Real-time monitoring system and intelligent adjustment method of building energy consumption based on the Internet of Things

Abstract

The present invention discloses a real-time monitoring system and intelligent adjustment method for building energy consumption based on the Internet of Things, and belongs to the technical field of energy consumption monitoring and adjustment. The real-time monitoring system for building energy consumption based on the Internet of Things includes a real-time monitoring module, an energy consumption analysis module and an energy consumption adjustment module. The present invention solves the problem that the existing patents can only monitor the power energy consumption of the building, and cannot adjust the energy consumption of the building when the energy consumption is high, which is inconvenient to use. The present invention improves the accuracy of energy consumption analysis of energy-consuming equipment inside the building. By generating the operating power adjustment level of the energy-consuming equipment inside the building, the operation of the energy-consuming equipment can be accurately controlled. When the energy consumption inside the building is high, the operating power of the energy-consuming equipment inside the building is adjusted to reduce energy consumption. At the same time, the operating power of the energy-consuming equipment inside the building is adjusted according to the comfort of the user, which improves the energy saving rate of the building while making the human body more comfortable.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption monitoring and regulation, and in particular to a real-time monitoring system for building energy consumption and an intelligent regulation method based on the Internet of Things. Background Art

[0002] The supervision and energy-saving transformation of building energy consumption first requires building energy consumption detection and analysis, which requires the collection and processing of information such as temperature, humidity, and electricity consumption in all rooms in the building.

[0003] A Chinese patent with publication number CN209149117U discloses a building energy consumption monitoring system, including a box body, a control box and a fuse box. A hinge symmetrical up and down is fixedly installed on the left front side of the box body, a cover plate is rotatably installed between the two hinges, an electromagnetic lock is fixedly installed on the middle right side of the cover plate, an adsorption block matched with the electromagnetic lock is fixedly installed on the middle right side of the box body, a control box is fixedly installed on the front side of the dry plate, equidistantly arranged electric meters are installed on the inside of the box body, a wire passing tube matched with the electric meter is fixedly installed on the inside of the box body, a wire is arranged inside the wire passing tube, and the electric meter is electrically connected to the external electrical equipment through the wire. The building energy consumption monitoring system detects the power consumption and has an intelligent control function. It is easy to use and has strong safety, good sealing effect, and the effect of avoiding the occurrence of power theft, which can meet the use requirements.

[0004] In actual use, the energy consumption monitoring system of the above patent can only monitor the power consumption of the building. When the energy consumption is high, it cannot adjust the energy consumption of the building, which is inconvenient to use. Therefore, it does not meet the existing needs. In this regard, we propose a real-time building energy consumption monitoring system and intelligent adjustment method based on the Internet of Things. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time monitoring system and intelligent adjustment method for building energy consumption based on the Internet of Things, which improves the accuracy of energy consumption analysis of energy-consuming equipment inside the building, can accurately control the operation of energy-consuming equipment, and adjust the operating power of energy-consuming equipment inside the building when the energy consumption inside the building is high to reduce energy consumption. At the same time, the operating power of energy-consuming equipment inside the building is adjusted according to the comfort of the user, thereby improving the energy saving rate of the building and making the human body more comfortable, solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a real-time monitoring system for building energy consumption based on the Internet of Things, comprising:

[0007] The real-time monitoring module is used to identify energy-consuming equipment in the building and collect various parameter data inside the building in real time. The various parameter data inside the building include parameter signals of the internal environment of the building, relevant characteristic parameters of each energy-consuming equipment and comfort data of users inside the building, and the collected data are classified and stored.

[0008] The energy consumption analysis module is used to establish an energy consumption analysis model and input the relevant characteristic parameters of the energy consumption equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building collected in real time into the energy consumption analysis model to obtain the energy consumption analysis results;

[0009] The energy consumption regulation module is used to determine the operating power regulation coefficient of the energy consumption equipment in the building according to the energy consumption analysis results of the energy consumption equipment in the building and the comfort data of the current users in the building, and to judge whether the regulation results of the energy consumption equipment in the building are optimal according to the comfort data of the users in the building.

[0010] Preferably, the real-time monitoring module includes:

[0011] Data collection module, used to identify energy-consuming equipment in the building and collect various parameter data inside the building;

[0012] The data classification module is used to classify the various parameter data collected from the internal environment of the building and store them separately according to the classification results.

[0013] Preferably, the data acquisition module comprises:

[0014] The first data acquisition module is used to collect parameter data of the internal environment of the building, and the parameter data of the environment includes ambient temperature, light intensity and ambient humidity;

[0015] The second data collection module is used to identify the energy-consuming equipment in the building and collect relevant characteristic parameters of each energy-consuming equipment, wherein the relevant characteristic parameters include the heat dissipation temperature of the energy-consuming equipment and the operating power of the energy-consuming equipment;

[0016] The third data collection module is used to collect comfort data of users inside the building, and the comfort data includes hot, cold and moderate.

