Household intelligent electricity utilization optimization regulation and control system based on energy management

Abstract

The invention relates to the technical field of electric equipment regulation and control, in particular to a household intelligent electricity utilization optimization regulation and control system based on energy management, which performs dynamic detection and evaluation among various transmission protocols such as ZigBee and Matter through multi-mode gateway equipment, comprehensively selects an optimal data transmission channel based on signal transmission delay, attenuation and packet loss rate, and realizes energy management. And when all protocols do not reach the standard, the Bluetooth standby channel is automatically started, so that the problem that a control instruction is invalid due to communication interference in a home environment is thoroughly solved. Besides, the system performs personalized fine adjustment on the general energy consumption prediction model by using a transfer learning technology, generates a high-precision household adaptive energy consumption prediction model, and realizes refined and personalized adjustment on electric equipment such as air conditioners and illumination by combining average preference vectors of members in different household areas. According to the invention, the stability of data transmission of household electric equipment and the intelligent level of regulation and control are effectively improved, and energy-saving optimization is achieved while the comfort level of members is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of electrical equipment control technology, and in particular to a smart home power optimization and control system based on energy management. Background Technology

[0002] With the popularization of distributed photovoltaics, energy storage, and high-power home appliances, households have transformed from simple electricity users into microgrid nodes integrating generation, storage, and consumption. Therefore, refined and automated household electricity regulation has become a common key link in achieving user comfort.

[0003] Traditional methods often rely on single Wi-Fi or ZigBee protocols for communication, which are susceptible to interference in the complex electromagnetic environment of a home. This can lead to packet loss or excessive latency in control commands, resulting in control failure. Furthermore, existing energy consumption prediction models lack versatility and cannot adapt to the specific electricity usage habits of different households, leading to large prediction errors and unsatisfactory control effects. Summary of the Invention

[0004] This invention provides a smart home power optimization and control system based on energy management, the main purpose of which is to improve the stability of data transmission of home electrical equipment and enhance the intelligence level of home electrical equipment control.

[0005] To achieve the above objectives, the present invention provides a home intelligent electricity optimization and control system based on energy management, comprising:

[0006] Receive power consumption optimization instructions, and identify a set of household electrical appliances based on the power consumption optimization instructions. The set of household electrical appliances includes multiple household electrical appliances.

[0007] The transmission protocols are extracted sequentially from multiple preset transmission protocols. Based on the transmission protocols and the pre-built gateway devices, a query command is generated and broadcast to all household electrical devices in the household electrical device set to obtain the query device set.

[0008] Based on the query device set, the transmission quality is evaluated to obtain the protocol transmission quality. The protocol transmission quality is then summarized to obtain the transmission quality of multiple protocols.

[0009] The transmission quality of multiple protocols is judged based on a preset transmission quality threshold to obtain a pass / fail result, which is either pass or fail.

[0010] If the qualification result is qualified, the optimal transmission quality is identified among multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol.

[0011] If the pass / fail result is unqualified, the pre-built Bluetooth channel will be started and the started Bluetooth channel will be recorded as the data transmission channel;

[0012] Based on the data transmission channel and the collection of household electrical equipment data sets, the data sets are input into a pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption.

[0013] Based on the preset current mode and predicted power consumption, the mode of the household electrical equipment set is switched to obtain the target electrical equipment set, wherein the current mode is either energy storage mode or power consumption mode.

[0014] The system divides the household into multiple areas, performs personalized management of members in each area based on a target set of electrical devices, obtains a set of adjustable electrical devices, and completes intelligent home power optimization and control based on energy management based on this set of adjustable electrical devices.

[0015] Optionally, the transmission quality assessment based on the query device set to obtain the protocol transmission quality includes:

[0016] Perform the following operation on each query device in the query device set:

[0017] The pre-configuration command for the query device is identified, and the pre-configuration command is activated based on the query command to obtain the activation command;

[0018] Based on the activation command and the query device, the gateway device is subjected to multiple signal transmissions to obtain a set of received signals. The set of received signals includes multiple received signals, and the received signals include: generation timestamp, household appliance ID and return signal.

[0019] The signal quality is obtained by evaluating the signal quality based on the received signal set.

[0020] Summarize the signal quality data for each device to obtain a set of signal quality data.

[0021] The transmission quality of the protocol is calculated based on the signal quality set.

[0022] Optionally, the system further includes: confirming the pre-configuration command for the query device.

[0023] Design a signal return script, which is used to execute multiple signal transmission steps and includes an activation interface. The signal return script sets the signal strength, number of transmissions, transmission interval, and device ID space.

[0024] Construct an instruction listening script, which is used to determine whether a query instruction has been received;

[0025] By embedding the instruction listening script into the activation interface of the signal return script, a control command template is obtained.

[0026] Confirm the household appliance ID corresponding to the queried device;

[0027] The control command template is embedded into the home appliance corresponding to the queried device based on the home appliance ID to obtain a pre-configured command. After the control command template is embedded, the empty device ID in the control command template will be replaced with the home appliance ID.

[0028] Optionally, the step of performing multiple signal transmissions to the gateway device based on the activation command and the query device to obtain a received signal set includes:

[0029] Based on the activation command, the configured signal strength, configured transmission interval, and configured number of transmissions are confirmed.

[0030] A return signal is generated based on the preset transmission time, the query device, and the configured signal strength, and the generation timestamp of the return signal is recorded.

[0031] The generated timestamp, the household appliance ID corresponding to the queried device, and the return signal are transmitted to the gateway device to obtain the received signal. The received signal includes: the generated timestamp, the household appliance ID, and the return signal.

[0032] Record the number of signals generated;

[0033] Calculate the re-emission time based on the generated timestamp and configured transmission interval;

[0034] The repeated transmission time is used as the transmission time, and the process of generating a return signal based on the preset transmission time, querying the device, and configuring the signal strength is repeated until the number of signals generated equals the configured number of transmissions.

[0035] The received signals are summarized to obtain the received signal set.

[0036] Optionally, the step of evaluating signal quality based on the received signal set to obtain signal quality includes:

[0037] The received signals are extracted sequentially from the received signal set;

[0038] The transmission timestamp and reception timestamp of the received signal are identified, and the signal transmission delay is calculated based on the transmission timestamp and reception timestamp;

[0039] Identify the received signal strength and calculate the signal attenuation value based on the received signal strength and the configured signal strength;

[0040] By summing up the signal transmission delay and signal attenuation values ​​respectively, we obtain the signal transmission delay set and the signal attenuation value set;

[0041] The average transmission delay and average attenuation values ​​are calculated by averaging the signal transmission delay set and the signal attenuation value set, respectively.

[0042] The number of received signals in the received signal set is counted, and the signal packet loss rate is calculated based on the number of received signals and the configured number of transmissions.

