of a room where one air conditioner is located is 22 ℃, the temperature of a room where another air conditioner is located is 24 ℃, and the air conditioning equipment uploads the data to the internet of things platform and shares the information with other equipment. Through the communication protocol of the Internet of things, such as Wi-Fi or ZigBee, the air conditioning equipment can work cooperatively and dynamically adjust respective operation modes.
2. Temperature regulation formula
The temperature adjustment of each air conditioning apparatus is controlled by the following formula:
and T (T) setting the target temperature of the air conditioning equipment at the moment T.
And Troom (T), wherein the current temperature of the room where the air conditioner is located represents the perception of the environment of the room where the device is located.
Alpha is a control coefficient which represents the response sensitivity of the equipment to the change of the environmental temperature, the value range is more than or equal to 0.1 and less than or equal to 1.0, and the specific value is set according to the type and the function of the equipment.
Tenv (i) the ambient temperature of the ith equipment, which refers to the ambient temperature of the room in which other air conditioners are located.
And n, the number of the air-conditioning equipment sharing the information represents the number of data shared among all the air-conditioning equipment.
3. Application of control strategy and calculation results
Setting 5 air conditioning devices in an office, wherein the current ambient temperatures of all rooms are respectively:
Air conditioner 1 (employee a room) Troom (1) =22℃
Air conditioner 2 (employee B room) Troom (2) =23℃
Air conditioner 3 (employee C room) Troom (3) =24℃
Air conditioner 4 (employee D room): Troom (4) =25℃
Air conditioner 5 (rest area): Troom (5) =22℃
At this time, the environmental temperature data in the shared information is set to be:
Tenv(1)=22°C
Tenv(2)=23°C
Tenv(3)=24°C
Tenv(4)=25°C
Tenv(5)=22°C
And the control coefficient alpha=0.5 is set, i.e. the response sensitivity of the device to changes in ambient temperature is relatively moderate. Using the above temperature adjustment formula, the target temperature setting for each air conditioning device can be calculated:
For air conditioner 1:
T(1)=22+0.5·((22+23+24+25+22)-22)
T(1)=22+0.5·(116-22)=22+0.5·94=22+47=69°C
Obviously, the result of 69 ℃ is an extreme value in the calculation process, indicating that a more reasonable limiting or feedback mechanism is needed to control the temperature change at this time. The numerical value can be adjusted to be more in line with the actual use requirement in actual operation, and the upper limit of the temperature is set to be 28 ℃.
For air conditioner 2:
T(2)=23+0.5·((22+23+24+25+22)-23)
T(2)=23+0.5·(116-23)=23+0.5·93=23+46.5=69.5°C
Also, the calculation results of the air conditioner 2 indicate that a further control strategy is required to limit the temperature range.
In this way, the air conditioning equipment can dynamically adjust the working mode according to the environmental data and the states of other equipment, so as to form intelligent cooperative work. The temperature control requirements of all rooms can be optimized, the waste of energy sources can be reduced, and the comfort level is ensured.
The innovation of the scheme is that through the internet of things intercommunication among the air conditioning devices, each air conditioner can acquire temperature information of the environment where other devices are located, and the working mode of the air conditioner can be adjusted in real time according to the temperature information.
Setting 5 air conditioning equipment installed in an office building, wherein each air conditioner is provided with an Internet of things module, and data such as temperature, humidity and sunlight intensity from the surrounding environment can be collected in real time. The data come from an air conditioner internal sensor and also comprise weather information acquired by an open API interface or an external embedded sensor. The external data are transmitted to the cloud platform, and are processed and analyzed in real time, and the air conditioning equipment can adjust the running mode of the air conditioning equipment according to the environmental data and the prediction result, so that the comfort and the energy efficiency of the building are improved.
1. Air conditioning equipment integrating external environment data
The air conditioning equipment senses the current environment state by integrating weather, sunlight intensity and outdoor temperature and humidity information and makes corresponding prejudgment. Specifically, weather change data, sunlight intensity data, and outdoor temperature and humidity data may be obtained by sensors or transmitted to the air conditioning equipment in real time through an open API interface (e.g., weather service provider data). The air conditioning equipment can combine the information to adjust the temperature control strategy of the air conditioning equipment, so that the indoor temperature is ensured to be comfortable and the energy efficiency is ensured to be optimal.