[0017] Preferably, the data acquisition module further includes:

[0018] A first real-time monitoring module, used for real-time monitoring of data transmission delay data of the first data acquisition module and the second data acquisition module;

[0019] The first delay coefficient acquisition module is used to acquire a first delay coefficient using the data transmission delay data of the first data acquisition module and the second data acquisition module; wherein the first delay coefficient is acquired by the following formula: Among them, Y 01 represents the first delay coefficient; y 01 Represents the first coefficient parameter; y 02 represents the second coefficient parameter; and the first coefficient parameter and the second coefficient parameter are obtained by the following formula: Among them, y 01 represents the first coefficient parameter; n represents the number of delayed data transmissions of the first data acquisition module; m represents the number of undelayed data transmissions of the first data acquisition module; T i represents the actual data transmission duration corresponding to the i-th data delay of the first data acquisition module; T ci represents the theoretical data transmission duration corresponding to the i-th data delay of the first data acquisition module; T yi T represents the actual data transmission duration corresponding to the i-th data without delay of the first data acquisition module; cyi Indicates the theoretical data transmission duration corresponding to the i-th data transmission without delay of the first data acquisition module; Among them, y 02 represents the second coefficient parameter; p represents the number of delayed data transmissions of the second data acquisition module; q represents the number of undelayed data transmissions of the second data acquisition module; T j represents the actual data transmission duration corresponding to the jth data delay of the second data acquisition module; T cj T represents the theoretical data transmission duration corresponding to the jth data delay of the second data acquisition module; yj T represents the actual data transmission duration corresponding to the jth data without delay of the second data acquisition module; cyj Indicates the theoretical data transmission duration corresponding to the j-th data without delay of the second data acquisition module;

[0020] A second real-time monitoring module, used for real-time monitoring of the data transmission delay data of the second data acquisition module and the third data acquisition module;

[0021] The second delay coefficient acquisition module is used to acquire the second delay coefficient by using the data transmission delay data of the second data acquisition module and the third data acquisition module; wherein the second delay coefficient is acquired by the following formula: Among them, Y 02 represents the second delay coefficient; y 02 Represents the second coefficient parameter; y 03 represents a third coefficient parameter; and the third coefficient parameter is obtained by the following formula: Among them, y 03 represents the third coefficient parameter; r represents the number of delayed data transmissions of the third data acquisition module; e represents the number of undelayed data transmissions of the third data acquisition module; T trepresents the actual data transmission duration corresponding to the tth data delay of the third data acquisition module; T ct T represents the theoretical data transmission duration corresponding to the tth data delay of the third data acquisition module; yt T represents the actual data transmission duration corresponding to the tth data without delay of the third data acquisition module; cyt Indicates the theoretical data transmission duration corresponding to the tth data without delay of the third data acquisition module;

[0022] A comparison module, configured to compare the first delay coefficient with the second delay coefficient to obtain a coefficient difference;

[0023] The data transmission rate adjustment module is used to compare the coefficient difference with a preset difference threshold, and adjust the data transmission rate of the first data acquisition module, the second data acquisition module and the third data acquisition module when the coefficient difference exceeds the preset difference threshold.

[0024] Preferably, the data transmission rate adjustment module includes:

[0025] A comparison execution module, used for comparing the coefficient difference with a preset difference threshold to obtain a comparison result;

[0026] A data transmission rate extraction module, used for retrieving the current data transmission rates of the first data acquisition module, the second data acquisition module and the third data acquisition module when the comparison result indicates that the coefficient difference exceeds a preset difference threshold;

[0027] A transmission rate adjustment execution module, used to adjust the data transmission rate of the first data acquisition module, the second data acquisition module and the third data acquisition module respectively by using the comparison result between the coefficient difference and the preset difference threshold value in combination with the first delay coefficient and the second delay coefficient;

[0028] The adjusted data transmission rate of the first data acquisition module is obtained by the following formula: Among them, V 01t represents the adjusted data transmission rate corresponding to the first data acquisition module; V 01 represents the data transmission rate before adjustment corresponding to the first data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient;

[0029] In addition, the adjusted data transmission rate of the second data acquisition module is obtained by the following formula: Among them, V 02t represents the adjusted data transmission rate corresponding to the second data acquisition module; V02 represents the data transmission rate before adjustment corresponding to the second data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient;

[0030] In addition, the adjusted data transmission rate of the third data acquisition module is obtained by the following formula: Among them, V 03t represents the adjusted data transmission rate corresponding to the third data acquisition module; V 03 represents the data transmission rate before adjustment corresponding to the third data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 02 Represents the second delay coefficient.

[0031] Preferably, the energy consumption analysis module includes:

[0032] A data receiving module is used to receive the collected various parameter signals of the internal environment of the building, the relevant characteristic parameters of the energy-consuming equipment and the comfort data of the users in the building;

[0033] The model building module is used to obtain the historical data of key characteristic parameters of the equipment inside the building, and train and fine-tune the historical data to obtain the energy consumption analysis model;

[0034] The data analysis module is used to input the relevant characteristic parameters of the energy consumption equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building into the energy consumption analysis model to obtain the energy consumption analysis results;

[0035] Preferably, the analysis process of the energy consumption analysis module specifically includes:

[0036] Receiving relevant characteristic parameters of energy-consuming equipment inside the building, and determining key characteristic parameters among the characteristic parameters;

[0037] Acquire historical data of key feature parameters, and train the historical data of key feature parameters to obtain a pre-trained model;

[0038] Receive an energy consumption analysis instruction, extract a characteristic parameter template in the energy consumption analysis instruction, and fine-tune the pre-trained model according to the characteristic parameter template to obtain an energy consumption analysis model;

[0039] The relevant characteristic parameters of the energy-consuming equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building collected in real time are input into the energy consumption analysis model to obtain the energy consumption analysis results.

