Load remote control method and system based on remote control and energy-saving optimization

Abstract

The invention relates to the technical field of load remote control, and discloses a load remote control method and system based on remote control and energy-saving optimization. The method comprises the steps of obtaining a first control instruction of a user; according to the first control instruction, load equipment is controlled so that the load equipment can conduct air conditioning work; in the working process of the load equipment, air information is obtained, and the air information is preprocessed; on the basis of the preprocessed air information, air abnormal value detection is carried out, and abnormal data are obtained; performing data extraction according to the abnormal data to obtain deviation data; matching the deviation data with a preset second control instruction database to obtain a second control instruction; and according to the second control instruction, the load equipment is controlled so that the load equipment can conduct air conditioning work. The method can continuously detect the indoor air and keep the indoor air comfortable according to the detection value, and the effect of improving the energy use efficiency is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of load control, and in particular to a load remote control method and system based on remote control and energy-saving optimization. Background Art

[0002] Load remote control can include real-time monitoring and control of the energy consumption of household electrical appliances. Load remote control can also include remotely operated switching devices to control loads connected to the power grid. It can be a simple switch operation or a more complex control strategy such as timing control, scene control, etc.

[0003] In the existing load (air conditioning) remote control technology, users generally use infrared remote controls or mechanical buttons to control the switch, temperature, and mode of the load. After being turned on, the load will continue to work according to the user's settings, and the energy consumption is not properly controlled.

[0004] In summary, for remote control of loads, existing load remote control technologies, such as infrared remote control, cannot meet the needs of users for remote load control, and existing control technologies do not effectively manage the energy consumption of loads. Summary of the invention

[0005] The present invention provides a load remote control method and system based on remote control and energy-saving optimization, which can remotely control the load through the load remote control device, and can continuously detect the indoor air and keep the indoor air comfortable according to the detection value, so as to achieve the effect of improving energy utilization efficiency.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a load remote control method based on remote control and energy-saving optimization, comprising:

[0007] Obtaining a first control instruction from a user;

[0008] According to the first control instruction, the load device is controlled so that the load device performs air conditioning work;

[0009] During the operation of the load equipment, air information is obtained and preprocessed; wherein the air information includes: air temperature, air humidity, and air circulation rate;

[0010] Based on the pre-processed air information, air anomaly detection is performed to obtain abnormal data;

[0011] Extracting data based on the abnormal data to obtain deviation data;

[0012] Matching the deviation data with a preset second control instruction database to obtain a second control instruction; wherein a mapping relationship is preset between the deviation data and the second control instruction;

[0013] According to the second control instruction, the load device is controlled so that the load device performs air conditioning work.

[0014] Preferably, the pre-processing method includes:

[0015] According to the air information, data standardization is performed to obtain a sample set of the air information; wherein the sample set includes a three-dimensional vector composed of the air temperature, air humidity, and air circulation rate, and the sample set D = {x1, x2, ..., x n}. Preferably, ...

[0016] Preferably, the air abnormal value detection is performed based on the pre-processed air information to obtain abnormal data, including:

[0017] According to the sample set, core points are selected according to preset conditions to obtain information core points;

[0018] According to the information core point, boundary points are selected according to preset conditions to obtain information boundary points;

[0019] Perform clustering according to the information core points and the information boundary points to obtain air information clusters;

[0020] According to the air information cluster, data points are traversed to obtain abnormal data.

[0021] Preferably, the preset conditions include:

[0022] Define the radius R as the maximum distance from one data point to another;

[0023] Define the minimum number of data points S as the minimum number of neighbors;

[0024] Selecting a first data point from the sample set, and when there are at least S data points within a neighborhood with the first data point as the center and R as the radius, determining the first data point as the information core point;

[0025] A second data point is selected from the sample set. When the number of data points existing in a neighborhood with the second data point as the center and R as the radius is less than S, and the second data point is in the neighborhood of other information core points, the second data point is determined as an information boundary point.

[0026] Preferably, clustering is performed according to the information core points and the information boundary points to obtain air information clusters, including:

[0027] In the sample set D = {x1, x2, ..., x n} to get any data point x i , according to the preset conditions, determine the data point x i Is it an information core point? If so, take the data point x i Form an air information cluster C for the information core point m If not, obtain different data points from the sample set again and repeat the information core point judgment until the air information cluster C is obtained. m .

