Intelligent dynamic energy-saving regulation and control method, system and equipment for central air conditioner and medium of intelligent dynamic energy-saving regulation and control method

Abstract

The invention provides an intelligent dynamic energy-saving regulation and control method, system and equipment for a central air conditioner and a medium thereof, and the method comprises the following steps: data collection: carrying out initialization setting on a central air conditioning system, including setting basic operation parameters, calibrating a sensor, and collecting indoor temperature and humidity, outdoor meteorological conditions, personnel activity conditions and building energy consumption data; reliable data support is provided for follow-up prediction and regulation through data cleaning and feature extraction, abnormal value and missing value removal and key feature extraction, energy consumption trend and load change in a period of time in the future are predicted by building a prediction model, and then the energy consumption trend and the load change in the period of time in the future are predicted based on the prediction result, the current environment condition and the personnel activity condition. Compared with the prior art, the system can collect and analyze various types of data in real time, including indoor temperature and humidity, outdoor meteorological conditions, personnel activity conditions and the like, so that the operation state and energy consumption condition of the system can be known more comprehensively.

Description

Technical Field

[0001] The present invention relates to a control method, system, equipment and medium thereof, specifically to a central air-conditioning intelligent dynamic energy-saving control method, system, equipment and medium thereof, belonging to the technical field of air-conditioning control methods. Background Art

[0002] Problems such as the load and system partition of central air conditioners cause uneven cooling and heating, hydraulic imbalance in the chilled water system, and excessive installed capacity of chillers, which are the reasons for a large waste of operating energy. There are many on-site equipment in the central air conditioning system. Reasonable configuration, reasonable installation and reasonable operation control are the guarantee for the healthy operation of the unit. At present, the domestic design of commercial air conditioners, central air conditioners, etc. is mainly based on national standards, industry standards and relevant experience data. The designed units often have a large margin, which is not conducive to energy saving. At the same time, with the rapid development of computer technology and Internet technology, the arrival of the big data era has become unstoppable, and the explosive growth of data volume has brought new opportunities and challenges to all walks of life.

[0003] Through big data analysis, we can design units and other on-site equipment (water pumps, fans, cooling towers, etc.) according to different building locations, different indoor and outdoor environments, and different functions of the building. Computers can simulate the operating conditions of the designed air-conditioning units, predict system energy consumption and achievable indoor environmental quality, which will greatly save time and cost in the development of new products.

[0004] The known Chinese authorized invention (Announcement No.: CN114383299B) discloses a central air-conditioning system operation strategy optimization method based on big data and dynamic simulation, which introduces an association rule model on the basis of a neural network model to construct a hybrid genetic association algorithm, and adjusts the operation strategy through big data analysis and dynamic simulation, thereby achieving good energy-saving effects and achieving optimal control of air-conditioning system energy saving;

[0005] It achieves optimized control of the air conditioning system by adjusting the operation strategy through simulation. However, its adaptability is still limited when facing various complex usage environments and changing conditions. This limitation often results in the control effect not being able to reach the optimal state, affecting the improvement of energy-saving efficiency.

[0006] Specifically, the operating environment of central air conditioning includes different types of buildings, different indoor layouts, diverse usage requirements and changeable external environmental conditions. The interaction of these complex factors requires frequent adjustments to the operating status of the central air conditioner to adapt to the ever-changing needs. Therefore, when facing a complex environment, the control effect is often unsatisfactory. For this reason, an intelligent dynamic energy-saving control method, system, equipment and medium for central air conditioning are proposed. Summary of the invention

[0007] In view of this, the present invention provides a central air-conditioning intelligent dynamic energy-saving control method, system, equipment and medium thereof to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial choice.

[0008] The technical solution of the embodiment of the present invention is implemented as follows: a central air-conditioning intelligent dynamic energy-saving control method comprises the following steps:

[0009] Data collection: Initialize the central air conditioning system, including setting basic operating parameters, calibrating sensors, and collecting indoor temperature and humidity, outdoor meteorological conditions, personnel activities, and building energy consumption data;

[0010] Data preprocessing and energy consumption prediction: preprocess the collected data, extract key features from the cleaned data, and predict future energy consumption trends and load changes through prediction algorithms;

[0011] Intelligent control strategy formulation: Based on the results of the prediction model and the current environmental conditions and personnel activities, determine whether the operating status of the central air conditioner needs to be adjusted, and dynamically generate intelligent control strategies based on the judgment results;

[0012] Strategy execution: The central air conditioning system executes the control strategy, including adjusting the air supply volume and cooling / heat output;

[0013] Effect evaluation: Compare energy consumption data before and after the implementation of control strategies to evaluate energy-saving efficiency;

[0014] Strategy optimization: Identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy.