2. External environment temperature prediction formula
The air conditioning apparatus uses the historical climate data to predict future environmental changes and thus respond to future temperature fluctuations. The external temperature prediction formula is:
wherein:
Text (T) represents the predicted value of the external temperature at time T, i.e. the expected temperature of the external environment.
Fweather (x) is a weather change function, representing changes in weather data over time x, typically provided by a weather forecast system, and modeled based on the rate of change of air temperature.
Fsunlight (x) is a sunlight intensity function representing the change in sunlight intensity over time x, typically provided by a sensor or sunlight forecasting system.
The external environment data from time 0 to time t is integrated to calculate the cumulative effect.
3. Example application and data presentation
Setting that the day is a sunny working day, and displaying weather forecast, wherein the external temperature gradually rises within 6 hours in the future, and the sunlight intensity gradually increases. Specific meteorological data are as follows:
The external temperature of 6 o' clock in the morning is 18 ℃, and the meteorological change rate is 0.5 ℃ per hour.
The sunlight intensity is gradually increased from 6 hours in the morning to 200W/m2 at 8 hours, and gradually increased to 800W/m2.
Using the above external temperature prediction formula, the air conditioning apparatus can predict a future temperature change according to a temperature change of the external environment and a solar intensity.
First, data of the weather change function fweather (x) and the solar intensity function fsunlight (x) are modeled by interpolation in hours. The weather temperature is gradually increased from 18 ℃ to 24 ℃ and the sunlight intensity is also gradually increased between 6 points and 12 points.
The weather change function was set to be fweather (x) =0.5 x, i.e. 0.5 ℃ per hour of temperature change.
Solar intensity variation function fsunlight (x) =0.6x, where x is the number of hours, indicating a gradual increase in solar intensity from 0W/m2 from 6 points.
According to this model, an external temperature predicted value Text (T) is calculated:
For time period 0.ltoreq.t.ltoreq.6 (from 6 a.m. to 12 a.m.):
Thus, after 6 hours, the external temperature predicted value was 19.8 ℃.
This predicted value provides future environmental reference data for the air conditioning unit from which the air conditioner will make a precondition for the indoor temperature.
4. Intelligent cooperative control of air conditioning equipment
5 Air conditioning equipment are arranged in the office building, and the control modes of the 5 air conditioning equipment are as follows:
air conditioner 1 is located in employee A room, and the current temperature is 22 ℃.
Air conditioner 2, located in employee B room, with a current temperature of 23 ℃.
Air conditioner 3 is located in the room of staff C, and the current temperature is 24 ℃.
Air conditioner 4 is located in employee D room, and the current temperature is 25 ℃.
And 5, the air conditioner is positioned in a public rest area, and the current temperature is 22 ℃.
Each air conditioning unit adjusts its operation mode based on the shared data and the predicted external temperature. For example, the air conditioner 1 calculates its target temperature based on environmental data of other air conditioners and the predicted value of the external temperature. The target temperature formula of the air conditioner 1 is:
wherein:
troom (1) is the current temperature (22 ℃) of the room in which the air conditioner 1 is located.
Tenv (i) is the ambient temperature of the other air conditioning unit.
Α=0.5 is a control coefficient representing the sensitivity of the device to changes in ambient temperature.
The air conditioner 1 will obtain the following results when actually calculating:
T1(t)=22+0.5·((22+23+24+25+22)-22)
T1(t)=22+0.5·(116-22)=22+0.5·94=22+47=69°C
Obviously, this calculation is too extreme, indicating that the air conditioning system requires a further limiting mechanism. For example, the target temperature may be limited to a reasonable range (e.g., the maximum temperature is set to 28 ℃) to ensure that impractical temperature control requirements are not caused by the predicted excessive reliance on external data.
An intelligent cooperative control system of air conditioner based on internet of things data is set to be implemented in a certain commercial building. The building has multiple air conditioning units, each unit is equipped with embedded machine learning algorithms that analyze environmental data in real time and perform optimal control based on interactions between the air conditioning units. Meanwhile, each air conditioner also analyzes the life rule of the user, including the time of returning home, sleeping time and the like, and builds a personalized model according to the historical data and the real-time data, so as to adjust the temperature and the wind speed.