[0028] Preferably, clustering is performed according to the information core points and the information boundary points to obtain air information clusters, and further includes:

[0029] Select other data points x from the neighborhood R that has been determined to be the information core point j , according to the preset conditions, determine the data point x j Is it an information core point? If so, the data point x j The data points in the R neighborhood of are added to the air information cluster; if not, the data point x j Determined as information boundary point.

[0030] Preferably, the step of screening outliers according to the air information cluster to obtain abnormal data includes:

[0031] According to the air information cluster, all data points in the air information cluster are removed from the sample set, and then core points and boundary points are selected according to the preset conditions to obtain the air information cluster after the data points are traversed;

[0032] The remaining data points are obtained according to the traversed air information clusters, and the remaining data points are determined as abnormal data.

[0033] Preferably, extracting data based on the abnormal data to obtain deviation data includes:

[0034] According to the abnormal data, data restoration is performed to obtain abnormal air information;

[0035] The abnormal air information is subtracted from the normal air information to obtain deviation data; wherein the normal air information is the air information corresponding to the first control instruction.

[0036] In a second aspect, the present invention provides a load remote control system based on remote control and energy-saving optimization, comprising:

[0037] An instruction acquisition module, used to acquire a first control instruction of a user;

[0038] A first instruction control module, used to control the load device according to the first control instruction so that the load device performs air conditioning;

[0039] The information acquisition module is used to acquire air information and pre-process the air information during the operation of the load equipment;

[0040] An anomaly detection module is used to detect air anomaly values ​​based on the preprocessed air information to obtain abnormal data;

[0041] A data extraction module, used for extracting data based on the abnormal data to obtain deviation data;

[0042] An instruction matching module, used for matching the deviation data with a preset second control instruction database to obtain a second control instruction;

[0043] The second instruction control module is used to control the load device according to the second control instruction so that the load device performs air conditioning.

[0044] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the load remote control methods based on remote control and energy-saving optimization described above.

[0045] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned load remote control methods based on remote control and energy-saving optimization.

[0046] Compared with the prior art, the present invention has the following beneficial effects: an embodiment of the present invention provides a load remote control method and system based on remote control and energy-saving optimization, the method comprising obtaining a first control instruction of a user; controlling a load device according to the first control instruction so that the load device performs air conditioning; obtaining air information during the operation of the load device, and preprocessing the air information; wherein the air information comprises: air temperature, air humidity, and air circulation rate; based on the preprocessed air information, performing air abnormal value detection to obtain abnormal data; performing data extraction according to the abnormal data to obtain deviation data; matching the deviation data with a preset second control instruction to obtain a second control instruction; wherein a mapping relationship is preset between the deviation data and the second control instruction; controlling the load device according to the second control instruction so that the load device performs air conditioning.

[0047] In the present invention, the method can control the load through the load remote control device according to the control instruction sent by the user, so that the load performs air conditioning. After receiving the control instruction sent by the user, the air temperature, air humidity, and air circulation rate can be detected in real time, and abnormal value detection can be performed based on the detected air temperature, air humidity, and air circulation rate information. If an abnormal value is detected, the air conditioning work of the load is adjusted to restore the air temperature, air humidity, and air circulation rate to the values ​​expected by the user. In summary, the user can remotely control the load through the load remote control device, which can reduce unnecessary energy consumption, such as remotely turning off the air conditioner when the user leaves; at the same time, the load device is intelligently adjusted according to the first control instruction and the second control instruction to adapt to the needs of the user and changes in the indoor environment, thereby improving the efficiency of energy use. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of a load remote control method based on remote control and energy-saving optimization provided by the first embodiment of the present invention;

[0049] Figure 2 It is a structural schematic diagram of a load remote control system based on remote control and energy-saving optimization provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Reference Figure 1 The first embodiment of the present invention provides a load remote control method based on remote control and energy-saving optimization, comprising the following steps:

[0052] S11, obtaining a first control instruction from a user;

[0053] S12, controlling the load device according to the first control instruction so that the load device performs air conditioning;

[0054] S13, during the operation of the load device, obtaining air information and preprocessing the air information;

[0055] S14, performing air abnormal value detection based on the pre-processed air information to obtain abnormal data;

[0056] S15, extracting data based on the abnormal data to obtain deviation data;

[0057] S16, matching the deviation data with a preset second control instruction database to obtain a second control instruction;

[0058] S17, controlling the load device according to the second control instruction so that the load device performs air conditioning.