[0015] Further preferably, the outdoor meteorological conditions include temperature, humidity, wind speed and solar radiation, the personnel activity data include the number of personnel, activity intensity, and location distribution, and the building energy consumption data include electrical energy and water energy;

[0016] The building energy consumption data is collected in real time through smart electricity meters and water meters, and is used to evaluate the energy efficiency of the central air-conditioning system and formulate energy-saving strategies;

[0017] The personnel activity data is obtained through infrared human body sensors and cameras;

[0018] The sensor comprises:

[0019] Temperature and humidity sensor: used to collect indoor and outdoor temperature and humidity data in real time;

[0020] Meteorological sensors: including temperature sensors, humidity sensors, wind speed sensors, solar radiation sensors, etc., used to collect outdoor meteorological conditions;

[0021] Infrared human body sensor: used to detect the number and location distribution of people in a building;

[0022] Camera: used for people counting and behavior recognition;

[0023] Smart electricity and water meters: used to collect real-time electricity and water consumption.

[0024] Further preferably, the prediction algorithm comprises the following steps:

[0025] Data cleaning: remove outliers in the data, including sudden temperature jumps and unreasonable energy consumption data, and use mean substitution method to handle missing values;

[0026] Feature extraction: Extract key features from raw data, including indoor and outdoor temperature difference, humidity difference, occupancy density, and building energy consumption patterns;

[0027] Model construction: Build a prediction model based on the BP neural network algorithm, set algorithm parameters, including learning rate, number of iterations, and regularization parameters, and input feature data into the model for training;

[0028] Real-time data input: The real-time monitored data is input into the trained prediction model. The prediction model outputs the future energy consumption trend and load change prediction results based on the input data. The prediction results are presented in numerical form.

[0029] Further preferably, the step of formulating the intelligent control strategy includes:

[0030] Data collection: Collect current environmental condition data, personnel activity data, and prediction model result data, and make condition judgments based on the collected data, including whether the current environmental conditions meet the preset comfort standards, whether the prediction model predicts significant changes in future temperature or humidity, and whether personnel activity changes;

[0031] Strategy formulation: According to the results of condition judgment, a control strategy is dynamically generated, which includes adjusting the air supply volume, cooling / heat output, and setting the temperature. The formulation of the control strategy is based on the energy-saving goals of the central air-conditioning system and the comfort requirements of the indoor environment;

[0032] Strategy execution and monitoring: Apply the generated control strategy to the central air-conditioning system, and monitor the system's operating status and changes in the indoor environment in real time.

[0033] Further preferably, when the strategy is executed, the following scheme is executed:

[0034] Energy consumption monitoring: Continuously monitor the energy consumption of the system, including the consumption of electricity, water and other resources;

[0035] Anomaly detection: using monitoring data to detect system anomalies, including sensor failures and equipment failures;

[0036] Early warning mechanism: The early warning mechanism is triggered when an abnormal situation is detected.

[0037] Further preferably, during the effect evaluation, the energy saving efficiency evaluation includes the following steps:

[0038] Energy consumption data collection: Collect energy consumption data of the central air-conditioning system in various time periods;

[0039] Energy consumption comparison analysis: Compare the energy consumption data before and after the central air conditioning system is regulated to evaluate the energy-saving effect brought about by the regulation method;

[0040] Energy consumption trend analysis: By drawing an energy consumption trend chart, we can observe the changes in energy consumption of the central air-conditioning system in different time periods.

[0041] Further preferably, when optimizing the strategy, problems existing in the control strategy are identified, including excessive energy consumption, poor comfort or poor system stability; and key parameters in the control strategy are adjusted, including temperature, humidity, thresholds of air supply volume and regulation rate.