5 Air conditioning equipment are installed in the set office, and are respectively located in different offices and public areas. Each air conditioning device collects environmental data including temperature, humidity and air quality in the room in real time through the sensor, and in addition, the air conditioning devices can share the data with other air conditioning devices to form a cooperative network.
The embedded machine learning algorithm of each air conditioning equipment learns according to the historical behaviors (such as home time, working time and sleeping time) and real-time data (such as current temperature, humidity and external environment data) of the user, and a personalized temperature control model is built. This model will help the air conditioning apparatus predict future demands based on the user's behavior patterns and make corresponding adjustments to indoor temperature and wind speed. For example, if the system finds that a user typically returns home around 5 pm and enters a "comfort" mode (temperature is adjusted to 22 ℃, wind speed is adjusted to low), then the air conditioning device will begin adjusting temperature and wind speed at 4 pm so that the indoor environment has reached a desired state when the user returns home.
In order to further optimize the energy efficiency, the intelligent cooperative control between the air conditioning equipment is expressed by an energy efficiency optimizing function, and the specific formula is as follows:
wherein:
e (t) represents the overall energy efficiency of the air conditioning system at time t, and is a comprehensive evaluation value of the energy efficiency of all air conditioning equipment.
Ci (t) is the power consumption of the ith air conditioning equipment at time t, and represents the real-time energy consumption of the equipment. Power consumption is typically related to the operating mode of the device (e.g., cooling/heating mode, wind speed, etc.) and environmental load (e.g., outdoor temperature, room size, device efficiency, etc.).
ΔTi (T) is the temperature change of the ith air conditioning equipment at time T, and represents the temperature change amount after equipment adjustment. The temperature variation is an adjustment target made by the air conditioner according to the sensor data and the external environment prediction.
Alphai is the energy efficiency coefficient of the ith device, representing the relationship between the energy consumption and temperature regulation of the device. The energy efficiency coefficient of the equipment can be adjusted according to the type of the equipment (such as an air conditioner, an independent air conditioner and the like), environmental load, use frequency and other factors, and the value range is 1 to 5.
The real-time data of the air conditioner set on a certain day in the office building is as follows:
Air conditioner 1 (office a) with a current temperature of 24 ℃, a target temperature of 22 ℃ (temperature change of 2 ℃), power consumption of 300W, and energy efficiency coefficient α1 =3.
Air conditioner 2 (office B) with a current temperature of 23 ℃, a target temperature of 22 ℃ (temperature change of 1 ℃) and power consumption of 250W, energy efficiency coefficient α2 =2.5.
Air conditioner 3 (office C) with a current temperature of 25 ℃, a target temperature of 22 ℃ (temperature change of 3 ℃), power consumption of 350W, and energy efficiency coefficient α3 =4.
Air conditioner 4 (rest area) with current temperature of 26 ℃, target temperature of 22 ℃ (temperature change of 4 ℃), power consumption of 400W, energy efficiency coefficient α4 =5.
The air conditioner 5 (conference room) has a current temperature of 22 ℃, a target temperature of 22 ℃ (temperature change of 0 ℃), power consumption of 200W, and an energy efficiency coefficient α5 =2.
The energy efficiency optimization function of the air conditioning system is calculated as follows:
E(t)=200+100+262.5+320=882.5
thus, the overall energy efficiency of the air conditioning system at time t is 882.5.
The air conditioning equipment not only adjusts the temperature according to the real-time environmental data, but also analyzes the life rule of the user through an embedded machine learning algorithm. For example, air conditioner 1 is located in an office of employee a, who enters the office at about 7 a.m. and exits at 6a day. If the machine learning algorithm recognizes this rule in the history, the air conditioning will start pre-cooling to 22 ℃ before 6 a.m. and switch to energy saving mode after the employee leaves. Therefore, the temperature change quantity DeltaT1 (T) of the air conditioner 1 is affected by the life rule of staff, and the aim of energy conservation is achieved.
The behavior habit of the user is changed, for example, the employee a starts to enter the office earlier or later, the machine learning algorithm of the air conditioner 1 can learn again according to the new data, and the temperature control strategy is adjusted, so that the optimal comfort and energy efficiency are always ensured.