[0059] It is worth noting that load remote control can include real-time monitoring and control of energy consumption of household electrical appliances. Load remote control can also include remotely operated switch devices for controlling loads connected to the power grid. It can be a simple switch operation or a more complex control strategy, such as timing control, scene control, etc. The present invention provides a load remote control method and system based on remote control and energy-saving optimization, which can remotely control the load through the load remote control device, and can continuously detect indoor air and keep the indoor air comfortable according to the detection value, so as to achieve the effect of improving energy efficiency.

[0060] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0061] In step S11, obtaining the user's first control instruction can be achieved in a variety of ways, including: CAT-1 (5G), Bluetooth, and infrared wireless communication.

[0062] In one implementation, the CAT-1 (5G) method includes: the user end uses a device that supports 5G network connection technology, such as a smart phone, tablet or other terminal; the load remote control device also needs to have 5G communication function and can access the 5G network; after both the user end and the load remote control end are connected to the network connection, a network connection is established at both ends, the user end sends a first control instruction through the device software, and the load remote control device obtains the user's first control instruction.

[0063] In another implementation, the infrared wireless communication method includes: the load remote control device has an external infrared transmitting sensor and a built-in infrared remote control code library, which can receive infrared control signals sent by the user to control the load's startup, shutdown, temperature adjustment, and adjustment mode (cooling, heating, ventilation, dehumidification).

[0064] In step S12, the load device is controlled according to the first control instruction so that the load device performs air conditioning. The process includes: the load remote control device receives the control instruction. The load remote control device first needs to receive the first control instruction sent by the user through the instruction acquisition module. This instruction may include but is not limited to turning on or off the device, setting a specific air temperature, adjusting the wind speed, etc.; the load remote control device parses the control instruction. After receiving the control instruction, the device parses the instruction to determine the specific operation requirements; the parsed instruction is sent to the load, and the load performs the corresponding instruction action according to the parsed instruction. For example, if the instruction action requires lowering the temperature, the load will increase the cooling power or extend the cooling time.

[0065] Specifically, the first control instruction includes: turning on or off the load, adjusting the temperature, adjusting the mode (cooling, heating, ventilation, dehumidification), setting a scheduled shutdown period, setting a scheduled startup period, setting a limit temperature, setting a startup temperature, setting a shutdown temperature, and setting a disabled period.

[0066] In step S13, during the operation of the load device, air information is acquired and preprocessed, wherein the air information includes: air temperature, air humidity, and air circulation rate.

[0067] Specifically, the above three types of air information can be obtained in the following ways:

[0068] (1) Obtained through sensors: Temperature sensors are used to detect the temperature of the air. Such sensors can measure the ambient temperature in real time through technologies such as thermistors, thermocouples or infrared thermometers; Humidity sensors are used to measure the humidity in the air. They are usually implemented through capacitive humidity sensors or resistive humidity sensors. They can monitor the water vapor content in the air and give relative humidity or absolute humidity values; Air flow rate sensors are used to detect the flow rate of the air. Common air flow rate sensors include hot wire anemometers, pitot tubes, etc., which can determine the air circulation conditions by measuring the flow rate of the air.

[0069] (2) Integrated environmental monitoring module: The system integrates an environmental monitoring module that can simultaneously measure temperature, humidity, and air flow rate. These modules usually contain multiple sensors and transmit the measurement data to the load remote control device through a unified interface.

[0070] Specifically, the preprocessing method includes: performing data standardization according to the air information to obtain a sample set of the air information. The sample set includes a three-dimensional vector composed of the air temperature, air humidity, and air circulation rate. The sample set D = {x1, x2, ..., x n}.

[0071] Since the DBSCAN algorithm used in step S14 is based on distance measurement and is sensitive to the distance between data points, if different features of the data (such as air temperature, humidity, and air circulation rate) have different scales, features with larger values ​​may have an excessive impact on the distance calculation, thereby causing deviations in the clustering results. Therefore, in the present invention, it is necessary to scale all features to the same scale through data standardization to eliminate the deviation caused by different magnitudes of feature values, so that the contribution of each feature to the distance calculation and clustering process is more balanced.

[0072] Among them, the methods of data standardization include: Z-score standardization (standard normal distribution standardization), Min-Max standardization (normalization), MaxAbs standardization, Robust standardization (robust standardization), Log standardization (logarithmic transformation), and L2 normalization. The specific data standardization should be determined according to the specific values ​​of air temperature, humidity, and air circulation rate. For example, if the data such as air temperature, air humidity, and air circulation rate roughly conform to the normal distribution, Z-score standardization can be selected.

[0073] It should be noted that the sample set D = {x1, x2, ..., x n} data point x i It is a three-dimensional vector composed of the standardized air temperature, air humidity and air circulation rate.