[0043] Signal quality is calculated based on packet loss rate, average transmission delay, and average attenuation.

[0044] Optionally, before inputting the device operation dataset into a pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption, the system further includes:

[0045] Read the general energy consumption prediction model;

[0046] The household electrical equipment set is periodically collected according to the preset transfer learning cycle to obtain multiple current household datasets;

[0047] Multiple current household datasets are input into a general energy consumption prediction model to obtain multiple current predicted energy consumptions;

[0048] Multiple prediction times for multiple current predicted energy consumptions are identified, and multiple current actual energy consumptions are read based on multiple prediction times, wherein the current predicted energy consumption corresponds one-to-one with the current actual energy consumption.

[0049] Multiple prediction error values ​​are constructed based on multiple current predicted energy consumption and multiple current actual energy consumption;

[0050] Multiple prediction error values ​​are matched with multiple current predicted energy consumption values ​​to obtain multiple training sample data, wherein the training sample data includes prediction error values ​​and current predicted energy consumption.

[0051] The adjustable network weight set of the general energy consumption prediction model was identified. The adjustable network weight set in the energy consumption prediction model was fine-tuned using multiple training sample data to obtain an adaptive energy consumption prediction model.

[0052] Optionally, the step of switching the mode of the household electrical equipment set according to the preset current mode and predicted energy consumption to obtain the target electrical equipment set includes:

[0053] Generate mode switching instructions based on predicted energy consumption and current mode;

[0054] Using a pre-built central control unit and data transmission channel, mode switching commands are broadcast to a set of household electrical appliances and a pre-built renewable energy storage module to obtain a target set of electrical appliances, which includes a renewable energy storage module.

[0055] Optionally, the step of generating a mode switching instruction based on predicted energy consumption and the current mode includes:

[0056] If the predicted power consumption is less than the preset low energy consumption threshold, then determine whether the current mode is energy storage mode.

[0057] If the current mode is energy storage mode, then a continuous command is generated; if the current mode is not energy storage mode, then an energy storage mode command is generated.

[0058] The continuous command or energy storage mode command shall be recorded as the mode switching command;

[0059] Otherwise, generate a continuous command or a power consumption mode command, and record the continuous command or power consumption mode command as a mode switching command.

[0060] Optionally, the step of performing personalized management of multiple household areas based on a target set of electrical appliances to obtain a set of adjustable electrical appliances includes:

[0061] Extract family regions sequentially from multiple family regions;

[0062] Family members are identified within the family area to obtain a family member ID table, which includes multiple family member IDs.

[0063] Extract family member IDs sequentially from the family member ID table, and extract the target member preference vector from the pre-built, pre-configured member preference vector table based on the family member ID;

[0064] The target member preference vectors are aggregated to obtain multiple target member preference vectors. Vector averaging is then performed on these multiple target member preference vectors to obtain an average preference vector. The average preference vector includes one or more of the following: temperature preference value, light preference value, and humidity preference value.

[0065] The target electrical equipment set identifies the regional electrical equipment set of the household area, wherein the regional electrical equipment set includes multiple regional electrical equipment;

[0066] The set of adjustable electrical equipment in the regional electrical equipment cluster is identified based on the average preference vector;

[0067] Adjust each adjustable electrical device in the adjustable electrical equipment set according to the average preference vector to obtain a single-area electrical equipment set;

[0068] By summarizing the sets of electrical devices used in each individual area of ​​a household, a set of adjustable electrical devices can be obtained.

[0069] To achieve the above objectives, the present invention also provides a home intelligent electricity optimization and control system based on energy management, comprising:

[0070] The electrical equipment query module is used to receive power optimization instructions, identify the set of household electrical equipment based on the power optimization instructions, wherein the set of household electrical equipment includes multiple household electrical equipment, extract the transmission protocol sequentially from multiple preset transmission protocols, generate a query instruction based on the transmission protocol and the pre-built gateway device, and broadcast the query instruction to all household electrical equipment in the set of household electrical equipment to obtain the query device set;

[0071] The transmission quality assessment module is used to assess the transmission quality based on the query device set, obtain the protocol transmission quality, and summarize the protocol transmission quality to obtain the transmission quality of multiple protocols.

[0072] The transmission channel construction module is used to judge the passability of multiple protocol transmission quality based on a preset transmission quality threshold and obtain a passability result, which is either passable or unpassable. If the passability result is passable, the optimal transmission quality is identified among the multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol. If the passability result is unpassable, the pre-constructed Bluetooth channel is started, and the started Bluetooth channel is recorded as the data transmission channel.

[0073] The electrical equipment management module is used to collect equipment operation datasets based on the data transmission channel and the household electrical equipment set. The equipment operation datasets are input into a pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption. Based on the preset current mode and the predicted energy consumption, the mode of the household electrical equipment set is switched to obtain the target electrical equipment set. The current mode is either energy storage mode or electricity consumption mode. Multiple household areas are divided, and personalized management of members in multiple household areas is carried out based on the target electrical equipment set to obtain the regulated electrical equipment set.

[0074] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0075] Memory, storing at least one instruction;

[0076] The processor executes the instructions stored in the memory to implement the aforementioned home intelligent power optimization and control system based on energy management.

[0077] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned energy management-based smart home power optimization and control system.

[0078] To address the problems described in the background, this invention first generates query commands based on transmission protocols and gateway devices. These commands are then broadcast to all household electrical devices in a centralized household electrical device set, resulting in a query device set. This step utilizes the gateway to poll and broadcast the query commands across different protocols, completing an online inventory and protocol capability survey of the entire network in one go. This rapidly collects the sample data required for subsequent quality assessment, laying a real-time and complete foundation for dynamic routing. Next, transmission quality is assessed based on the query device set to obtain protocol transmission quality. This step involves devices transmitting signals in a loop according to a pre-set script, with the same strength, frequency, and interval. By unifying control variables, the three-dimensional indicators of delay, attenuation, and packet loss are accurately measured, quantifying the quality of protocols into comparable single quality values. This ensures the stability of subsequent electrical device adjustments. Furthermore, based on transmission quality thresholds, the transmission quality of multiple protocols is judged for compliance, yielding compliance results. This step uses a unified threshold to assess the quality of multiple protocols. The system identifies and filters out all non-compliant links to eliminate the risk of control commands failing due to protocol instability. When all mainstream protocols fail, it falls back to the Bluetooth Mesh backup channel, ensuring that optimized commands can still reach the device. This allows the solution to remain connected and uncontrolled even in extreme environments with strong interference or protocol incompatibility. The device's operating dataset is input into an adaptive energy consumption prediction model to obtain predicted energy consumption. This step uses transfer learning to fine-tune the general energy consumption model into a personalized predictor for different households, allowing the prediction results to continuously converge with changes in household routines and seasons, achieving hourly lead time and kilowatt-hour accuracy in energy consumption forecasting. Finally, based on the target set of electrical devices, personalized management of multiple household areas is performed to obtain a set of adjustable electrical devices. This step integrates family member identities and average preference vectors at the room level to perform micro-environmental adaptive adjustment of adjustable devices, ensuring the comfort of different family members and improving the intelligence level of household electrical device control. Therefore, this invention can improve the stability of data transmission for household electrical devices and enhance the intelligence level of household electrical device control. Attached Figure Description

[0079] Figure 1 A flowchart illustrating a home smart electricity optimization and control system based on energy management, provided in an embodiment of the present invention;

[0080] Figure 2 A functional block diagram of a home intelligent power consumption optimization and control system based on energy management provided in an embodiment of the present invention;

[0081] Figure 3 This is a schematic diagram of the structure of an electronic device for implementing the energy management-based smart home power optimization and control system, as provided in an embodiment of the present invention.