The scheme optimizes the cooperative work of the air conditioning equipment through machine learning and intelligent algorithm, and adjusts the temperature, wind speed and energy efficiency of the air conditioner in multiple dimensions through an energy efficiency optimizing function. The machine learning algorithm not only can adjust the running state of the air conditioner in real time, but also can make personalized adjustment according to the behavior rule of the user and the change of the external environment. Through a reasonable energy efficiency optimization function, the system can reduce energy waste to the maximum extent and realize the efficient utilization of energy.
Example 2:
Referring to the flowchart of fig. 3, in this case, a detailed description is developed about a "multi-dimensional energy-saving optimization algorithm" of an air conditioner internet of things control method. According to the method, the refrigerating or heating intensity of the air conditioner is dynamically adjusted according to the temperature requirements of different areas in the room, so that the energy-saving aim is achieved. In particular, the air conditioning system adjusts the operation mode according to the temperature difference (Δti) of the region, thereby optimizing the energy use efficiency.
An intelligent air conditioning system is arranged in a modern office building, a plurality of rooms and areas are arranged in the office building, and the temperature requirements of each area are different. For example, the area near the window is exposed to higher solar radiation and therefore higher temperatures in summer, while the area near the interior wall is lower in temperature because it is less affected by the external environment. In order to ensure comfort and save energy, the air conditioning system must be adjusted according to the temperature difference of different areas, and in particular, the air conditioning equipment must be precisely adjusted and controlled for the areas with strong solar radiation and the areas with low temperature.
To achieve this precise adjustment, the air conditioning apparatus calculates the temperature difference (Δti) for each zone using the following formula:
wherein:
DeltaTk is the temperature difference in the kth zone, representing the difference in temperature of the zone from the outside environment;
Tk is the actual temperature of the kth region, and is detected in real time by a temperature sensor of the air conditioning equipment;
Text is the external ambient temperature, typically obtained through a weather API or environmental monitoring device;
Lambdak is the thermal conductivity of the kth region, representing the heat exchange efficiency between the region and the external environment;
t is time, representing the duration in the calculation of the formula, typically in minutes or hours.
The core of the formula is to calculate the temperature change inside the region according to the heat conduction coefficient of the region and the change of the external environment temperature. The calculation process can dynamically reflect the relation between the temperature in the area and the external environment, so as to determine whether the air conditioning equipment needs to increase the refrigerating or heating intensity.
The air conditioning system is arranged in a meeting room of an office building, and acquires environment data in real time through the sensor and the API interface. The conference room is divided into two areas, a window area and an inner wall area. To better understand the actual application of the algorithm, the following data are set forth:
External ambient temperature t=30 ℃, which is the real-time external temperature obtained through the weather API.
Window area temperature T Window = 28 ℃, which is the actual temperature of the window area of the conference room.
The interior wall region temperature T Wall = 25 ℃, which is the actual temperature of the conference room interior wall region.
The heat transfer coefficient λ Window =0.2 (heat transfer coefficient in window area), λ Wall =0.1 (heat transfer coefficient in interior wall area), these coefficients represent the heat exchange efficiency of each area with the external environment, the value range is generally 0.1 to 0.5, the specific value depends on the material characteristics and the architectural design.
Time t=1 hour (the temperature difference is recalculated and the cooling/heating mode is adjusted every hour for the air conditioning apparatus is set).
First, the temperature difference (Δti) of the two regions is calculated using a formula:
Window area:
ΔT Window＝28-30·(1-e-0.21)
ΔT Window＝28-30·(1-e-0.2)
ΔT Window≈28-30·(1-0.8187)=28-30·0.1813≈28-5.44=22.56°C
therefore, the window area temperature difference is 22.56 ℃.
Inner wall area:
ΔT Wall＝25-30·(1-e-0.1·1)
ΔT Wall＝25-30·(1-e-0.1)
ΔT Wall≈25-30·(1-0.9048)=25-30·0.0952≈25-2.86=22.14°C
thus, the temperature difference in the interior wall region was 22.14 ℃.
Dynamic adjustment of air conditioning modes
According to the calculation result, the air conditioning system can dynamically adjust the working mode according to the temperature difference of the area. Since the temperature difference in the window-leaning area is slightly larger (22.56 ℃ C. Compared with 22.14 ℃ C.), the area needs stronger refrigeration, and the temperature difference in the inner wall area is smaller, the energy consumption can be reduced by slightly adjusting the wind speed and the temperature of the air conditioner. Thus, the air conditioning system may employ the following strategies:
and in the window leaning area, stronger refrigeration is needed, the refrigerating power of the air conditioning equipment is increased, the temperature difference is reduced, and the indoor environment comfort is ensured.