[0074] In step S14, the air abnormal value detection is performed based on the pre-processed air information to obtain abnormal data, including:

[0075] According to the sample set, core points are selected according to preset conditions to obtain information core points;

[0076] According to the information core point, boundary points are selected according to preset conditions to obtain information boundary points;

[0077] Perform clustering according to the information core points and the information boundary points to obtain air information clusters;

[0078] According to the air information cluster, data points are traversed to obtain abnormal data.

[0079] Step S14 uses the DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm, which is a density-based clustering algorithm used to find high-density areas from a data set and divide them into clusters. It can automatically identify clusters of any shape and has the ability to process noise (abnormal data). The DBSCAN algorithm mainly involves the following core concepts for clustering: ε-neighborhood, the ε-neighborhood of a data point refers to all points within a range less than or equal to ε from the point; minPts-minimum number of points, refers to the minimum number of neighbors; core point, if the ε-neighborhood of a data point contains at least minPts points (including itself), then the point is defined as a core point, and the core point is the key point to form a cluster; boundary point, although a point does not meet the conditions of becoming a core point (that is, the number of points in the ε-neighborhood is insufficient), but it is in the ε-neighborhood of a core point, then the point is called a boundary point, and the boundary point belongs to a part of the cluster; noise point, if a point is neither a core point nor a boundary point, it is regarded as a noise point or an abnormal point and does not belong to any cluster.

[0080] In the present invention, the DBSCAN algorithm is mainly used to detect outliers in the sample set, and then corresponding control is performed according to the detected outliers.

[0081] Specifically, the preset conditions include:

[0082] Define the radius R as the maximum distance from one data point to another;

[0083] Define the minimum number of data points S as the minimum number of neighbors;

[0084] Selecting a first data point from the sample set, and when there are at least S data points within a neighborhood with the first data point as the center and R as the radius, determining the first data point as the information core point;

[0085] A second data point is selected from the sample set. When the number of data points existing in a neighborhood with the second data point as the center and R as the radius is less than S, and the second data point is in the neighborhood of other information core points, the second data point is determined as an information boundary point.

[0086] According to the preset conditions, the information core point and the information boundary point can be determined respectively, laying the foundation for subsequent screening to obtain abnormal data. Specifically, when selecting the information core point, a data point x should be randomly selected from the sample set. i , if the data point x i If there are more than S data points within a radius of less than R around it, it is considered to be an information core point; when selecting information boundary points, a data point x should be randomly selected from the sample set. i , if the data point x i If the number of data points contained in the radius R neighborhood is less than S, but it is within other information core points, then the data point x is called i is the information boundary point; if the randomly selected data point x i If the information core point determination conditions are not met, and the information boundary point determination conditions are not met, then it is determined to be a noise point, i.e., abnormal data.

[0087] In one embodiment, clustering is performed based on the information core points and the information boundary points to obtain air information clusters, including: in the sample set D = {x1, x2, ..., x n} to get any data point x i , according to the preset conditions, determine the data point x i Is it an information core point? If so, take the data point x i Form an air information cluster C for the information core point m If not, obtain different data points from the sample set again and repeat the information core point judgment until the air information cluster C is obtained. m The purpose of this step is to find meaningful clusters in the air information (such as temperature, humidity, flow rate, etc.) data set and assign similar air information data points to the same cluster. Through this clustering, the air information can be better organized and analyzed, and the normal air status data cluster can be found for subsequent anomaly detection and control adjustments.

[0088] In another embodiment, clustering is performed based on the information core point and the information boundary point to obtain the air information cluster, and further includes: selecting other data points x from the R neighborhood of the information core point determined to be the information core point j , according to the preset conditions, determine the data point x j Is it an information core point? If so, the data point x j The data points in the R neighborhood of are added to the air information cluster; if not, the data point x j The purpose of this step is to ensure the integrity of the air information cluster, increase the accuracy of the clustering results, and ensure that all relevant data points can be correctly classified into appropriate clusters. It also lays the foundation for subsequent abnormal data detection, because accurate cluster structure helps to identify abnormal points more clearly.

[0089] Specifically, the step of screening outliers according to the air information cluster to obtain abnormal data includes:

[0090] According to the air information cluster, all data points in the air information cluster are removed from the sample set, and then core points and boundary points are selected according to the preset conditions to obtain the air information cluster after the data points are traversed;

[0091] The remaining data points are obtained according to the traversed air information clusters, and the remaining data points are determined as abnormal data.