[0042] An intelligent dynamic energy-saving control system for central air conditioners, comprising a data acquisition module, an energy consumption prediction module, a control strategy generation module, a strategy execution module, an optimization module and an evaluation module;

[0043] The signal ends of the data acquisition module, the energy consumption prediction module, the control strategy generation module and the strategy execution module are connected in sequence, and the signal ends of the evaluation module, the optimization module and the control strategy generation module are connected in sequence;

[0044] The data acquisition module is used to collect indoor temperature and humidity, outdoor meteorological conditions, personnel activities and building energy consumption data. The data acquisition module includes a temperature and humidity sensor, a meteorological sensor, an infrared human body sensor, a camera, a smart meter and a water meter;

[0045] The energy consumption prediction module is used to predict future energy consumption trends and load changes through a prediction algorithm;

[0046] The control strategy generation module is used to determine whether the operating state of the central air conditioner needs to be adjusted according to the prediction results of the pre-energy consumption prediction module and the current environmental conditions and personnel activities, and dynamically generate an intelligent control strategy according to the judgment results;

[0047] The strategy execution module is used to execute the control strategy, including adjusting the air supply volume and cooling / heat output;

[0048] The evaluation module is used to compare the energy consumption data before and after the implementation of the control strategy to evaluate the energy saving efficiency;

[0049] The optimization module is used to identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy.

[0050] A central air-conditioning intelligent dynamic energy-saving control device, the device comprising:

[0051] at least one processor; and,

[0052] a memory communicatively connected to the at least one processor; wherein,

[0053] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a central air-conditioning intelligent dynamic energy-saving control method.

[0054] A computer storage medium stores computer instructions for enabling a computer to execute a central air-conditioning intelligent dynamic energy-saving control method.

[0055] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0056] The present invention removes outliers and missing values ​​and extracts key features through data cleaning and feature extraction, thereby providing reliable data support for subsequent prediction and regulation. By establishing a prediction model, the energy consumption trend and load change in the future are predicted, and then based on the prediction results and the current environmental conditions and personnel activities, the control strategy can be dynamically generated and optimized. Compared with the prior art, the present invention can collect and analyze various types of data in real time, including indoor temperature and humidity, outdoor meteorological conditions, personnel activities, etc., so as to have a more comprehensive understanding of the operating status and energy consumption of the system. Based on real-time data and prediction models, the present invention can dynamically generate and optimize control strategies to adapt to changing environmental conditions and usage requirements, thereby realizing precise control of the central air-conditioning system, ensuring that energy consumption is minimized while meeting comfort requirements.

[0057] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0059] Figure 1 This is a flow chart of the steps of a central air conditioner intelligent dynamic energy-saving control method of the present invention;

[0060] Figure 2 is a flow chart of the steps of the prediction algorithm of the present invention;

[0061] Figure 3 A flow chart of the steps for formulating the intelligent control strategy of the present invention;

[0062] Figure 4 This is a structural diagram of a central air-conditioning intelligent dynamic energy-saving control system of the present invention;

[0063] Figure 5 This is a structural diagram of a central air-conditioning intelligent dynamic energy-saving control device of the present invention. DETAILED DESCRIPTION

[0064] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0065] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] like Figure 1-Figure 4 As shown, the embodiment of the present invention provides a central air conditioner intelligent dynamic energy-saving control method, comprising the following steps:

[0067] Data collection: Initialize the central air conditioning system, including setting basic operating parameters, calibrating sensors, and collecting indoor temperature and humidity, outdoor meteorological conditions, personnel activities, and building energy consumption data;

[0068] Outdoor meteorological conditions include temperature, humidity, wind speed and solar radiation; personnel activity data include the number of personnel, activity intensity and location distribution; building energy consumption data include electrical energy and water energy;

[0069] Building energy consumption data is collected in real time through smart electricity and water meters to evaluate the energy efficiency of central air-conditioning systems and develop energy-saving strategies;

[0070] Building energy consumption data mainly include:

[0071] Power consumption: The power consumption of the central air conditioning system can be collected in real time through smart meters;

[0072] Water energy consumption: When the central air conditioning system involves cooling towers, humidifiers and other equipment, water energy consumption is also an important monitoring indicator;

[0073] Other resource consumption: Depending on the specific configuration and usage of the central air conditioning system, other resources consumption may also be involved, such as gas and steam;

[0074] The data of personnel activities are obtained through infrared human body sensors and cameras;

[0075] The personnel activities mainly include the following aspects:

[0076] Number of people: The real-time number of people in the building, which can be obtained through infrared human body sensors or cameras combined with people counting algorithms;

[0077] For office buildings, since people are concentrated in the office area and the working hours are relatively fixed, the air supply volume and temperature settings can be adjusted in a targeted manner according to the density of people in the office area and the working hours. During peak office hours, increase the air supply volume and appropriately reduce the temperature setting; during non-office hours, reduce the air supply volume and increase the temperature setting. For shopping malls, considering the large mobility of people and the different functions of different areas (such as dining areas, shopping areas, etc.), a zoning control strategy can be adopted to independently adjust parameters such as air supply volume, cooling / heat output, etc. according to the real-time personnel activities and environmental conditions in each area.