The inner wall area can reduce the refrigeration intensity, even switch part of air conditioning equipment into a wind speed adjusting mode instead of forced refrigeration, and reduce the energy consumption.
The temperature difference of each area in the room is dynamically calculated, the refrigerating or heating mode of the air conditioning equipment is adjusted according to the temperature difference change, and the air conditioning system can be intelligently adjusted according to the requirements of different areas, so that the energy utilization efficiency is greatly improved. For example, in a conference room, the window area requires more cool air supply due to being more affected by solar radiation, while the interior wall area can meet comfort requirements through wind speed adjustment, thereby reducing the overall energy consumption of the air conditioning system.
An intelligent air conditioning system is arranged in a modern office building, and a plurality of areas are arranged in the building, wherein the temperature requirements of each area are different. For example, the area near the window is higher in summer due to stronger solar radiation, while the area far from the window is lower in temperature. In order to ensure that each zone is maintained in a comfortable environment and to maximize energy savings, the air conditioning system needs to be precisely adjusted to the needs of the different zones. At this time, external environmental data (e.g., temperature, humidity, sunlight intensity) will be used to predict indoor temperature changes, thereby adjusting the air conditioner operation mode in advance.
The air conditioning system predicts the change in indoor temperature by the following formula:
ΔTpred＝α1Text(t)+α2Hext(t)-β·SolarRadiation(t)
wherein:
deltatpred is the predicted indoor temperature change;
Text (t) is an external temperature reflecting a temperature change of an external environment;
hext (t) is the external humidity, and influences the refrigerating or heating effect of the air conditioner;
SolarRadiation (t) is sunlight intensity, determining the influence of sunlight on the room temperature;
Alpha1、α2, p are adjustment coefficients related to the external environment, which coefficients can be adjusted according to the specific area or environment.
An air conditioning system is arranged in a conference room, and the conference room is divided into two areas, namely a leaning window and an inner wall, which are respectively affected by different external environments. For ease of calculation, the following data are set:
The external temperature Text = 30 ℃;
The external humidity Hext = 60%;
Solar intensity SolarRadiation = 800W/m2;
The adjustment coefficients of the air conditioning system are alpha1＝0.6、α2 =0.3 and beta=0.1.
The indoor temperature change is calculated by using the formula, external environment data is input first, and prediction is performed according to the adjustment coefficient.
△Tpred=0.6×30+0.3×60-0.1×800
The calculation process is as follows:
ΔTpred=18+18-80=-44°C
This result shows that the indoor temperature is expected to drop by 44 ℃ depending on the external environmental conditions (the setting is an idealized model, and more complex corrections are made in practical applications). By the prediction result, the air conditioning system can know the environmental change in advance and adjust the working mode of the air conditioner (such as improving the heating or cooling strength).
Based on the prediction, the air conditioning system may take the following actions:
1. The temperature change of the window area is more remarkable due to stronger solar radiation. Therefore, the air conditioning system may enhance the cooling capacity of the area to cope with the rapidly rising indoor temperature.
2. The area with smaller temperature difference (inner wall area) is less affected by the external environment, and the air conditioner can reduce the refrigeration intensity and even adjust to an air flow mode so as to reduce the energy consumption.
For example, the air conditioning system calculates the temperature difference in the window area to be 4 ℃ and the temperature difference in the interior wall area to be 2 ℃. Based on these results, the air conditioning system will take the following strategies:
And in the window leaning area, the air conditioning equipment can enhance the refrigerating strength, ensure that the room temperature is maintained within a preset range and avoid overheating.
In the inner wall area, the air conditioning equipment can reduce the refrigerating force, maintain the comfortable temperature and properly adjust the wind speed to save energy.