[0040] Preferably, the adjustment process of the energy consumption adjustment module specifically includes:

[0041] Determine the comfort data of users inside the building based on the temperature inside the building, and preset the operating power adjustment coefficient of energy-consuming equipment;

[0042] Determine the operating power adjustment coefficient of the energy-consuming equipment in the building based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building;

[0043] Compare the operating power adjustment coefficient of the energy-consuming equipment in the building with the preset operating power adjustment coefficient of the energy-consuming equipment to obtain the operating power adjustment coefficient deviation of the energy-consuming equipment.

[0044] The operating power regulation coefficient deviation of the energy-consuming equipment is calculated by differentiation to obtain the operating power regulation coefficient deviation change rate of the energy-consuming equipment;

[0045] The deviation change rate of the operating power adjustment coefficient of the energy-consuming equipment is input into the fuzzy controller, the adjustment level of the energy-consuming equipment is output, and the energy-consuming equipment is adjusted accordingly;

[0046] After the adjustment is completed, the comfort data of the people inside the building is collected to determine whether the comfort data of the people inside the building is moderate. If not, the energy-consuming equipment will continue to be adjusted until the user's comfort data is moderate.

[0047] Preferably, the energy consumption adjustment module includes:

[0048] The adjustment module is used to determine the operating power adjustment coefficient of the energy-consuming equipment inside the building according to the energy consumption analysis result, and adjust the operating power of the energy-consuming equipment inside the building according to the adjustment coefficient;

[0049] The judgment module is used to judge whether the comfort data of the people inside the building is moderate based on the real-time collected comfort data of the people inside the building.

[0050] The intelligent adjustment method of the building energy consumption real-time monitoring system based on the Internet of Things includes the following steps:

[0051] Step 1: Identify the energy-consuming equipment in the building, collect various parameter data inside the building in real time, and classify and store the collected data.

[0052] Step 2: Establish an energy consumption analysis model, and input the real-time collected data of various parameters inside the building into the energy consumption analysis model to obtain energy consumption analysis results;

[0053] Step 3: Determine the operating power adjustment coefficient of the energy-consuming equipment in the building based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building, and judge whether the adjustment results of the energy-consuming equipment in the building are optimal based on the comfort data of the users in the building;

[0054] Step 4: If the optimum is reached, stop adjusting the energy-consuming equipment; if the optimum is not reached, continue adjusting the operating power of the energy-consuming equipment until the user's comfort data is moderate.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] The present invention conducts energy consumption analysis on energy consumption equipment inside a building by establishing an energy consumption analysis model, and combines relevant characteristic parameters of the energy consumption equipment inside the building, multiple parameter signals of the internal environment of the building, and comfort data of users inside the building, thereby improving the accuracy of energy consumption analysis of energy consumption equipment inside the building. By judging the comfort data of users inside the building and combining the energy consumption analysis results of the energy consumption equipment inside the building to generate the operating power adjustment level of the energy consumption equipment inside the building, the operation of the energy consumption equipment can be accurately controlled. When the energy consumption inside the building is high, the operating power of the energy consumption equipment inside the building is adjusted to reduce energy consumption. At the same time, the operating power of the energy consumption equipment inside the building is adjusted according to the comfort of the users, thereby improving the energy saving rate of the building and making the human body more comfortable. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a module schematic diagram of the real-time monitoring system for building energy consumption based on the Internet of Things of the present invention;

[0058] Figure 2 The present invention is a flow chart of the intelligent adjustment method of the real-time monitoring system for building energy consumption based on the Internet of Things. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. According to 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.

[0060] In order to solve the problem that the existing energy consumption monitoring system can only monitor the power consumption of the building in actual use, and cannot adjust the energy consumption of the building when the energy consumption is high, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0061] The real-time monitoring system of building energy consumption based on the Internet of Things includes:

[0062] The real-time monitoring module is used to identify energy-consuming equipment in the building and collect various parameter data inside the building in real time. The various parameter data inside the building include parameter signals of the internal environment of the building, relevant characteristic parameters of each energy-consuming equipment and comfort data of users inside the building. The collected data is classified and stored, and the energy consumption of energy-consuming equipment inside the building can be adjusted according to various factors to achieve precise adjustment.

[0063] The energy consumption analysis module is used to establish an energy consumption analysis model, and input the relevant characteristic parameters of the energy consumption equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building into the energy consumption analysis model to obtain the energy consumption analysis results. By establishing an energy consumption analysis model, the energy consumption of the energy consumption equipment inside the building is analyzed, thereby ensuring the accuracy of the energy consumption analysis.

[0064] The energy consumption regulation module is used to determine the operating power regulation coefficient of the energy consumption equipment inside the building according to the energy consumption analysis results of the energy consumption equipment inside the building and the comfort data of the current users inside the building, and judge whether the regulation results of the energy consumption equipment inside the building are optimal according to the comfort of the users inside the building. The operating power of the energy consumption equipment is adjusted in combination with the comfort of the people inside the building. This can not only adjust the energy consumption data of the energy consumption equipment inside the building and reduce energy consumption, but also ensure the comfort of the people inside the building, and is more practical.