[0078] Activity intensity: The activity level of people, such as sitting still, walking, exercising, etc., which can be evaluated through behavior recognition algorithms, and then the central air conditioning system's air supply volume, temperature and other parameters can be adjusted to meet the comfort requirements under different activity intensities;

[0079] Location distribution: The location distribution of people in the building, which helps the system to more accurately adjust the air supply or temperature in local areas, improving energy efficiency and comfort;

[0080] Sensors include:

[0081] Temperature and humidity sensor: used to collect indoor and outdoor temperature and humidity data in real time to reflect the comfort level of the indoor environment;

[0082] Meteorological sensors: including temperature sensors, humidity sensors, wind speed sensors, solar radiation sensors, etc., used to collect outdoor meteorological conditions to evaluate the impact of the outdoor environment on the energy consumption of the central air-conditioning system;

[0083] Infrared human body sensor: used to detect the number and location distribution of people in a building. The activity of people is of great significance for evaluating indoor load and adjusting air supply volume.

[0084] Camera: used for people counting and behavior recognition;

[0085] Smart electricity and water meters: used to collect real-time electricity and water consumption.

[0086] Data preprocessing and energy consumption prediction: preprocess the collected data, extract key features from the cleaned data, and predict future energy consumption trends and load changes through prediction algorithms;

[0087] The prediction algorithm includes the following steps:

[0088] Data cleaning: remove outliers in the data, including sudden temperature jumps and unreasonable energy consumption data, and use mean substitution method to handle missing values;

[0089] Feature extraction: Extract key features from raw data, including indoor and outdoor temperature difference, humidity difference, occupancy density, and building energy consumption patterns;

[0090] Model construction: Build a prediction model based on the neural network algorithm, set the algorithm parameters, including learning rate, number of iterations, and regularization parameters, input feature data into the prediction, and conduct training;

[0091] Real-time data input: The real-time monitored data is input into the trained prediction model. The prediction model outputs the future energy consumption trend and load change prediction results based on the input data. The prediction results are presented in numerical form.

[0092] Intelligent control strategy formulation: Based on the results of the prediction model and the current environmental conditions and personnel activities, determine whether the operating status of the central air conditioner needs to be adjusted, and dynamically generate intelligent control strategies based on the judgment results;

[0093] The steps to formulate an intelligent control strategy include:

[0094] Data collection: Collect current environmental condition data, personnel activity data, and prediction model result data, and make condition judgments based on the collected data, including whether the current environmental conditions meet the preset comfort standards, whether the prediction model predicts significant changes in future temperature or humidity, and whether personnel activity changes;

[0095] Strategy formulation: Dynamically generate control strategies based on the results of condition judgment. The control strategies include adjusting the air supply volume, cooling / heat output, and setting the temperature. The formulation of the control strategies is based on the energy-saving goals of the central air-conditioning system and the comfort requirements of the indoor environment.

[0096] Strategy execution and monitoring: Apply the generated control strategy to the central air-conditioning system and monitor the system's operating status and changes in the indoor environment in real time;

[0097] By dynamically adjusting parameters such as air supply volume, cooling / heat output and set temperature, the system can minimize energy consumption while ensuring indoor environmental comfort;

[0098] The content of the regulatory strategy includes:

[0099] Air supply volume adjustment: Dynamically adjust the air supply volume according to indoor population density, activity intensity, temperature, humidity and other environmental conditions. For example, in areas with dense population and high activity intensity, increase the air supply volume to maintain indoor air circulation and comfort; in areas with sparse population or low activity intensity, reduce the air supply volume to save energy.

[0100] Cooling / heat output adjustment: According to the indoor and outdoor temperature and humidity differences and the prediction results of the prediction model for future temperature and humidity, the cooling or heat output of the central air-conditioning system is adjusted. For example, during the hot summer period, the cooling capacity is increased to reduce the indoor temperature; and during the cold winter period, the heat output is increased to maintain a suitable indoor temperature.