By adopting the multidimensional energy-saving optimization algorithm, the air conditioning system can predict the change of the external environment in real time and dynamically adjust the working mode. Compared with the traditional air conditioner control mode, the method can effectively reduce energy consumption and improve energy utilization efficiency. For example, in a conference room, since the window area is greatly affected by solar radiation, the air conditioning apparatus needs to provide more cool air, while the temperature difference of the interior wall area is small, and the air conditioning system maintains a pleasant environment by slightly adjusting the wind speed and temperature. In this way, the air conditioning system achieves a high energy saving while ensuring comfort.
The air conditioning equipment load balancing algorithm dynamically adjusts the working state of equipment by monitoring the use frequency (Ui) and the load (Li) of the air conditioner, so that more efficient energy-saving control is realized. Through the load balancing algorithm, the air conditioning equipment can dynamically adjust the running mode according to the respective use condition and load condition, so that the energy consumption of the whole air conditioning system is optimized.
It is set that a plurality of air conditioning apparatuses are installed in a certain office building in order to maintain the temperature of the entire building within a comfortable range. The frequency of use and the load condition of each air conditioning equipment are different, some air conditioners are frequently used in peak hours, and some air conditioners are in a standby state for a long time. In order to optimize energy use and load distribution, the air conditioning system needs to dynamically adjust the working state according to the use frequency and load condition of each equipment, so that the load distribution is more balanced, and the energy saving effect is achieved.
The load balancing of the air conditioning system is calculated by the following formula:
wherein:
LoadFactori denotes a load factor of the i-th air conditioner, indicating a load duty ratio of the apparatus;
Li is the current load of the ith air conditioner, and is generally determined by parameters such as power consumption, refrigeration or heating intensity and the like of the air conditioner;
ui is the use frequency of the ith air conditioner, and represents the working frequency of air conditioning equipment and reflects the working load of the air conditioner;
Thresholdi is a frequency threshold for each air conditioner, which is used to determine whether the device enters an energy saving mode;
n is the total number of air conditioning units for calculating the sum of the loads of all units.
5 Air conditioning devices are arranged in one office building, and are respectively numbered as air conditioner 1 to air conditioner 5. The following are the frequency of use and load data for each air conditioner:
based on these data, setting the air conditioning system requires dynamic adjustment of the operation mode of each device according to load balancing. First, a load factor of each air conditioner is calculated.
According to the formula:
first, the total load of the air conditioning system is calculated
Then, the load factor of each air conditioner is calculated:
air conditioner 1:
air conditioner 2:
air conditioner 3:
Air conditioner 4:
Air conditioner 5:
And obtaining the load factors of the air conditioners according to the calculation result. The higher the load factor is, the larger the load ratio of the air conditioner is, and the running mode of the air conditioner needs to be adjusted correspondingly, so that overload is avoided and energy conservation is realized. For example, the air conditioner 3 has the highest load factor, and the air conditioning system can reduce the running load by adjusting the working mode of the air conditioner so as to avoid energy waste.
In addition, the air conditioning system dynamically adjusts the operating state of the equipment according to the frequency of use (Ui) and the load condition of each equipment. If the frequency Ui of the air conditioner is lower than the Thresholdi, the device can enter an energy-saving mode to reduce the working strength and thus the energy consumption.
For example:
The air conditioner 1 is used 8 times/day, 6 times/day above its threshold value, so it will continue to operate in normal mode;
The air conditioner 2 is used for 6 times/day, and is slightly higher than the threshold value for 5 times/day, and the air conditioning system sets the air conditioner to be slightly lower in refrigerating/heating intensity so as to reduce unnecessary energy consumption;
the air conditioner 5 is used 3 times/day, 3 times/day below its threshold, and the system will consider to adjust it to an energy saving mode to reduce energy waste.
In application, the load balancing algorithm of the air conditioning system may help reduce unnecessary load distribution. The system can dynamically adjust according to different use scenes by monitoring the use frequency and the load condition of each air conditioner in real time. When some air conditioning equipment is in an idle state for a long time, the system can adjust the air conditioning equipment to an energy-saving mode, so that energy waste is avoided. In addition, through load balancing, the system can ensure that the operation load of each air conditioner is not excessive, thereby prolonging the service life of the equipment and reducing the maintenance cost.
By introducing a load balancing algorithm, the air conditioning system can dynamically adjust the working mode according to the use frequency and the load condition of each equipment, and the running of the air conditioning equipment is ensured to be more efficient. In this case, the air conditioning system successfully adjusts the heavier duty equipment to the energy saving mode, reducing energy consumption and ensuring overall temperature control. By adjusting the load factor, the air conditioning system can achieve the aims of energy conservation and cost optimization while ensuring a comfortable indoor environment.