[0065] Real-time monitoring module, including:

[0066] The data acquisition module is used to identify energy-consuming equipment in the building and collect various parameter data inside the building.

[0067] The data classification module is used to classify the various parameter data collected from the internal environment of the building and store them separately according to the classification results.

[0068] Data acquisition module, including:

[0069] The first data acquisition module is used to collect parameter data of the internal environment of the building, and the parameter data of the environment includes ambient temperature, light intensity and ambient humidity.

[0070] The second data collection module is used to identify the energy-consuming equipment in the building and collect relevant characteristic parameters of each energy-consuming equipment, wherein the relevant characteristic parameters include the heat dissipation temperature of the energy-consuming equipment and the operating power of the energy-consuming equipment.

[0071] The third data collection module is used to collect comfort data of users inside the building, and the comfort data includes hot, cold and moderate.

[0072] When the hot and cold postures of people inside the building are to wipe sweat with their hands, fan themselves with their hands, or roll up their sleeves, the hot and cold state of people inside the building is that they feel hot, and the comfort level at this time is hot.

[0073] When the hot and cold postures of the people inside the building are hugging their arms, raising their hands and breathing out, and hugging their necks with both hands, the hot and cold state of the people inside the building is that the people inside the building feel cold, and the comfort level at this time is cold.

[0074] When the hot and cold postures of people inside the building are in a normal state, the hot and cold postures of people inside the building are comfortable, and the comfort level at this time is moderate. When the operating power of the energy-consuming equipment inside the building is adjusted, when the user comfort level is moderate, the operating power adjustment result of the current energy-consuming equipment is the best. The energy consumption of the energy-consuming equipment inside the building can be adjusted according to the comfort level of the users inside the building, which can ensure the comfort of the users while adjusting the energy consumption of the building, and is more practical.

[0075] Specifically, the data acquisition module further includes:

[0076] A first real-time monitoring module, used for real-time monitoring of data transmission delay data of the first data acquisition module and the second data acquisition module;

[0077] The first delay coefficient acquisition module is used to acquire a first delay coefficient using the data transmission delay data of the first data acquisition module and the second data acquisition module; wherein the first delay coefficient is acquired by the following formula: Among them, Y 01 represents the first delay coefficient; y 01 Represents the first coefficient parameter; y 02 represents the second coefficient parameter; and the first coefficient parameter and the second coefficient parameter are obtained by the following formula: Among them, y 01 represents the first coefficient parameter; n represents the number of delayed data transmissions of the first data acquisition module; m represents the number of undelayed data transmissions of the first data acquisition module; T i represents the actual data transmission duration corresponding to the i-th data delay of the first data acquisition module; T ci represents the theoretical data transmission duration corresponding to the i-th data delay of the first data acquisition module; T yi T represents the actual data transmission duration corresponding to the i-th data without delay of the first data acquisition module; cyi Indicates the theoretical data transmission duration corresponding to the i-th data transmission without delay of the first data acquisition module; Among them, y 02represents the second coefficient parameter; p represents the number of delayed data transmissions of the second data acquisition module; q represents the number of undelayed data transmissions of the second data acquisition module; T j represents the actual data transmission duration corresponding to the jth data delay of the second data acquisition module; T cj T represents the theoretical data transmission duration corresponding to the jth data delay of the second data acquisition module; yj T represents the actual data transmission duration corresponding to the jth data without delay of the second data acquisition module; cyj Indicates the theoretical data transmission duration corresponding to the j-th data without delay of the second data acquisition module;

[0078] A second real-time monitoring module, used for real-time monitoring of the data transmission delay data of the second data acquisition module and the third data acquisition module;

[0079] The second delay coefficient acquisition module is used to acquire the second delay coefficient by using the data transmission delay data of the second data acquisition module and the third data acquisition module; wherein the second delay coefficient is acquired by the following formula: Among them, Y 02 represents the second delay coefficient; y 02 Represents the second coefficient parameter; y 03 represents a third coefficient parameter; and the third coefficient parameter is obtained by the following formula: Among them, y 03 represents the third coefficient parameter; r represents the number of delayed data transmissions of the third data acquisition module; e represents the number of undelayed data transmissions of the third data acquisition module; T t represents the actual data transmission duration corresponding to the tth data delay of the third data acquisition module; T ct T represents the theoretical data transmission duration corresponding to the tth data delay of the third data acquisition module; yt T represents the actual data transmission duration corresponding to the tth data without delay of the third data acquisition module; cyt Indicates the theoretical data transmission duration corresponding to the tth data without delay of the third data acquisition module;

[0080] A comparison module, configured to compare the first delay coefficient with the second delay coefficient to obtain a coefficient difference;

[0081] The data transmission rate adjustment module is used to compare the coefficient difference with a preset difference threshold, and adjust the data transmission rate of the first data acquisition module, the second data acquisition module and the third data acquisition module when the coefficient difference exceeds the preset difference threshold.

[0082] The technical effect of the above technical solution is: by introducing the first and second real-time monitoring modules, the data transmission delay data between the first data acquisition module, the second data acquisition module and the third data acquisition module can be monitored in real time. This real-time monitoring mechanism helps to timely discover problems in data transmission, such as network congestion, equipment failure, etc., and provides data support for subsequent optimization and adjustment.