[0101] Set temperature adjustment: Dynamically adjust the set temperature of the central air conditioner based on the comfort needs of indoor occupants and the prediction results of the prediction model on future temperature changes. For example, when it is predicted that the temperature will rise in the future, lower the set temperature in advance to prevent indoor overheating; when it is predicted that the temperature will drop in the future, increase the set temperature in advance to keep the room warm.

[0102] Strategy execution: The central air conditioning system executes the control strategy, including adjusting the air supply volume and cooling / heat output;

[0103] When the policy is executed, the following scenarios are executed:

[0104] Energy consumption monitoring: Continuously monitor the energy consumption of the system, including the consumption of electricity, water and other resources;

[0105] Anomaly detection: using monitoring data to detect system anomalies, including sensor failures and equipment failures;

[0106] Early warning mechanism: The early warning mechanism is triggered when an abnormal situation is detected.

[0107] Effect evaluation: Compare energy consumption data before and after the implementation of control strategies to evaluate energy-saving efficiency;

[0108] When evaluating the effect, the energy saving efficiency evaluation includes the following steps:

[0109] Energy consumption data collection: Collect energy consumption data of the central air-conditioning system in various time periods;

[0110] Energy consumption comparison analysis: Compare the energy consumption data before and after the central air conditioning system is regulated to evaluate the energy-saving effect brought about by the regulation method;

[0111] Energy consumption trend analysis: By drawing an energy consumption trend chart, observe the energy consumption changes of the central air-conditioning system in different time periods;

[0112] At the same time, it also includes comfort satisfaction evaluation:

[0113] User satisfaction survey: Design a reasonable user satisfaction survey questionnaire that covers indoor temperature, humidity, air quality, noise level and other aspects, distribute the questionnaire to users regularly, and collect feedback.

[0114] Comfort index monitoring: Use sensors and other equipment to monitor the comfort index of the indoor environment in real time, such as temperature, humidity, and PM2.5 concentration, and compare the monitoring results with the preset comfort standards to evaluate the comfort performance of the system;

[0115] Satisfaction data analysis: Conduct statistical analysis on the collected user satisfaction data to understand users' overall evaluation of system performance. Based on the analysis results, identify key issues of concern to users and directions for improvement.

[0116] Strategy optimization: Identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy. When optimizing the strategy, identify problems with the control strategy, including excessive energy consumption, poor comfort, or poor system stability; adjust key parameters in the control strategy, including temperature, humidity, air supply thresholds, and regulation rates.

[0117] When encountering extreme weather (such as extreme cold, extreme heat, etc.), the system automatically switches to emergency mode to prioritize the basic comfort needs of indoor occupants. For example, in extremely cold weather, the heating power is increased to ensure that the indoor temperature is not lower than the set minimum temperature threshold; in hot weather, the cooling output is increased to control the indoor temperature within a safe range. When a device failure is detected, the system immediately starts the backup device and adjusts the control strategy to maintain the relative stability of the indoor environment through other normally operating devices. At the same time, a fault alarm is issued to notify maintenance personnel to handle it in a timely manner.

[0118] An intelligent dynamic energy-saving control system for central air conditioners, comprising a data acquisition module, an energy consumption prediction module, a control strategy generation module, a strategy execution module, an optimization module and an evaluation module;

[0119] The signal ends of the data acquisition module, the energy consumption prediction module, the control strategy generation module and the strategy execution module are connected in sequence, and the signal ends of the evaluation module, the optimization module and the control strategy generation module are connected in sequence;

[0120] The data acquisition module is used to collect indoor temperature and humidity, outdoor meteorological conditions, personnel activities and building energy consumption data. The data acquisition module includes temperature and humidity sensors, meteorological sensors, infrared human body sensors, cameras, smart electricity meters and water meters;

[0121] The energy consumption prediction module is used to predict future energy consumption trends and load changes through prediction algorithms;

[0122] The control strategy generation module is used to determine whether the operation status of the central air conditioner needs to be adjusted according to the prediction results of the pre-energy consumption prediction module and the current environmental conditions and personnel activities, and dynamically generate intelligent control strategies based on the judgment results;

[0123] The strategy execution module is used to execute the control strategy, including adjusting the air supply volume and cooling / heat output;

[0124] The evaluation module is used to compare the energy consumption data before and after the implementation of the control strategy and evaluate the energy saving efficiency;

[0125] The optimization module is used to identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy.