[0082] Explanation of reference numerals in the attached figures:

[0083] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0084] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0085] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0086] This application provides a home intelligent electricity optimization and control system based on energy management. The executing entity of the home intelligent electricity optimization and control system based on energy management includes, but is not limited to, at least one of the electronic devices that can be configured to execute the system provided in this application embodiment, such as a server and a terminal. In other words, the home intelligent electricity optimization and control system based on energy management can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0087] Reference Figure 1 The diagram shown is a flowchart of a home intelligent electricity optimization and control system based on energy management, provided in an embodiment of the present invention. In this embodiment, the home intelligent electricity optimization and control system based on energy management includes:

[0088] S1. Receive power consumption optimization instructions, and identify a set of household electrical appliances based on the power consumption optimization instructions. The set of household electrical appliances includes multiple household electrical appliances.

[0089] Understandably, the power optimization instruction is a human-initiated instruction to optimize the power consumption of a set of household electrical devices. A set of household electrical devices refers to a collection of multiple household electrical devices, where household electrical devices refer to devices that require electricity for daily household use, such as smart sockets, lighting systems, and air conditioners.

[0090] S2. Extract the transmission protocol sequentially from the preset multiple transmission protocols, generate a query command based on the transmission protocol and the pre-built gateway device, and broadcast the query command to all household electrical devices in the household electrical device set to obtain the query device set.

[0091] It should be explained that the transmission protocol refers to a standard or convention used for communication between devices. In this solution, multiple transmission protocols include ZigBee 3.0 and Matter. The gateway device refers to the central control device responsible for coordinating communication between home appliances and external networks. The query command refers to a command generated by the gateway device for broadcasting to home appliances. This query command has the function of activating pre-configured commands. When a home appliance receives the query command, it will execute the subsequent activation command. The query device set refers to a collection of multiple query devices, where a query device refers to a home appliance that receives the query command.

[0092] S3. Evaluate the transmission quality based on the query device set to obtain the protocol transmission quality, summarize the protocol transmission quality, and obtain the transmission quality of multiple protocols.

[0093] Understandably, the protocol transmission quality refers to a numerical value that quantifies the communication performance of the transmission protocol. The higher the protocol transmission quality, the better the communication performance of the transmission protocol.

[0094] In detail, the transmission quality assessment based on the query device set to obtain the protocol transmission quality includes:

[0095] Perform the following operation on each query device in the query device set:

[0096] The pre-configuration command for the query device is identified, and the pre-configuration command is activated based on the query command to obtain the activation command;

[0097] Based on the activation command and the query device, the gateway device is subjected to multiple signal transmissions to obtain a set of received signals. The set of received signals includes multiple received signals, and the received signals include: generation timestamp, household appliance ID and return signal.

[0098] The signal quality is obtained by evaluating the signal quality based on the received signal set.

[0099] Summarize the signal quality data for each device to obtain a set of signal quality data.

[0100] The transmission quality of the protocol is calculated based on the signal quality set.

[0101] It is clear that the pre-configured command refers to a script or code segment pre-written by a person to perform signal transmission tasks. The activation command refers to the pre-configured command after activation. Activating the pre-configured command based on a query command means triggering the execution flow of the pre-configured command through a query command. The received signal set refers to a collection of multiple received signals. The received signal refers to the information returned by the query device to the gateway device after receiving a query command. The received signal is in the form of a data packet or signal frame, and includes a generation timestamp, the household appliance ID, and a return signal. The signal quality refers to the quality of the received signal returned by the query device. Calculating the protocol transmission quality based on the signal quality set means calculating the average value of all signal qualities in the signal quality set and using this average value as the protocol transmission quality.

[0102] Furthermore, the aforementioned pre-configuration command includes signal strength, number of transmissions, transmission interval, and device ID space. This pre-configuration command is embedded in each household appliance. When a household appliance receives a query command, the pre-configuration command will be activated. By running the activated pre-configuration command, the following operations will be performed: the household appliance will generate a signal (i.e., a return signal) with a strength specified in the pre-configuration command, and this signal will include the timestamp of the generation and the corresponding ID information of the household appliance. This signal will then be returned to the gateway device. The above signal return steps will be repeated every transmission interval until the number of returned signals equals the number of transmissions.

[0103] It should be explained that through the above operations, all household appliances can maintain a consistent transmission frequency, signal strength, and number of transmissions under different transmission protocols. These parameters can all be known in advance by the gateway device. Since the signal strength, number of transmissions, and transmission interval are all preset values, each household appliance follows a unified configuration, making the information content returned to the gateway device predictable. This not only reduces the amount of information carried by the received signal but also improves the efficiency of subsequent analysis and calculation of the received signal, ultimately achieving automation of transmission protocol quality assessment.

[0104] Specifically, the system further includes the following: confirming the pre-configured command for the query device.

[0105] Design a signal return script, which is used to execute multiple signal transmission steps and includes an activation interface. The signal return script sets the signal strength, number of transmissions, transmission interval, and device ID space.

[0106] Construct an instruction listening script, which is used to determine whether a query instruction has been received;

[0107] By embedding the instruction listening script into the activation interface of the signal return script, a control command template is obtained.

[0108] Confirm the household appliance ID corresponding to the queried device;

[0109] The control command template is embedded into the home appliance corresponding to the queried device based on the home appliance ID to obtain a pre-configured command. After the control command template is embedded, the empty device ID in the control command template will be replaced with the home appliance ID.

[0110] It should be explained that the signal return script refers to manually written code used to execute subsequent multiple signal transmission steps. This script is used to execute the subsequent steps of transmitting signals to the gateway device multiple times based on activation commands and device queries to obtain a received signal set. The activation interface refers to the code interface used to receive and respond to query commands. The signal strength refers to the signal strength returned by the household appliance to the gateway device, set manually, and its unit is dBm (decibels milliwatts). The number of transmissions refers to the number of signals returned by the household appliance to the gateway device, set manually. The transmission interval refers to the time interval between each two signal returns from the household appliance to the gateway device, set manually. The device ID empty space refers to the reserved position in the signal return script for filling in the specific household appliance ID.