[0083] The first and second delay coefficients (Y 01 and Y 02 ), these coefficients not only take into account the delay of data transmission (by comparing the actual transmission time with the theoretical transmission time), but also comprehensively consider the number of delayed and undelayed transmissions. This calculation method can more comprehensively reflect the performance and stability of the data acquisition module, and provide a scientific basis for subsequent optimization and adjustment.

[0084] By comparing the first delay coefficient and the second delay coefficient and calculating the coefficient difference, the difference in data transmission efficiency between different data acquisition modules can be intuitively understood. When the coefficient difference exceeds the preset difference threshold, it means that the data transmission efficiency of some data acquisition modules is low and there may be a bottleneck. At this time, targeted adjustments to these modules can be made through the data transmission rate adjustment module to effectively improve the overall data transmission efficiency.

[0085] The entire technical solution forms a closed-loop optimization mechanism through real-time monitoring, data analysis, coefficient calculation and comparison, and dynamic adjustment of data transmission rate. This mechanism enables the data acquisition system to automatically adjust according to actual operation conditions to adapt to different network environments, device states, and data transmission requirements, thereby achieving optimal transmission efficiency and stability.

[0086] By monitoring and adjusting the data transmission rate in real time, the delay and error in data transmission can be reduced, and the reliability and stability of the system can be improved. This is especially important for data acquisition applications that require high reliability and low latency (such as industrial automation, remote monitoring, etc.).

[0087] In summary, this technical solution optimizes the data transmission efficiency between data acquisition modules through real-time monitoring, precise calculation, dynamic adjustment and other means, and improves the overall performance and stability of the system.

[0088] Specifically, the data transmission rate adjustment module includes:

[0089] A comparison execution module, used for comparing the coefficient difference with a preset difference threshold to obtain a comparison result;

[0090] A data transmission rate extraction module, used for retrieving the current data transmission rates of the first data acquisition module, the second data acquisition module and the third data acquisition module when the comparison result indicates that the coefficient difference exceeds a preset difference threshold;

[0091] A transmission rate adjustment execution module, used to adjust the data transmission rate of the first data acquisition module, the second data acquisition module and the third data acquisition module respectively by using the comparison result between the coefficient difference and the preset difference threshold value in combination with the first delay coefficient and the second delay coefficient;

[0092] The adjusted data transmission rate of the first data acquisition module is obtained by the following formula: Among them, V 01t represents the adjusted data transmission rate corresponding to the first data acquisition module; V 01 represents the data transmission rate before adjustment corresponding to the first data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient;

[0093] In addition, the adjusted data transmission rate of the second data acquisition module is obtained by the following formula: Among them, V 02t represents the adjusted data transmission rate corresponding to the second data acquisition module; V 02 represents the data transmission rate before adjustment corresponding to the second data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient;

[0094] In addition, the adjusted data transmission rate of the third data acquisition module is obtained by the following formula: Among them, V 03t represents the adjusted data transmission rate corresponding to the third data acquisition module; V 03 represents the data transmission rate before adjustment corresponding to the third data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 02 Represents the second delay coefficient.

[0095] The technical effect of the above technical solution is: the coefficient difference is compared with the preset difference threshold through the comparison execution module, and the adjustment of the data transmission rate is triggered only when the coefficient difference exceeds the threshold. This mechanism ensures the necessity of adjustment and avoids system fluctuations caused by unnecessary adjustments. At the same time, the adjusted data transmission rate is calculated by combining the coefficient difference and the delay coefficient using a specific formula, making the adjustment more accurate and reasonable.

[0096] Note that when adjusting the data transmission rate of the third data acquisition module, the second delay coefficient (Y 02 ) instead of the first delay coefficient (Y 01 ). This shows that the technical solution takes into account the differences between different data acquisition modules and adopts differentiated adjustment strategies. This strategy can better adapt to the actual performance of different modules, thereby achieving more optimized data transmission efficiency.

[0097] The entire adjustment process is dynamic, and it dynamically adjusts the data transmission rate based on the real-time monitored data transmission delay data and the calculated delay coefficient. This dynamic adaptability enables the data acquisition system to quickly respond to changes in external factors such as network environment and device status, and maintain efficient and stable data transmission.

[0098] By precisely adjusting the data transmission rate, you can reduce delays and congestion in data transmission, and improve the efficiency and reliability of data transmission. This is especially important for application scenarios that require high-speed and stable data transmission (such as real-time monitoring systems, big data processing, etc.). The adjusted system can process data faster, reduce the risk of data loss and errors, and thus improve overall performance.

[0099] This technical solution modularizes the data transmission rate adjustment process, making each module relatively independent and easy to maintain and expand. When it is necessary to add a new data acquisition module or adjust an existing module, it is only necessary to make appropriate modifications to the corresponding module without large-scale reconstruction of the entire system.

[0100] In summary, this technical solution significantly enhances the reliability and stability of the data acquisition system by accurately adjusting the data transmission rate, adopting differentiated adjustment strategies, achieving dynamic adaptability, and improving the overall performance of the system, providing strong support for efficient and stable data transmission.

[0101] Energy consumption analysis module, including:

[0102] The data receiving module is used to receive the collected various parameter signals of the internal environment of the building, the relevant characteristic parameters of the energy-consuming equipment and the comfort data of the users in the building.