[0092] It is worth noting that the remaining data points are the points that neither meet the core point conditions nor are included in a cluster after all data points in the air information cluster are removed from the sample set and the core points and boundary points are selected according to the preset conditions to obtain the air information cluster after the data points are traversed. The remaining data points are also called noise points. In the present invention, they are defined as abnormal data.

[0093] In step S15, data extraction is performed based on the abnormal data to obtain deviation data, including:

[0094] According to the abnormal data, data restoration is performed to obtain abnormal air information;

[0095] According to the abnormal air information, the deviation data is obtained by subtracting the abnormal air information from the normal air information;

[0096] Specifically, the abnormal data obtained is the air information after data standardization, including the air temperature, air humidity, and air circulation rate after data standardization. After data restoration, the air information (air temperature, air humidity, air circulation rate) that deviates from the normal value is obtained, that is, abnormal air information. The reasons for the deviation from the normal value include: sudden changes in climate conditions, seasonal changes in climate, and human-induced changes in indoor air. The above factors will cause the air information to deviate from the original normal value.

[0097] Specifically, the data restoration method includes: performing an inverse operation corresponding to the data standardization used in step S13 on the obtained abnormal data, so as to obtain the abnormal air information in the same format as the normal air information.

[0098] Specifically, before the abnormal air information is subtracted from the normal air information, it should be ensured that the format of the abnormal air information and the normal air information is the same. It is worth noting that the normal air information is the air information corresponding to the first control instruction. For example, if the first control instruction is: room temperature "22°C", humidity "50%", and air flow rate "normal", then the normal air information is set to: room temperature "22°C", humidity "50%", and air flow rate "normal". The obtained deviation information should be the difference between the abnormal air information and the normal air information.

[0099] In step S16, the deviation data is matched with a preset second control instruction database to obtain a second control instruction. Specifically, a mapping relationship is preset between the deviation data and the second control instruction. It is worth noting that the mapping relationship between the deviation data and the second control instruction means that the system associates the deviation of the air information (i.e., the difference between the actual data and the normal data) with the corresponding control instruction through a predefined logic or algorithm. When the system detects that the air parameter deviates from the normal value, it will match the preset second control instruction according to this mapping relationship to adjust the working state of the equipment and restore it to the expected air state. Among them, the mapping relationship can include the following forms: threshold mapping, pre-setting the upper and lower threshold ranges of the deviation data, whenever the deviation exceeds a certain threshold, the corresponding control instruction is triggered, such as when the indoor temperature deviation is greater than ±2°C, the corresponding cooling or heating instruction is triggered; proportional mapping, proportionally adjusting the control instruction according to the size of the deviation, the larger the deviation, the greater the adjustment force, such as when the temperature deviation is +3°C, the cooling power of the air conditioner is set to 60%, when the temperature deviation is +5°C, the cooling power is set to 80%; logical mapping, based on multi-dimensional deviation data, using logical judgment to determine the control instruction, this mapping relationship can take into account the deviation of multiple parameters, such as temperature, humidity, flow rate, etc., and generate suitable control instructions through logical rules. For example, when the temperature deviation is greater than +3°C and the humidity deviation is greater than +10%, the system will trigger cooling and dehumidification instructions at the same time.

[0100] It is worth noting that the second control instructions may include: temperature adjustment instructions, such as cooling instructions, heating instructions, and adjusting the set temperature; humidity adjustment instructions, such as dehumidification instructions, humidification instructions, and adjusting the set humidity; air flow rate adjustment instructions, such as increasing wind speed instructions, reducing wind speed instructions, and turning on / off ventilation equipment; comprehensive adjustment instructions, such as multi-parameter linkage adjustment and scene control instructions (sleep mode, working mode); equipment mode adjustment instructions, such as switching working modes (energy-saving mode, high-efficiency mode) and adjusting the operating cycle; equipment start and stop instructions; energy consumption optimization instructions; alarm and notification instructions.

[0101] In step S17, the load device is controlled according to the second control instruction so that the load device performs air conditioning. The process includes: the load remote control device receives the control instruction. The load remote control device first needs to receive the first control instruction sent by the user through the instruction acquisition module. This instruction may include but is not limited to turning on or off the device, setting a specific air temperature, adjusting the wind speed, etc.; the load remote control device parses the control instruction. After receiving the control instruction, the device parses the instruction to determine the specific operation requirements; the parsed instruction is sent to the load, and the load performs the corresponding instruction action according to the parsed instruction. For example, if the instruction action requires lowering the temperature, the load will increase the cooling power or extend the cooling time.