[0111] Furthermore, the instruction monitoring script refers to the code segment used to continuously monitor whether a query instruction is received. This script determines whether a query instruction has been received; if so, it executes subsequent pre-configured commands. The control command template refers to the signal return script after the instruction monitoring script is embedded. The household appliance ID refers to the binary encoding of the device ID of the household appliance. Embedding the control command template into the household appliance corresponding to the query device based on the household appliance ID means: writing the control command template into the storage unit of the household appliance; after the control command template is embedded, the empty device ID slots in the control command template will be replaced with the household appliance ID, so that the signals subsequently transmitted to the gateway device (i.e., received signals) carry the specific household appliance ID.

[0112] In detail, the process of transmitting signals to the gateway device multiple times based on activation commands and query devices to obtain a received signal set includes:

[0113] Based on the activation command, the configured signal strength, configured transmission interval, and configured number of transmissions are confirmed.

[0114] A return signal is generated based on the preset transmission time, the query device, and the configured signal strength, and the generation timestamp of the return signal is recorded.

[0115] The generated timestamp, the household appliance ID corresponding to the queried device, and the return signal are transmitted to the gateway device to obtain the received signal. The received signal includes: the generated timestamp, the household appliance ID, and the return signal.

[0116] Record the number of signals generated;

[0117] Calculate the re-emission time based on the generated timestamp and configured transmission interval;

[0118] The repeated transmission time is used as the transmission time, and the process of generating a return signal based on the preset transmission time, querying the device, and configuring the signal strength is repeated until the number of signals generated equals the configured number of transmissions.

[0119] The received signals are summarized to obtain the received signal set.

[0120] It should be explained that the configured signal strength, configured transmission interval, and configured transmission count refer to the signal strength, transmission interval, and transmission count in the activation command, respectively. The transmission time refers to the time when the return signal is generated. At the beginning of signal transmission, this transmission time is the time when the query device receives the query command, and it will be continuously updated as the signal transmission progresses (continuously updated to the retransmission time). The return signal refers to the signal generated by the query device with the configured signal strength. The generation timestamp refers to the timestamp when the return signal is generated. The signal generation quantity refers to the number of received signals corresponding to the household appliance ID that the current gateway device has received. The retransmission time refers to the time one configured transmission interval after the generation timestamp. Calculating the retransmission time based on the generation timestamp and configured transmission interval means calculating the time one configured transmission interval after the generation timestamp; this time is the retransmission time.

[0121] In detail, the step of evaluating signal quality based on the received signal set to obtain signal quality includes:

[0122] The received signals are extracted sequentially from the received signal set;

[0123] The transmission timestamp and reception timestamp of the received signal are identified, and the signal transmission delay is calculated based on the transmission timestamp and reception timestamp;

[0124] Identify the received signal strength and calculate the signal attenuation value based on the received signal strength and the configured signal strength;

[0125] By summing up the signal transmission delay and signal attenuation values ​​respectively, we obtain the signal transmission delay set and the signal attenuation value set;

[0126] The average transmission delay and average attenuation values ​​are calculated by averaging the signal transmission delay set and the signal attenuation value set, respectively.

[0127] The number of received signals in the received signal set is counted, and the signal packet loss rate is calculated based on the number of received signals and the configured number of transmissions.

[0128] Signal quality is calculated based on packet loss rate, average transmission delay, and average attenuation.

[0129] It is clear that the transmission timestamp refers to the moment the received signal is generated, i.e., the generation timestamp mentioned above. The reception timestamp refers to the timestamp at which the gateway device receives the received signal. Since this reception timestamp is recorded and generated by the gateway device itself, it does not require an additional transmission process. The signal transmission delay refers to the absolute time difference between the reception timestamp and the transmission timestamp. The received signal strength refers to the signal strength of the received signal when the gateway device receives the signal. Because the signal will be lost and attenuated during the transmission process from the query device to the gateway device, this received signal strength is not equal to the configured signal strength. The signal attenuation value refers to the numerical value that quantifies the degree of attenuation of the received signal during transmission. The larger the signal attenuation value, the higher the degree of attenuation of the received signal during transmission. The formula for calculating the signal attenuation value is: ,in, Indicates the signal attenuation value. and These represent the received signal strength and the configured signal strength, respectively. The number of received signals refers to the number of signals received in the centralized signal reception group. The packet loss rate refers to the packet loss rate when the query device transmits a signal to the gateway device; this packet loss rate is calculated as follows: ,in, Indicates the signal packet loss rate. and These represent the number of received signals and the number of transmissions configured, respectively.

[0130] Furthermore, the formula for calculating the signal quality mentioned above is as follows:

[0131] ,

[0132] in, Indicates signal quality, This represents a weighting constant for signal transmission delay, set manually. This represents a mapping function that maps variables within the function to the range 0 to 1. Optionally, this mapping function can be: ,in, Let e ​​represent the function variable and e represent the natural constant. This represents a manually set adjustable constant. This represents a weighting constant that represents the manually set signal attenuation value. The weighting constant represents the manually set signal packet loss rate, where... .

[0133] S4. Based on a preset transmission quality threshold, the transmission quality of multiple protocols is judged to obtain a pass / fail result, where the pass / fail result is either pass or fail.

[0134] Understandably, the transmission quality threshold refers to a manually set constant used to determine whether the protocol transmission quality is qualified. When the transmission quality of each of the multiple protocol transmission qualities is less than the transmission quality threshold, it indicates that the multiple transmission protocols corresponding to the multiple protocol transmission qualities cannot guarantee stable data transmission, that is, multiple transmissions are unqualified, and therefore the qualification result is unqualified; otherwise, the qualification result is recorded as qualified.

[0135] S5. If the qualification result is qualified, the optimal transmission quality is identified among multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol.

[0136] It is clear that the optimal transmission quality refers to the transmission quality of the protocol with the highest numerical value among multiple protocol transmission quality values. The target protocol refers to the transmission protocol corresponding to the optimal transmission quality. The data transmission channel refers to a communication link established based on the target protocol for data transmission between home appliances and the gateway. Specifically, building a data transmission channel based on the target protocol means configuring the gateway device and home appliances to communicate using this protocol.

[0137] S6. If the pass / fail result is unqualified, start the pre-built Bluetooth channel and record the started Bluetooth channel as the data transmission channel.

[0138] It should be explained that when the pass / fail result is unsatisfactory, it indicates that there may be network interference or protocol incompatibility. In this case, it is necessary to maintain data transmission between the home appliance set and the gateway device through the Bluetooth module. The Bluetooth channel refers to a backup communication channel established based on the Bluetooth Mesh protocol.