[0103] The model building module is used to obtain the historical data of key characteristic parameters of internal building equipment, and to train and fine-tune the historical data to obtain an energy consumption analysis model.

[0104] The data analysis module is used to input the relevant characteristic parameters of the energy consumption equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building into the energy consumption analysis model to obtain the energy consumption analysis results.

[0105] The analysis process of the energy consumption analysis module includes:

[0106] Relevant characteristic parameters of energy-consuming equipment inside the building are received, and key characteristic parameters among the characteristic parameters are determined.

[0107] The historical data of the key feature parameters are obtained, and the historical data of the key feature parameters are trained to obtain a pre-trained model.

[0108] An energy consumption analysis instruction is received, and a characteristic parameter template in the energy consumption analysis instruction is extracted, and a pre-trained model is fine-tuned according to the characteristic parameter template to obtain an energy consumption analysis model.

[0109] The relevant characteristic parameters of the energy-consuming equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building collected in real time are input into the energy consumption analysis model to obtain the energy consumption analysis results.

[0110] The adjustment process of the energy consumption adjustment module specifically includes:

[0111] The comfort data of users inside the building is determined based on the temperature inside the building, and the operating power adjustment coefficient of the energy-consuming equipment is preset.

[0112] The operating power adjustment coefficient of the energy-consuming equipment in the building is determined based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building.

[0113] The operating power adjustment coefficient of the energy-consuming equipment in the building is compared with the preset operating power adjustment coefficient of the energy-consuming equipment to obtain the operating power adjustment coefficient deviation of the energy-consuming equipment.

[0114] The operating power regulation coefficient deviation of the energy-consuming equipment is differentiated to obtain the operating power regulation coefficient deviation change rate of the energy-consuming equipment.

[0115] The deviation change rate of the operating power adjustment coefficient of the energy-consuming equipment is input into the fuzzy controller, the adjustment level of the energy-consuming equipment is output, and the energy-consuming equipment is adjusted accordingly.

[0116] After the adjustment is completed, the comfort data of the people inside the building is collected to determine whether the comfort data of the people inside the building is moderate. If not, the energy-consuming equipment will continue to be adjusted until the user's comfort data is moderate.

[0117] Energy consumption regulation module, including:

[0118] The adjustment module is used to determine the operating power adjustment coefficient of the energy-consuming equipment inside the building according to the energy consumption analysis result, and adjust the operating power of the energy-consuming equipment inside the building according to the adjustment coefficient.

[0119] The judgment module is used to judge whether the comfort data of the people inside the building is moderate based on the real-time collected comfort data of the people inside the building.

[0120] Determine the operating power adjustment coefficient of energy-consuming equipment within the building, including:

[0121] Establish energy consumption constraint parameters and set energy consumption conversion coefficient thresholds based on the energy consumption constraint parameters.

[0122] The energy consumption conversion coefficient set is screened according to the energy consumption conversion coefficient threshold to obtain a target energy consumption conversion coefficient set, wherein the target energy consumption conversion coefficient set is an energy consumption conversion coefficient set that meets the energy consumption conversion coefficient threshold.

[0123] The optimization of adjustment parameters is performed according to the target energy consumption conversion coefficient set to generate optimized adjustment parameters. The optimized adjustment parameters are used as the operating power adjustment coefficients of the energy-consuming equipment in the building to accurately control the operation of the energy-consuming equipment. When the energy consumption inside the building is high, the operating power of the energy-consuming equipment inside the building is adjusted to reduce energy consumption. At the same time, the operating power of the energy-consuming equipment inside the building is adjusted according to the user's comfort, which improves the energy saving rate of the building while making the human body more comfortable.

[0124] The intelligent adjustment method of the building energy consumption real-time monitoring system based on the Internet of Things includes the following steps:

[0125] Step 1: Identify the energy-consuming equipment in the building, collect various parameter data inside the building in real time, and divide the various parameter data into relevant characteristic parameters of the energy-consuming equipment inside the building, various parameter signals of the internal environment of the building, and comfort data of users inside the building, and store the collected data.

[0126] Step 2: Obtain key characteristic parameters and historical data of key characteristic parameters in the characteristic parameters, train the historical data of key characteristic parameters to obtain a pre-trained model, extract the characteristic parameter template, fine-tune the pre-trained model according to the characteristic parameter template, obtain an energy consumption analysis model, and input the various parameter data collected in real time inside the building into the energy consumption analysis model to obtain energy consumption analysis results.

[0127] Step 3: Determine the operating power adjustment coefficient of the energy-consuming equipment in the building based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building. Determine the adjustment level of the energy-consuming equipment based on the operating power adjustment coefficient of the energy-consuming equipment in the building, and adjust the energy-consuming equipment accordingly. After the adjustment is completed, judge whether the adjustment result of the energy-consuming equipment in the building is optimal based on the comfort of the users in the building.

[0128] Step 4: If the optimum is reached, stop adjusting the energy-consuming equipment. If the optimum is not reached, continue to adjust the operating power of the energy-consuming equipment until the user's comfort data is moderate, thereby achieving precise adjustment of the energy consumption of the energy-consuming equipment inside the building and reducing energy consumption.