[0126] This system can be linked with the intelligent lighting system. When it detects changes in the indoor activity area, it can not only adjust the air supply and temperature of the central air conditioner, but also notify the lighting system to adjust the lighting brightness of the corresponding area to achieve comprehensive utilization of energy. For example, when personnel move from the office area to the conference room, the central air conditioning system automatically adjusts the air supply and temperature of the conference room, and the lighting system automatically increases the lighting brightness of the conference room and reduces the lighting brightness of the office area. When linked with the security system, if the security system detects an abnormal situation in a certain area (such as an intrusion alarm), this system can temporarily shut down the central air conditioner in the area to avoid the spread of abnormal situations due to the flow of personnel, and send alarm information to management personnel.

[0127] According to an embodiment of the present disclosure, the central air-conditioning intelligent dynamic energy-saving control device includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0128] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the central air-conditioning intelligent dynamic energy-saving control device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the central air-conditioning intelligent dynamic energy-saving control device performs all or part of the steps of a signal line conduction sensitivity test method of each embodiment of the present disclosure.

[0129] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0130] like Figure 5 The present invention provides a schematic diagram of the structure of a central air-conditioning intelligent dynamic energy-saving control device according to an embodiment of the present invention. The present invention provides a schematic diagram of the structure of a central air-conditioning intelligent dynamic energy-saving control device suitable for implementing the embodiment of the present invention. Figure 5 The central air-conditioning intelligent dynamic energy-saving control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0131] like Figure 5 As shown, the central air-conditioning intelligent dynamic energy-saving control device may include a processor (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the central air-conditioning intelligent dynamic energy-saving control device are also stored. The processor, ROM, and RAM are connected to each other via a bus. The input / output (I / O) interface is also connected to the bus.

[0132] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as magnetic tapes and hard disks; and communication devices. The communication device can allow the central air-conditioning intelligent dynamic energy-saving control device to communicate with other devices (such as edge computing devices) wirelessly or wired to exchange data. Figure 5 The central air-conditioning intelligent dynamic energy-saving control device with various devices is shown, but it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0133] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of a signal line conduction sensitivity test method of an embodiment of the present disclosure are executed.

[0134] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0135] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of a signal line conduction sensitivity testing method of each embodiment of the present disclosure are executed.

[0136] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0137] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0138] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0139] In the present disclosure, 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 that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0140] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0141] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0142] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0143] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0144] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

[0145] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A central air-conditioning intelligent dynamic energy-saving control method, characterized in that: The following steps are involved: Data collection: Initialize the central air conditioning system, including setting basic operating parameters, calibrating sensors, and collecting indoor temperature and humidity, outdoor meteorological conditions, personnel activities, and building energy consumption data; Data preprocessing and energy consumption prediction: preprocess the collected data, extract key features from the cleaned data, and predict future energy consumption trends and load changes through prediction algorithms; Intelligent control strategy formulation: Based on the results of the prediction model and the current environmental conditions and personnel activities, determine whether the operating status of the central air conditioner needs to be adjusted, and dynamically generate intelligent control strategies based on the judgment results; Strategy execution: The central air conditioning system executes the control strategy, including adjusting the air supply volume and cooling / heat output; Effect evaluation: Compare energy consumption data before and after the implementation of control strategies to evaluate energy-saving efficiency; Strategy optimization: Identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy.

2. According to claim 1, a central air-conditioning intelligent dynamic energy-saving control method is characterized in that: The outdoor meteorological conditions include temperature, humidity, wind speed and solar radiation; the personnel activity data include the number of personnel, activity intensity and location distribution; and the building energy consumption data include electrical energy and water energy; The building energy consumption data is collected in real time through smart electricity meters and water meters, and is used to evaluate the energy efficiency of the central air-conditioning system and formulate energy-saving strategies; The personnel activity data is obtained through infrared human body sensors and cameras; The sensor comprises: Temperature and humidity sensor: used to collect indoor and outdoor temperature and humidity data in real time; Meteorological sensors: including temperature sensors, humidity sensors, wind speed sensors, solar radiation sensors, etc., used to collect outdoor meteorological conditions; Infrared human body sensor: used to detect the number and location distribution of people in a building; Camera: used for people counting and behavior recognition; Smart electricity and water meters: used to collect real-time electricity and water consumption.