[0139] S7. Based on the data transmission channel and the collection of household electrical equipment data sets, the data sets are input into the pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption.

[0140] Understandably, the device operation dataset refers to a collection of operation data from multiple devices. Each device operation data point corresponds to a household appliance, and this data includes multiple parameters such as temperature, voltage, and current of that appliance over a past period (e.g., the past 10 minutes). The adaptive energy consumption prediction model refers to a model that predicts the energy consumption of the entire household appliance set after a certain period (e.g., one hour later). The predicted energy consumption refers to the energy consumption output by the adaptive energy consumption prediction model, and its unit is kilowatt-hour (kWh).

[0141] Specifically, before inputting the device operation dataset into the pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption, the system further includes:

[0142] Read the general energy consumption prediction model;

[0143] The household electrical equipment set is periodically collected according to the preset transfer learning cycle to obtain multiple current household datasets;

[0144] Multiple current household datasets are input into a general energy consumption prediction model to obtain multiple current predicted energy consumptions;

[0145] Multiple prediction times for multiple current predicted energy consumptions are identified, and multiple current actual energy consumptions are read based on multiple prediction times, wherein the current predicted energy consumption corresponds one-to-one with the current actual energy consumption.

[0146] Multiple prediction error values ​​are constructed based on multiple current predicted energy consumption and multiple current actual energy consumption;

[0147] Multiple prediction error values ​​are matched with multiple current predicted energy consumption values ​​to obtain multiple training sample data, wherein the training sample data includes prediction error values ​​and current predicted energy consumption.

[0148] The adjustable network weight set of the general energy consumption prediction model was identified. The adjustable network weight set in the energy consumption prediction model was fine-tuned using multiple training sample data to obtain an adaptive energy consumption prediction model.

[0149] It should be explained that the general energy consumption prediction model refers to a neural network model built by relevant researchers to predict the overall energy consumption of a set of household electrical appliances. This model has good generalization ability but is not optimized for specific household environments. Therefore, subsequent transfer learning is needed based on specific household conditions. The training method for this general energy consumption prediction model is as follows: relevant researchers obtain equipment operation datasets and energy consumption datasets from multiple households through experiments. The energy consumption datasets consist of the overall energy consumption of different households in different periods. The neural network model is then trained based on these equipment operation datasets and energy consumption datasets to obtain the general energy consumption prediction model. The neural network model used for training can be an LSTM (Long Short-Term Memory) network, a GRU (Gated Recurrent Unit) model, or similar models.

[0150] Furthermore, the transfer learning cycle refers to a manually set data collection cycle for transferring learning the general energy consumption prediction model, typically one to two days. The current household dataset refers to the running dataset corresponding to the set of household electrical appliances collected within the transfer learning cycle. The current predicted energy consumption refers to the predicted value output by the general energy consumption prediction model based on a given current household dataset. The prediction time refers to the time when the current predicted energy consumption is predicted, where each prediction time corresponds one-to-one with the current predicted energy consumption. For example, if the current predicted energy consumption is the predicted energy consumption at time A, then time A is the prediction time for the current predicted energy consumption. The current actual energy consumption refers to the total energy consumption recorded for the set of household electrical appliances at the prediction time, which can be directly read from the gateway device. The prediction error value refers to the absolute difference between the current predicted energy consumption and the current actual energy consumption at the same prediction time. The training sample data refers to the combination of the prediction error value and the corresponding current predicted energy consumption.

[0151] Understandably, the adjustable network weight set refers to the set of adjustable parameters in a neural network that is set manually. The fine-tuning of the adjustable network weight set in the energy consumption prediction model using multiple training sample data means: adjusting the above-mentioned adjustable network weight set according to multiple training sample data through the backpropagation algorithm to minimize the prediction error.

[0152] S8. Based on the preset current mode and predicted power consumption, switch the mode of the household electrical equipment set to obtain the target electrical equipment set, wherein the current mode is either energy storage mode or power consumption mode.

[0153] Understandably, the target set of electrical devices refers to the set of household electrical devices after mode switching. Both the energy storage mode and the power consumption mode refer to the artificially constructed operating rules of the set of household electrical devices under different conditions. In energy storage mode, some unnecessary devices in the set of household electrical devices will be turned off (e.g., air conditioners, lighting systems, etc.), and only necessary devices will operate at minimum power consumption, such as allowing the ventilation system to operate at minimum power consumption. Different household electrical devices correspond to different minimum power consumption levels. In this energy storage mode, the renewable energy storage module will locally store its own generated power for later use. In power consumption mode, all household devices will continue to operate; for example, the central air conditioner will be turned on at its rated power, and the lighting system will be turned on. The renewable energy storage module will release the stored electricity to share the grid load. The execution rules of the above energy storage mode and power consumption mode will be written by relevant personnel using scripts to implement the corresponding rules of the above modes, and this code will be embedded in each household electrical device. This allows the household electrical device to execute the corresponding mode rules through its own code as soon as it receives a mode switching command.

[0154] Specifically, the step of switching the mode of the household electrical equipment set according to the preset current mode and predicted energy consumption to obtain the target electrical equipment set includes:

[0155] Generate mode switching instructions based on predicted energy consumption and current mode;

[0156] Using a pre-built central control unit and data transmission channel, mode switching commands are broadcast to a set of household electrical appliances and a pre-built renewable energy storage module to obtain a target set of electrical appliances, which includes a renewable energy storage module.

[0157] As is clear, the renewable energy storage module refers to a device capable of converting and storing renewable energy sources (such as solar and wind power) into electrical energy, for example, a home solar energy storage system. The mode switching command refers to a command used to instruct household appliances to switch operating modes. The central control unit refers to the core processing device of the home energy management system, responsible for command distribution and device coordination. When a target appliance receives a mode switching command, it will switch its mode to the mode specified in the command. The target appliance set refers to the collection of all household appliances and renewable energy storage modules that have received the mode switching command.

[0158] Specifically, the generation of mode switching instructions based on predicted energy consumption and the current mode includes:

[0159] If the predicted power consumption is less than the preset low energy consumption threshold, then determine whether the current mode is energy storage mode.

[0160] If the current mode is energy storage mode, then a continuous command is generated; if the current mode is not energy storage mode, then an energy storage mode command is generated.

[0161] The continuous command or energy storage mode command shall be recorded as the mode switching command;

[0162] Otherwise, generate a continuous command or a power consumption mode command, and record the continuous command or power consumption mode command as a mode switching command.

[0163] It is clear that the low energy consumption threshold refers to a manually set constant used to distinguish whether the current predicted energy consumption is low. When the predicted energy consumption is less than the low energy consumption threshold, it indicates that the area where the household appliances are located is uninhabited. The continuous instruction refers to the instruction to keep the household appliances in their current mode. The energy storage mode instruction refers to the instruction to switch the household appliances to energy storage mode. The power consumption mode instruction refers to the instruction to switch the household appliances to power consumption mode. The method for generating the continuous instruction or the power consumption mode instruction is the same as the method for generating the continuous instruction and the energy storage mode instruction.