[0129] In summary, the real-time monitoring system and intelligent adjustment method of building energy consumption based on the Internet of Things of the present invention analyzes the energy consumption of energy-consuming equipment inside the building by establishing an energy consumption analysis model, and combines the relevant characteristic parameters of the energy-consuming equipment inside the building, the various parameter signals of the internal environment of the building and the comfort data of the users inside the building. It can adjust the energy consumption of the energy-consuming equipment inside the building according to the comfort of the users inside the building, and can ensure the comfort of the users while adjusting the energy consumption of the building, thereby improving the accuracy of the energy consumption analysis of the energy-consuming equipment inside the building. By judging the comfort data of the users inside the building and combining the energy consumption analysis results of the energy-consuming equipment inside the building to generate the operating power adjustment level of the energy-consuming equipment inside the building, it can accurately control the operation of the energy-consuming equipment. When the energy consumption inside the building is high, the operating power of the energy-consuming equipment inside the building is adjusted to reduce energy consumption. At the same time, the operating power of the energy-consuming equipment inside the building is adjusted according to the comfort of the users, thereby improving the energy saving rate of the building and making the human body more comfortable.

[0130] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0131] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Real-time monitoring system of building energy consumption based on the Internet of Things, Features: include: The real-time monitoring module is used to identify energy-consuming equipment in the building and collect various parameter data inside the building in real time. The various parameter data inside the building include parameter signals of the internal environment of the building, relevant characteristic parameters of each energy-consuming equipment and comfort data of users inside the building, and the collected data are classified and stored; The real-time monitoring module comprises: Data collection module, used to identify energy-consuming equipment in the building and collect various parameter data inside the building; The data classification module is used to classify the various parameter data collected from the internal environment of the building and store them separately according to the classification results; The data acquisition module comprises: The first data acquisition module is used to collect parameter data of the internal environment of the building, and the parameter data of the environment includes ambient temperature, light intensity and ambient humidity; The second data collection module is used to identify the energy-consuming equipment in the building and collect relevant characteristic parameters of each energy-consuming equipment, wherein the relevant characteristic parameters include the heat dissipation temperature of the energy-consuming equipment and the operating power of the energy-consuming equipment; The third data collection module is used to collect the comfort data of users inside the building; The data acquisition module further includes: A first real-time monitoring module, used for real-time monitoring of data transmission delay data of the first data acquisition module and the second data acquisition module; The first delay coefficient acquisition module is used to acquire a first delay coefficient using the data transmission delay data of the first data acquisition module and the second data acquisition module; wherein the first delay coefficient is acquired by the following formula: Among them, Y 01 represents the first delay coefficient; y 01 Represents the first coefficient parameter; y 02 represents the second coefficient parameter; and the first coefficient parameter and the second coefficient parameter are obtained by the following formula: Among them, y 01 represents the first coefficient parameter; n represents the number of delayed data transmissions of the first data acquisition module; m represents the number of undelayed data transmissions of the first data acquisition module; T i represents the actual data transmission duration corresponding to the i-th data delay of the first data acquisition module; T ci represents the theoretical data transmission duration corresponding to the i-th data delay of the first data acquisition module; T yi T represents the actual data transmission duration corresponding to the i-th data without delay of the first data acquisition module; cyi Indicates the theoretical data transmission duration corresponding to the i-th data transmission without delay of the first data acquisition module; Among them, y 02 represents the second coefficient parameter; p represents the number of delayed data transmissions of the second data acquisition module; q represents the number of undelayed data transmissions of the second data acquisition module; T j represents the actual data transmission duration corresponding to the jth data delay of the second data acquisition module; T cj T represents the theoretical data transmission duration corresponding to the jth data delay of the second data acquisition module; yj T represents the actual data transmission duration corresponding to the jth data without delay of the second data acquisition module; cyj Indicates the theoretical data transmission duration corresponding to the j-th data without delay of the second data acquisition module; A second real-time monitoring module, used for real-time monitoring of the data transmission delay data of the second data acquisition module and the third data acquisition module; The second delay coefficient acquisition module is used to acquire the second delay coefficient by using the data transmission delay data of the second data acquisition module and the third data acquisition module; wherein the second delay coefficient is acquired by the following formula: Among them, Y 02 represents the second delay coefficient; y 02 Represents the second coefficient parameter; y 03 represents a third coefficient parameter; and the third coefficient parameter is obtained by the following formula: Among them, y 03 represents the third coefficient parameter; r represents the number of delayed data transmissions of the third data acquisition module; e represents the number of undelayed data transmissions of the third data acquisition module; T t represents the actual data transmission duration corresponding to the tth data delay of the third data acquisition module; T ct T represents the theoretical data transmission duration corresponding to the tth data delay of the third data acquisition module; yt T represents the actual data transmission duration corresponding to the tth data without delay of the third data acquisition module; cyt Indicates the theoretical data transmission duration corresponding to the tth data without delay of the third data acquisition module; A comparison module, configured to compare the first delay coefficient with the second delay coefficient to obtain a coefficient difference; a data transmission rate adjustment module, configured to compare the coefficient difference with a preset difference threshold, and to adjust the data transmission rates of the first data acquisition module, the second data acquisition module, and the third data acquisition module when the coefficient difference exceeds the preset difference threshold; The data transmission rate adjustment module comprises: A comparison execution module, used for comparing the coefficient difference with a preset difference threshold to obtain a comparison result; A data transmission rate extraction module, configured to retrieve the current data transmission rates of the first data acquisition module, the second data acquisition module and the third data acquisition module when the comparison result indicates that the coefficient difference exceeds a preset difference threshold; A transmission rate adjustment execution module, used to adjust the data transmission rate of the first data acquisition module, the second data acquisition module and the third data acquisition module respectively by using the comparison result between the coefficient difference and the preset difference threshold value in combination with the first delay coefficient and the second delay coefficient; The adjusted data transmission rate of the first data acquisition module is obtained by the following formula: Among them, V 01t represents the adjusted data transmission rate corresponding to the first data acquisition module; V 01 represents the data transmission rate before adjustment corresponding to the first data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient; In addition, the adjusted data transmission rate of the second data acquisition module is obtained by the following formula: Among them, V 02t represents the adjusted data transmission rate corresponding to the second data acquisition module; V 02 represents the data transmission rate before adjustment corresponding to the second data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 01 represents the first delay coefficient; In addition, the adjusted data transmission rate of the third data acquisition module is obtained by the following formula: Among them, V 03t represents the adjusted data transmission rate corresponding to the third data acquisition module; V 03 represents the data transmission rate before adjustment corresponding to the third data acquisition module; Y c Represents the coefficient difference; Y y Indicates the preset difference threshold; Y 02 represents the second delay coefficient; The energy consumption analysis module is used to establish an energy consumption analysis model and input the relevant characteristic parameters of the energy consumption equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building collected in real time into the energy consumption analysis model to obtain the energy consumption analysis results; The energy consumption regulation module is used to determine the operating power regulation coefficient of the energy consumption equipment in the building according to the energy consumption analysis results of the energy consumption equipment in the building and the comfort data of the current users in the building, and to judge whether the regulation results of the energy consumption equipment in the building are optimal according to the comfort data of the users in the building.