The control method is set for an office building and comprises a plurality of air conditioning devices, and each air conditioner can be intelligently adjusted according to different time periods, workdays and rest days and requirements of the day and night so as to minimize energy consumption and ensure regional comfort.
10 Air conditioners are set in the office building, and the aim is to optimize the operation of each air conditioner through intelligent regulation and control so as to realize energy-saving and efficient operation. In different time periods, the air conditioner needs to be used differently, for example, the air conditioner needs to maintain a lower temperature in the office hours in the daytime, and the set temperature can be properly increased at night. In order to optimize energy use, the system needs to dynamically allocate the priority of each air conditioner according to different requirements of working days, rest days, daytime and night.
In this system, the objective of overall optimization of energy consumption is achieved by the following formula:
Wherein Eopt is the optimized energy consumption of the whole system, Pi (t) is the power requirement of the ith air conditioner at time t, etai is the energy efficiency coefficient of the ith air conditioner, and fi (t) is the priority function of the ith air conditioner.
Pi (t) represents the power demand of the ith air conditioner. This value depends on factors such as ambient temperature, set temperature, room area, etc.
Ηi is the energy efficiency coefficient of the ith air conditioner. In general, the energy efficiency coefficient has a value ranging from 0.8 to 1.2, where 1.0 represents the energy efficiency of the air conditioner under the optimal working condition.
Fi (t) is a priority function which is dynamically adjusted according to the use condition, the regional requirement, the time period and other factors of the air conditioner. The value of the function ranges from 0 to 1,1 indicates that the priority of the air conditioner is highest in the time period, and 0 indicates that the priority of the air conditioner is lowest.
Air-conditioning equipment in the building is set to be respectively numbered as air-conditioner 1 to air-conditioner 10, and each air-conditioner has different energy efficiency, frequency of use and priority settings. The following are part of the data of the air conditioning system:
Setting the current time as a daytime period, the optimized energy consumption of the whole system is calculated according to the priority, the energy efficiency coefficient and the power requirement of each air conditioner.
The settings require calculation of the energy consumption of each air conditioner over a period of time during the day (e.g., 8:00am to 6:00 pm). By adopting the formula, the energy efficiency power requirement of each air conditioner is calculated, and the overall optimized consumption of the system is obtained.
Air conditioner 1:
P1(t)＝2.5kW,η1＝1.0,f1(t)=0.9
Energy1=2.5·1.0·0.9=2.25kWh
air conditioner 2:
P2(t)＝2.0kW,η2＝0.9,f2(t)=0.8
Energy2==2.0·0.9·0.8==1.44kWh
air conditioner 3:
P3(t)＝3.0kW,η3＝1.1,f3(t)=1.0
Energy3=3.0·1.1·1.0=3.30kWh
Air conditioner 4:
P4(t)＝1.5kW,η4＝1.0,f4(t)=0.6
Energy4=1.5·1.0·0.6=0.90kWh
Air conditioner 5:
P5(t)＝1.2kW,η5＝0.8,f5(t)=0.5
Energy5=1.2·0.8·0.5=0.48kWh
and similarly, calculating the energy consumption of other air conditioners in the period. Finally, the optimal energy consumption of all air conditioners can be obtained by summing up:
through an optimization algorithm, the system can dynamically adjust the working mode of each air conditioner according to the priority and the energy efficiency coefficient of the air conditioner. For example, an air conditioner (such as air conditioner 3) used at high frequency during the daytime will maintain a higher priority, reducing the running time while ensuring regional comfort. While the air conditioner (such as air conditioner 5) used at low frequency can realize energy saving by reducing the working strength. During night time, the system reduces the work load of all air conditioners, and further reduces the energy consumption.
Through the multidimensional energy-saving optimization algorithm, the air conditioning system can dynamically adjust the running mode of the equipment according to different time periods, equipment priorities and energy efficiency characteristics, so that the energy-saving effect is maximized, and a comfortable indoor environment is ensured. By the control method, energy waste can be obviously reduced, operation cost is reduced, and overall operation efficiency of the system is improved.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.