[0164] S9. Divide the household into multiple areas, perform personalized management of members in multiple household areas based on the target set of electrical equipment, obtain the set of adjustable electrical equipment, and complete the intelligent household power optimization and control based on energy management based on the set of adjustable electrical equipment.

[0165] It is clear that the multiple home areas refer to the multiple areas where the target set of electrical appliances is located, including, for example, bedrooms, balconies, and kitchens. The adjusted set of electrical appliances refers to the target set of electrical appliances after personalized management.

[0166] Furthermore, since different family members have different habits, the target electrical equipment sets in different areas also need to be adjusted in a personalized way according to different family members.

[0167] Specifically, the method of personalized management of multiple household areas based on a target set of electrical appliances to obtain a set of regulated electrical appliances includes:

[0168] Extract family regions sequentially from multiple family regions;

[0169] Family members are identified within the family area to obtain a family member ID table, which includes multiple family member IDs.

[0170] Extract family member IDs sequentially from the family member ID table, and extract the target member preference vector from the pre-built, pre-configured member preference vector table based on the family member ID;

[0171] The target member preference vectors are aggregated to obtain multiple target member preference vectors. Vector averaging is then performed on these multiple target member preference vectors to obtain an average preference vector. The average preference vector includes one or more of the following: temperature preference value, light preference value, and humidity preference value.

[0172] The target electrical equipment set identifies the regional electrical equipment set of the household area, wherein the regional electrical equipment set includes multiple regional electrical equipment;

[0173] The set of adjustable electrical equipment in the regional electrical equipment cluster is identified based on the average preference vector;

[0174] Adjust each adjustable electrical device in the adjustable electrical equipment set according to the average preference vector to obtain a single-area electrical equipment set;

[0175] By summarizing the sets of electrical devices used in each individual area of ​​a household, a set of adjustable electrical devices can be obtained.

[0176] It should be explained that the aforementioned family member ID table refers to a table composed of multiple family member IDs, which is stored in the central control unit. The family member ID refers to the binary code of a family member stored in the central control unit. The family member ID table is obtained by family members uploading personal information, such as nickname, age, preferences, etc., through a relevant APP (this APP is used to control the central control unit and can monitor the operating data of various household electrical devices in real time). After this information is uploaded to the central control unit, the central control unit will construct an ID code for each nickname, i.e., the family member ID.

[0177] Furthermore, the pre-configured member preference vector table refers to a table of multiple member preference vectors. Each member preference vector corresponds to a family member ID, and the vector represents the corresponding family member's preferences regarding the use of household electrical appliances. This vector includes: air conditioning comfort temperature (winter), air conditioning comfort temperature (summer), shading level (winter), shading level (summer), ventilation level (summer), etc. This vector is manually entered by a user through the aforementioned app. For example, a family member might enter the following preference vector: [Air conditioning comfort temperature (winter): 20 degrees Celsius, Air conditioning comfort temperature (summer): 16 degrees Celsius, Shading level (winter): 3, Shading level (summer): 4, Ventilation level (summer): 5]. The temperature preference value, light preference value, and humidity preference value refer to the possible vector elements in the average preference vector. They represent the temperature, light, and humidity values ​​that should be set for the current household area, respectively. These temperature, light, and humidity values ​​are controlled by relevant devices. In addition, the average preference vector may also include shading preference values, etc. The target member preference vector refers to the member preference vector corresponding to the family member ID. The average preference vector refers to a vector that has undergone vector averaging, where vector averaging refers to the operation of averaging the corresponding positions of different vectors, and the non-integer parts of the averaged vector are approximately positive integers. For example, the averaged vector is ( ), where 1.5 is the non-integer part, which is approximated as a positive integer (using rounding or other approximation methods) 2, then the approximated vector is ( The term "regional electrical equipment set" refers to a collection of multiple target electrical devices within a household area. The term "adjustable electrical equipment set" refers to a collection of multiple regional electrical devices that can be adjusted using an average preference vector. For example, a refrigerator cannot be automatically adjusted, while a shading module (sunshade, etc.) can be automatically adjusted; thus, the shading module is considered an adjustable electrical device. The term "single-region electrical equipment set" refers to the adjusted set of adjustable electrical devices. Adjusting each adjustable electrical device in the adjustable electrical equipment set according to the average preference vector means controlling the parameters of the adjustable electrical device corresponding to the value in the average preference vector. For example, if an average preference vector contains: air conditioner temperature 24 degrees Celsius, shading level 3, then the average preference vector contains a temperature preference value of 24 degrees Celsius and a shading preference value of level 3. Therefore, the air conditioner temperature in the household area is set to 24 degrees Celsius. If a shading module exists in the household area, the shading level of the shading module is set to 3 (different levels correspond to different parameters such as the opening angle of the shading panel, and these parameters are built into the device's processing center).

[0178] To address the problems described in the background, this invention first generates query commands based on transmission protocols and gateway devices. These commands are then broadcast to all household electrical devices in a centralized household electrical device set, resulting in a query device set. This step utilizes the gateway to poll and broadcast the query commands across different protocols, completing an online inventory and protocol capability survey of the entire network in one go. This rapidly collects the sample data required for subsequent quality assessment, laying a real-time and complete foundation for dynamic routing. Next, transmission quality is assessed based on the query device set to obtain protocol transmission quality. This step involves devices transmitting signals in a loop according to a pre-set script, with the same strength, frequency, and interval. By unifying control variables, the three-dimensional indicators of delay, attenuation, and packet loss are accurately measured, quantifying the quality of protocols into comparable single quality values. This ensures the stability of subsequent electrical device adjustments. Furthermore, based on transmission quality thresholds, the transmission quality of multiple protocols is judged for compliance, yielding compliance results. This step uses a unified threshold to assess the quality of multiple protocols. The system identifies and filters out all non-compliant links to eliminate the risk of control commands failing due to protocol instability. When all mainstream protocols fail, it falls back to the Bluetooth Mesh backup channel, ensuring that optimized commands can still reach the device. This allows the solution to remain connected and uncontrolled even in extreme environments with strong interference or protocol incompatibility. The device's operating dataset is input into an adaptive energy consumption prediction model to obtain predicted energy consumption. This step uses transfer learning to fine-tune the general energy consumption model into a personalized predictor for different households, allowing the prediction results to continuously converge with changes in household routines and seasons, achieving hourly lead time and kilowatt-hour accuracy in energy consumption forecasting. Finally, based on the target set of electrical devices, personalized management of multiple household areas is performed to obtain a set of adjustable electrical devices. This step integrates family member identities and average preference vectors at the room level to perform micro-environmental adaptive adjustment of adjustable devices, ensuring the comfort of different family members and improving the intelligence level of household electrical device control. Therefore, this invention can improve the stability of data transmission for household electrical devices and enhance the intelligence level of household electrical device control.