[0102] It is worth noting that the air conditioning work of the load equipment refers to adjusting the air parameters in the environment through the load control system to meet the user's comfort needs or optimize energy consumption. Air conditioning work usually involves the adjustment of temperature, humidity, and air flow. The air conditioning work of the load includes: temperature regulation, such as cooling, heating, and temperature maintenance; humidity regulation, such as dehumidification, humidification, and humidity balance; air flow regulation, such as air circulation, ventilation, and air flow rate control.

[0103] The following describes the working process of the present invention by taking a relatively common scenario as an example. The working process is as follows:

[0104] There is a load remote control device that connects the load and the user end through wireless communication methods including CAT-1 (5G), Bluetooth, and infrared. There is also a user end that uses a smart phone, tablet or other terminal to connect to the load remote control device through CAT-1 (5G), Bluetooth, and infrared wireless communication methods.

[0105] Users want to remotely control their home air conditioning systems to maintain indoor air comfort and optimize energy consumption. Users use a 5G-enabled smartphone to send control commands through a mobile app to adjust the temperature, humidity and air flow rate of their home air conditioner.

[0106] First, the user issues the first control instruction through the mobile phone application, requiring the indoor temperature to be set to 22°C, the humidity to be set to 50%, and the air conditioner working mode to be set to "cooling". The mobile phone sends the control instruction to the load remote control device at home through the 5G network. After receiving the first control instruction, the load remote control device parses the instruction and controls the air conditioner to start working. The air conditioner starts cooling, adjusts the temperature, and adjusts the humidity and wind speed according to the set value. During the operation of the air conditioner, the load remote control device obtains the indoor air temperature, humidity and air flow rate data in real time through sensors. These data will be standardized so that the data values ​​of different features are on the same scale for subsequent clustering and outlier detection. The load remote control device uses the DBSCAN clustering algorithm to detect outliers on the standardized air information. The system divides the air information into normal data clusters and identifies abnormal data (such as temperature and humidity deviations from normal values). The system calculates the deviation of air parameters based on the detected abnormal data. For example, suppose that due to a sudden change in the external environment, the indoor temperature rises to 25°C and the humidity rises to 60%. The system compares the abnormal air information with the set normal air information (22°C and 50%), and obtains the deviation data: the temperature deviation is +3°C, and the humidity deviation is +10%. The system matches the deviation data with the preset second control instruction database. Based on the preset mapping rules, the system decides the adjustment measures to be taken. For example, based on the temperature deviation of +3°C, the system decides to increase the cooling power of the air conditioner, and at the same time, based on the humidity deviation of +10%, the dehumidification function is started. The system matches the deviation data with the preset second control instruction database. Based on the preset mapping rules, the system decides the adjustment measures to be taken. For example, based on the temperature deviation of +3°C, the system decides to increase the cooling power of the air conditioner, and at the same time, based on the humidity deviation of +10%, the dehumidification function is started.

[0107] It is worth mentioning that during the entire process, the load remote control equipment will continuously monitor the air information and make adjustments based on abnormal conditions to ensure the comfort and energy-saving optimization of the indoor environment.

[0108] In summary, the present invention provides a load remote control method based on remote control and energy-saving optimization, including obtaining a first control instruction from a user; controlling a load device according to the first control instruction so that the load device performs air conditioning; obtaining air information during the operation of the load device, and preprocessing the air information; wherein the air information includes: air temperature, air humidity, and air circulation rate; based on the preprocessed air information, performing air abnormality detection to obtain abnormal data; based on the abnormal data, performing data extraction to obtain deviation data; matching the deviation data with a preset second control instruction database to obtain a second control instruction; wherein a mapping relationship is preset between the deviation data and the second control instruction; according to the second control instruction, controlling the load device so that the load device performs air conditioning.

[0109] It is worth noting that load remote control can include real-time monitoring and control of the energy consumption of household electrical appliances. Load remote control can also include remotely operated switching devices to control loads connected to the power grid. It can be a simple switch operation or a more complex control strategy, such as timing control, scene control, etc.

[0110] In the present invention, the method can control the load through the load remote control device according to the control instruction sent by the user, so that the load performs air conditioning. After receiving the control instruction sent by the user, the air temperature, air humidity, and air circulation rate can be detected in real time, and abnormal value detection can be performed based on the detected air temperature, air humidity, and air circulation rate information. If an abnormal value is detected, the air conditioning work of the load is adjusted to restore the air temperature, air humidity, and air circulation rate to the values ​​expected by the user. In summary, the user can remotely control the load through the load remote control device, which can reduce unnecessary energy consumption, such as remotely turning off the air conditioner when the user leaves; at the same time, the load device is intelligently adjusted according to the first control instruction and the second control instruction to adapt to the needs of the user and changes in the indoor environment, thereby improving the efficiency of energy use.