3. According to claim 1, a central air-conditioning intelligent dynamic energy-saving control method is characterized in that: The prediction algorithm comprises the following steps: Data cleaning: remove outliers in the data, including sudden temperature jumps and unreasonable energy consumption data, and use mean substitution method to handle missing values; Feature extraction: Extract key features from raw data, including indoor and outdoor temperature difference, humidity difference, occupancy density, and building energy consumption patterns; Model construction: Build a prediction model based on the neural network algorithm, set the algorithm parameters, including learning rate, number of iterations, and regularization parameters, input feature data into the prediction, and conduct training; Real-time data input: The real-time monitored data is input into the trained prediction model. The prediction model outputs the future energy consumption trend and load change prediction results based on the input data. The prediction results are presented in numerical form.

4. The central air conditioner intelligent dynamic energy-saving control method according to claim 1 is characterized in that: The steps of formulating the intelligent control strategy include: Data collection: Collect current environmental condition data, personnel activity data, and prediction model result data, and make condition judgments based on the collected data, including whether the current environmental conditions meet the preset comfort standards, whether the prediction model predicts significant changes in future temperature or humidity, and whether personnel activity changes; Strategy formulation: According to the results of condition judgment, a control strategy is dynamically generated, which includes adjusting the air supply volume, cooling / heat output, and setting the temperature. The formulation of the control strategy is based on the energy-saving goals of the central air-conditioning system and the comfort requirements of the indoor environment; Strategy execution and monitoring: Apply the generated control strategy to the central air-conditioning system, and monitor the system's operating status and changes in the indoor environment in real time.

5. The central air conditioner intelligent dynamic energy-saving control method according to claim 1 is characterized in that: When the policy is executed, the following scenarios are executed: Energy consumption monitoring: Continuously monitor the energy consumption of the system, including the consumption of electricity, water and other resources; Anomaly detection: using monitoring data to detect system anomalies, including sensor failures and equipment failures; Early warning mechanism: The early warning mechanism is triggered when an abnormal situation is detected.

6. The central air conditioner intelligent dynamic energy-saving control method according to claim 1, characterized in that: When evaluating the effect, the energy saving efficiency evaluation includes the following steps: Energy consumption data collection: Collect energy consumption data of the central air-conditioning system in various time periods; Energy consumption comparison analysis: Compare the energy consumption data before and after the central air conditioning system is regulated to evaluate the energy-saving effect brought about by the regulation method; Energy consumption trend analysis: By drawing an energy consumption trend chart, we can observe the changes in energy consumption of the central air-conditioning system in different time periods.

7. The central air conditioner intelligent dynamic energy-saving control method according to claim 1 is characterized in that: When optimizing the strategy, identify problems with the control strategy, including excessive energy consumption, poor comfort, or poor system stability; adjust key parameters in the control strategy, including temperature, humidity, air supply thresholds, and regulation rates.

8. A central air conditioner intelligent dynamic energy-saving control system, applied to a central air conditioner intelligent dynamic energy-saving control method as claimed in any one of claims 1 to 7, characterized in that: It includes data acquisition module, energy consumption prediction module, control strategy generation module, strategy execution module, optimization module and evaluation module; The signal ends of the data acquisition module, the energy consumption prediction module, the control strategy generation module and the strategy execution module are connected in sequence, and the signal ends of the evaluation module, the optimization module and the control strategy generation module are connected in sequence; The data acquisition module is used to collect indoor temperature and humidity, outdoor meteorological conditions, personnel activities and building energy consumption data. The data acquisition module includes a temperature and humidity sensor, a meteorological sensor, an infrared human body sensor, a camera, a smart meter and a water meter; The energy consumption prediction module is used to predict future energy consumption trends and load changes through a prediction algorithm; The control strategy generation module is used to determine whether the operating state of the central air conditioner needs to be adjusted according to the prediction results of the pre-energy consumption prediction module and the current environmental conditions and personnel activities, and dynamically generate an intelligent control strategy according to the judgment results; The strategy execution module is used to execute the control strategy, including adjusting the air supply volume and cooling / heat output; The evaluation module is used to compare the energy consumption data before and after the implementation of the control strategy to evaluate the energy saving efficiency; The optimization module is used to identify problems and deficiencies in the control strategy based on the evaluation results, formulate improvement plans for the identified problems, and optimize the control strategy.

9. A central air-conditioning intelligent dynamic energy-saving control device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the central air-conditioning intelligent dynamic energy-saving control method described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute a central air-conditioning intelligent dynamic energy-saving control method as described in any one of claims 1-7.

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