[0179] like Figure 2 The diagram shown is a functional block diagram of a home intelligent power consumption optimization and control system based on energy management, provided in an embodiment of the present invention.

[0180] The energy management-based smart home electricity optimization and control system 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the energy management-based smart home electricity optimization and control system 100 may include an electrical device query module 101, a transmission quality assessment module 102, a transmission channel construction module 103, and an electrical device management module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0181] The electrical equipment query module 101 is used to receive an electricity optimization instruction, identify a set of household electrical equipment based on the electricity optimization instruction, wherein the set of household electrical equipment includes multiple household electrical equipment, extracts the transmission protocol sequentially from multiple preset transmission protocols, generates a query instruction based on the transmission protocol and a pre-built gateway device, and broadcasts the query instruction to all household electrical equipment in the set of household electrical equipment to obtain the query equipment set;

[0182] The transmission quality assessment module 102 is used to perform transmission quality assessment based on the query device set, obtain the protocol transmission quality, summarize the protocol transmission quality, and obtain multiple protocol transmission qualities.

[0183] The transmission channel construction module 103 is used to judge the passability of multiple protocol transmission quality based on a preset transmission quality threshold and obtain a passability result, wherein the passability result is passable or unpassable. If the passability result is passable, the optimal transmission quality is identified among the multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol. If the passability result is unpassable, the pre-constructed Bluetooth channel is started, and the started Bluetooth channel is recorded as the data transmission channel.

[0184] The electrical equipment management module 104 is used to collect equipment operation datasets based on the data transmission channel and the household electrical equipment set, input the equipment operation datasets into a pre-built adaptive energy consumption prediction model to obtain predicted energy consumption, switch the mode of the household electrical equipment set according to the preset current mode and predicted energy consumption to obtain the target electrical equipment set, wherein the current mode is either energy storage mode or electricity consumption mode, divide multiple household areas, and perform personalized management of members in multiple household areas based on the target electrical equipment set to obtain the adjustable electrical equipment set.

[0185] In detail, the modules in the energy management-based home smart electricity optimization and control system 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The technology used is the same as that of the home intelligent power optimization and control system based on energy management described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0186] like Figure 3 The diagram shown is a structural schematic of an electronic device for implementing a home intelligent power consumption optimization and control system based on energy management, according to an embodiment of the present invention.

[0187] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a home smart electricity optimization and control system program based on energy management.

[0188] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a home intelligent power optimization and control system program based on energy management, but also to temporarily store data that has been output or will be output.

[0189] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a home intelligent power optimization and control system program based on energy management) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0190] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0191] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0192] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management system, thereby enabling functions such as charging management, discharging management, and power consumption management through the power management system. The power supply may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0193] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0194] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0195] The program for a smart home electricity optimization and control system based on energy management, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0196] Receive power consumption optimization instructions, and identify a set of household electrical appliances based on the power consumption optimization instructions. The set of household electrical appliances includes multiple household electrical appliances.

[0197] The transmission protocols are extracted sequentially from multiple preset transmission protocols. Based on the transmission protocols and the pre-built gateway devices, a query command is generated and broadcast to all household electrical devices in the household electrical device set to obtain the query device set.

[0198] Based on the query device set, the transmission quality is evaluated to obtain the protocol transmission quality. The protocol transmission quality is then summarized to obtain the transmission quality of multiple protocols.

[0199] The transmission quality of multiple protocols is judged based on a preset transmission quality threshold to obtain a pass / fail result, which is either pass or fail.

[0200] If the qualification result is qualified, the optimal transmission quality is identified among multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol.

[0201] If the pass / fail result is unqualified, the pre-built Bluetooth channel will be started and the started Bluetooth channel will be recorded as the data transmission channel;

[0202] Based on the data transmission channel and the collection of household electrical equipment data sets, the data sets are input into a pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption.

[0203] Based on the preset current mode and predicted power consumption, the mode of the household electrical equipment set is switched to obtain the target electrical equipment set, wherein the current mode is either energy storage mode or power consumption mode.

[0204] The system divides the household into multiple areas, performs personalized management of members in each area based on a target set of electrical devices, obtains a set of adjustable electrical devices, and completes intelligent home power optimization and control based on energy management based on this set of adjustable electrical devices.

[0205] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0206] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0207] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0208] Receive power consumption optimization instructions, and identify a set of household electrical appliances based on the power consumption optimization instructions. The set of household electrical appliances includes multiple household electrical appliances.

[0209] The transmission protocols are extracted sequentially from multiple preset transmission protocols. Based on the transmission protocols and the pre-built gateway devices, a query command is generated and broadcast to all household electrical devices in the household electrical device set to obtain the query device set.

[0210] Based on the query device set, the transmission quality is evaluated to obtain the protocol transmission quality. The protocol transmission quality is then summarized to obtain the transmission quality of multiple protocols.

[0211] The transmission quality of multiple protocols is judged based on a preset transmission quality threshold to obtain a pass / fail result, which is either pass or fail.

[0212] If the qualification result is qualified, the optimal transmission quality is identified among multiple protocol transmission quality, and the transmission protocol corresponding to the optimal transmission quality is recorded as the target protocol. A data transmission channel is constructed based on the target protocol.

[0213] If the pass / fail result is unqualified, the pre-built Bluetooth channel will be started and the started Bluetooth channel will be recorded as the data transmission channel;

[0214] Based on the data transmission channel and the collection of household electrical equipment data sets, the data sets are input into a pre-built adaptive energy consumption prediction model to obtain the predicted energy consumption.

[0215] Based on the preset current mode and predicted power consumption, the mode of the household electrical equipment set is switched to obtain the target electrical equipment set, wherein the current mode is either energy storage mode or power consumption mode.

[0216] The system divides the household into multiple areas, performs personalized management of members in each area based on a target set of electrical devices, obtains a set of adjustable electrical devices, and completes intelligent home power optimization and control based on energy management based on this set of adjustable electrical devices.