[0111] Reference Figure 2 The second embodiment of the present invention provides a load remote control device based on remote control and energy-saving optimization, comprising:

[0112] An instruction acquisition module, used to acquire a first control instruction of a user;

[0113] A first instruction control module, used to control the load device according to the first control instruction so that the load device performs air conditioning;

[0114] The information acquisition module is used to acquire air information and pre-process the air information during the operation of the load equipment;

[0115] An anomaly detection module is used to detect air anomaly values ​​based on the preprocessed air information to obtain abnormal data;

[0116] A data extraction module, used for extracting data based on the abnormal data to obtain deviation data;

[0117] An instruction matching module, used for matching the deviation data with a preset second control instruction database to obtain a second control instruction;

[0118] The second instruction control module is used to control the load device according to the second control instruction so that the load device performs air conditioning.

[0119] In an optional implementation, the information acquisition module is specifically used to:

[0120] According to the air information, data standardization is performed to obtain a sample set of the air information; wherein the sample set includes a three-dimensional vector composed of the air temperature, air humidity, and air circulation rate, and the sample set D = {x1, x2, ..., x n}.

[0121] In an optional implementation, the anomaly detection module is specifically used to:

[0122] According to the sample set, core points are selected according to preset conditions to obtain information core points;

[0123] According to the information core point, boundary points are selected according to preset conditions to obtain information boundary points;

[0124] Perform clustering according to the information core points and the information boundary points to obtain air information clusters;

[0125] According to the air information cluster, data points are traversed to obtain abnormal data.

[0126] In an optional implementation, the anomaly detection module is further specifically configured to:

[0127] Define the radius R as the maximum distance from one data point to another;

[0128] Define the minimum number of data points S as the minimum number of neighbors;

[0129] Selecting a first data point from the sample set, and when there are at least S data points within a neighborhood with the first data point as the center and R as the radius, determining the first data point as the information core point;

[0130] A second data point is selected from the sample set. When the number of data points existing in a neighborhood with the second data point as the center and R as the radius is less than S, and the second data point is in the neighborhood of other information core points, the second data point is determined as an information boundary point.

[0131] In an optional implementation, the anomaly detection module is further specifically configured to: in the sample set D = {x1, x2, ..., x n} to get any data point x i , according to the preset conditions, determine the data point x i Is it an information core point? If so, take the data point x i Form an air information cluster C for the information core point m If not, obtain different data points from the sample set again and repeat the information core point judgment until the air information cluster C is obtained. m .

[0132] In an optional implementation, the anomaly detection module is further specifically configured to:

[0133] Select other data points x from the neighborhood R that has been determined to be the information core point j , according to the preset conditions, determine the data point x j Is it an information core point? If so, the data point x j The data points in the R neighborhood of are added to the air information cluster; if not, the data point x j Determined as information boundary point.

[0134] In an optional implementation, the anomaly detection module is further specifically configured to:

[0135] According to the air information cluster, all data points in the air information cluster are removed from the sample set, and then core points and boundary points are selected according to the preset conditions to obtain the air information cluster after the data points are traversed;

[0136] The remaining data points are obtained according to the traversed air information clusters, and the remaining data points are determined as abnormal data.

[0137] In an optional implementation, the data extraction module is specifically used to:

[0138] According to the abnormal data, data restoration is performed to obtain abnormal air information;

[0139] The abnormal air information is subtracted from the normal air information to obtain deviation data; wherein the normal air information is the air information corresponding to the first control instruction.

[0140] It should be noted that a load remote control system based on remote control and energy-saving optimization provided in an embodiment of the present invention is used to execute all process steps of a load remote control method based on remote control and energy-saving optimization in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and therefore will not be repeated here.

[0141] The embodiment of the present invention further provides a terminal device. The terminal device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a load remote control program based on remote control and energy-saving optimization. When the processor executes the computer program, the steps in the above-mentioned load remote control method embodiments based on remote control and energy-saving optimization are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are implemented, such as an abnormality detection module.