2. According to the real-time monitoring system for building energy consumption based on the Internet of Things in claim 1, Features: The analysis process of the energy consumption analysis module specifically includes: Receiving relevant characteristic parameters of energy-consuming equipment inside the building, and determining key characteristic parameters among the characteristic parameters; Acquire historical data of key feature parameters, and train the historical data of key feature parameters to obtain a pre-trained model; Receive an energy consumption analysis instruction, extract a characteristic parameter template in the energy consumption analysis instruction, and fine-tune the pre-trained model according to the characteristic parameter template to obtain an energy consumption analysis model; The relevant characteristic parameters of the energy-consuming equipment inside the building, the various parameter signals of the internal environment of the building, and the comfort data of the users inside the building collected in real time are input into the energy consumption analysis model to obtain the energy consumption analysis results.

3. According to the real-time monitoring system for building energy consumption based on the Internet of Things in claim 1, Features: The adjustment process of the energy consumption adjustment module specifically includes: Determine the comfort data of users inside the building based on the temperature inside the building, and preset the operating power adjustment coefficient of energy-consuming equipment; Determine the operating power adjustment coefficient of the energy-consuming equipment in the building based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building; Compare the operating power adjustment coefficient of the energy-consuming equipment in the building with the preset operating power adjustment coefficient of the energy-consuming equipment to obtain the operating power adjustment coefficient deviation of the energy-consuming equipment; The operating power regulation coefficient deviation of the energy-consuming equipment is calculated by differentiation to obtain the operating power regulation coefficient deviation change rate of the energy-consuming equipment; The deviation change rate of the operating power adjustment coefficient of the energy-consuming equipment is input into the fuzzy controller, the adjustment level of the energy-consuming equipment is output, and the energy-consuming equipment is adjusted accordingly; After the adjustment is completed, the comfort data of the people inside the building is collected to determine whether the comfort data of the people inside the building is moderate. If not, the energy-consuming equipment will continue to be adjusted until the user's comfort data is moderate.

4. According to the real-time monitoring system for building energy consumption based on the Internet of Things as claimed in claim 3, Features: The energy consumption adjustment module comprises: The adjustment module is used to determine the operating power adjustment coefficient of the energy-consuming equipment inside the building according to the energy consumption analysis result, and adjust the operating power of the energy-consuming equipment inside the building according to the adjustment coefficient; The judgment module is used to judge whether the comfort data of the people inside the building is moderate based on the real-time collected comfort data of the people inside the building.

5. According to claim 4, the intelligent adjustment method of the real-time monitoring system of building energy consumption based on the Internet of Things, Features: The following steps are involved: Step 1: Identify energy-consuming equipment in the building, collect multiple parameter data inside the building in real time, and classify and store the collected data; Step 2: Establish an energy consumption analysis model, and input the real-time collected data of various parameters inside the building into the energy consumption analysis model to obtain energy consumption analysis results; Step 3: Determine the operating power adjustment coefficient of the energy-consuming equipment in the building based on the energy consumption analysis results of the energy-consuming equipment in the building and the comfort data of the current users in the building, and judge whether the adjustment results of the energy-consuming equipment in the building are optimal based on the comfort data of the users in the building; Step 4: If the optimum is reached, stop adjusting the energy-consuming equipment; if the optimum is not reached, continue adjusting the operating power of the energy-consuming equipment until the user's comfort data is moderate.

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