[0142] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0143] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of terminal devices and do not constitute a limitation on the terminal device. The terminal device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0144] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0145] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the terminal device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0146] Wherein, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0147] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0148] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A load remote control method based on remote control and energy-saving optimization, characterized in that: include: Obtaining a first control instruction from a user; According to the first control instruction, the load device is controlled so that the load device performs air conditioning work; During the operation of the load equipment, air information is obtained and preprocessed; wherein the air information includes: air temperature, air humidity, and air circulation rate; Based on the pre-processed air information, air anomaly detection is performed to obtain abnormal data; Extracting data based on the abnormal data to obtain deviation data; Matching the deviation data with a preset second control instruction database to obtain a second control instruction; wherein a mapping relationship is preset between the deviation data and the second control instruction; According to the second control instruction, the load device is controlled so that the load device performs air conditioning work.

2. The load remote control method based on remote control and energy-saving optimization according to claim 1 is characterized in that: The pre-processing method includes: According to the air information, data standardization is performed to obtain a sample set of the air information; wherein the sample set includes a three-dimensional vector composed of the air temperature, air humidity, and air circulation rate, and the sample set D = {x1, x2, ..., x n }.

3. The load remote control method based on remote control and energy-saving optimization according to claim 2 is characterized in that: The method of performing air abnormal value detection based on the pre-processed air information to obtain abnormal data includes: According to the sample set, core points are selected according to preset conditions to obtain information core points; According to the information core point, boundary points are selected according to preset conditions to obtain information boundary points; Perform clustering according to the information core points and the information boundary points to obtain air information clusters; According to the air information cluster, data points are traversed to obtain abnormal data.

4. The load remote control method based on remote control and energy-saving optimization according to claim 3 is characterized in that: The preset conditions include: Define the radius R as the maximum distance from one data point to another; Define the minimum number of data points S as the minimum number of neighbors; Selecting a first data point from the sample set, and when there are at least S data points within a neighborhood with the first data point as the center and R as the radius, determining the first data point as the information core point; A second data point is selected from the sample set. When the number of data points existing in a neighborhood with the second data point as the center and R as the radius is less than S, and the second data point is in the neighborhood of other information core points, the second data point is determined as an information boundary point.

5. The load remote control method based on remote control and energy-saving optimization according to claim 3 is characterized in that: The clustering is performed according to the information core points and the information boundary points to obtain air information clusters, including: In the sample set D = {x1, x2, ..., x n } to get any data point x i , according to the preset conditions, determine the data point x i Is it an information core point? If so, take the data point x i Form an air information cluster C for the information core point m If not, obtain different data points from the sample set again and repeat the information core point judgment until the air information cluster C is obtained. m .

6. The load remote control method based on remote control and energy-saving optimization according to claim 3 is characterized in that: The clustering is performed according to the information core points and the information boundary points to obtain air information clusters, and further includes: Select other data points x from the neighborhood R that has been determined to be the information core point j , according to the preset conditions, determine the data point x j Is it an information core point? If so, the data point x j The data points in the R neighborhood of are added to the air information cluster; if not, the data point x j Determined as information boundary point.

7. The load remote control method based on remote control and energy-saving optimization according to claim 3 is characterized in that: The step of screening outliers according to the air information cluster to obtain abnormal data includes: According to the air information cluster, all data points in the air information cluster are removed from the sample set, and then core points and boundary points are selected according to the preset conditions to obtain the air information cluster after the data points are traversed; The remaining data points are obtained according to the traversed air information clusters, and the remaining data points are determined as abnormal data.

8. The load remote control method based on remote control and energy-saving optimization according to claim 1 is characterized in that: The step of extracting data based on the abnormal data to obtain deviation data includes: According to the abnormal data, data restoration is performed to obtain abnormal air information; The abnormal air information is subtracted from the normal air information to obtain deviation data; wherein the normal air information is the air information corresponding to the first control instruction.

9. A load remote control system based on remote control and energy-saving optimization, characterized in that: include: An instruction acquisition module, used to acquire a first control instruction of a user; A first instruction control module, used to control the load device according to the first control instruction so that the load device performs air conditioning; The information acquisition module is used to acquire air information and pre-process the air information during the operation of the load equipment; An anomaly detection module is used to detect air anomaly values ​​based on the preprocessed air information to obtain abnormal data; A data extraction module, used for extracting data based on the abnormal data to obtain deviation data; An instruction matching module, used for matching the deviation data with a preset second control instruction database to obtain a second control instruction; The second instruction control module is used to control the load device according to the second control instruction so that the load device performs air conditioning.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the load remote control method based on remote control and energy-saving optimization as described in any one of claims 1 to 8.

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