[0217] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0218] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0219] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0220] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0221] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A home intelligent power optimization and regulation system based on energy management, characterized in that, The system comprises: receiving power optimization instructions, confirming a set of household power devices based on the power optimization instructions, wherein the set of household power devices comprises a plurality of household power devices; extracting a transmission protocol in a plurality of preset transmission protocols in turn, generating a query instruction based on the transmission protocol and a pre-constructed gateway device, broadcasting the query instruction to all household power devices in the set of household power devices, and obtaining a set of queried devices; based on the set of queried devices, performing transmission quality evaluation to obtain protocol transmission quality, and summarizing the protocol transmission quality to obtain a plurality of protocol transmission qualities; based on a preset transmission quality threshold, judging the eligibility of the plurality of protocol transmission qualities to obtain an eligibility result, wherein the eligibility result is eligible or ineligible; if the eligibility result is eligible, identifying the optimal transmission quality in the plurality of protocol transmission qualities, recording the transmission protocol corresponding to the optimal transmission quality as a target protocol, and constructing a data transmission channel based on the target protocol; if the eligibility result is ineligible, starting a pre-constructed Bluetooth channel, and recording the started Bluetooth channel as the data transmission channel; based on the data transmission channel and the set of household power devices, collecting a set of device operation data, inputting the set of device operation data into a pre-constructed adaptive energy consumption prediction model, and obtaining a predicted power consumption; based on a preset current mode and the predicted power consumption, performing mode switching on the set of household power devices to obtain a target set of power devices, wherein the current mode is a energy storage mode or a power consumption mode; dividing a plurality of household areas, performing member individualized management on the plurality of household areas based on the target set of power devices, obtaining an adjusted set of power devices, and completing energy management-based household intelligent power optimization regulation based on the adjusted set of power devices.

2. The energy source management based home smart power consumption optimization regulating system according to claim 1, wherein, The transmission quality evaluation based on the set of queried devices to obtain the protocol transmission quality comprises: for each queried device in the set of queried devices, performing the following operations: confirming a preconfigured command of the queried device, activating the preconfigured command based on the query instruction to obtain an activated command; based on the activated command and the queried device, performing multiple signal transmissions on the gateway device to obtain a set of received signals, wherein the set of received signals comprises a plurality of received signals, and each received signal comprises a generated timestamp, a household power device ID, and a returned signal; based on the set of received signals, performing signal quality evaluation to obtain signal quality; summarizing the signal quality corresponding to the queried device to obtain a set of signal qualities; based on the set of signal qualities, calculating the protocol transmission quality.

3. The energy source management based home smart power consumption optimization regulating system according to claim 2, wherein, The system further comprises: designing a signal return script, wherein the signal return script is used to perform the step of multiple signal transmissions, and the signal return script contains an activated interface, wherein the signal return script sets signal strength, transmission times, transmission intervals, and device ID vacancies; constructing an instruction monitoring script, wherein the instruction monitoring script is used to determine whether the query instruction is received; embedding the instruction monitoring script into the activated interface in the signal return script to obtain a control command template; confirming a household power device ID corresponding to the queried device; Embedding the control command template into the household electrical equipment corresponding to the query device based on the household electrical equipment ID, to obtain a pre-configuration command, wherein when the control command template is completed, the equipment ID position in the control command template is replaced by the household electrical equipment ID.

4. The energy source management based home smart power consumption optimization regulating system of claim 3, wherein, The gateway device is transmitted multiple times based on the activation command and the query device, to obtain a set of received signals, including: Confirming the configuration signal strength, the configuration transmission interval and the configuration transmission times based on the activation command; Generating a return signal according to the preset transmission time, the query device and the configuration signal strength, and recording the generation timestamp of the return signal; Transmitting the generation timestamp, the household electrical equipment ID corresponding to the query device and the return signal to the gateway device to obtain a received signal, wherein the received signal contains the generation timestamp, the household electrical equipment ID and the return signal; Recording the number of signal generations; Calculating the repeated transmission time according to the generation timestamp and the configuration transmission interval; Taking the repeated transmission time as the transmission time, and returning to the step of generating a return signal according to the preset transmission time, the query device and the configuration signal strength, until the number of signal generations is equal to the configuration transmission times; Summarizing the received signals to obtain a set of received signals.

5. The energy management based home smart power optimization regulating system of claim 4, wherein, The signal quality is evaluated according to the set of received signals, including: Extracting the received signals in the set of received signals in turn; Confirming the transmission timestamp and the reception timestamp of the received signal, and calculating the signal transmission delay based on the transmission timestamp and the reception timestamp; Identifying the received signal strength of the received signal, and calculating the signal attenuation value based on the received signal strength and the configuration signal strength; Respectively summarizing the signal transmission delay and the signal attenuation value to obtain a set of signal transmission delays and a set of signal attenuation values; Respectively calculating the average transmission delay and the average attenuation value by averaging the set of signal transmission delays and the set of signal attenuation values; Statistically calculating the signal loss rate based on the number of received signals and the configuration transmission times; Calculating the signal quality according to the signal loss rate, the average transmission delay and the average attenuation value.

6. The energy management based home smart power optimization regulating system of claim 5, wherein, Before the device running data set is input into the pre-constructed adaptive energy consumption prediction model to obtain the predicted energy consumption, the system further includes: Reading a general energy consumption prediction model; Periodically collecting a plurality of current household data sets according to a preset migration learning period, to obtain a plurality of current household data sets; Inputting the plurality of current household data sets into the general energy consumption prediction model to obtain a plurality of current predicted energy consumptions; Confirming a plurality of prediction time points of the plurality of current predicted energy consumptions, and reading a plurality of current real energy consumptions based on the plurality of prediction time points, wherein the current predicted energy consumption and the current real energy consumption correspond one by one; Constructing a plurality of prediction error values based on the plurality of current predicted energy consumptions and the plurality of current real energy consumptions; Matching the plurality of prediction error values with the plurality of current predicted energy consumptions to obtain a plurality of training sample data, wherein the training sample data includes the prediction error value and the current predicted energy consumption; Confirming the adjustable network weight set of the general energy consumption prediction model, and fine-tuning the adjustable network weight set in the energy consumption prediction model using the plurality of training sample data to obtain an adaptive energy consumption prediction model.

7. The energy management based home smart power optimization regulating system of claim 6, wherein, The mode switching of the household electrical equipment set according to the preset current mode and the predicted power consumption obtains a target electrical equipment set, and the mode switching comprises: generating a mode switching instruction based on the predicted power consumption and the current mode; broadcasting the mode switching instruction to the household electrical equipment set and the pre-constructed renewable energy storage module by using the pre-constructed central control unit and data transmission channel to obtain the target electrical equipment set, wherein the target electrical equipment set comprises the renewable energy storage module.

8. The energy management based home smart power optimization regulating system of claim 7, wherein, The mode switching instruction is generated based on the predicted power consumption and the current mode, and the mode switching instruction comprises: if the predicted power consumption is less than a preset low energy consumption threshold, determining whether the current mode is a storage mode; if the current mode is the storage mode, generating a continuous instruction, and if the current mode is not the storage mode, generating a storage mode instruction; the continuous instruction or the storage mode instruction is recorded as the mode switching instruction; otherwise, generating a continuous instruction or an electrical mode instruction, and recording the continuous instruction or the electrical mode instruction as the mode switching instruction.

9. The energy management based home smart power optimization regulating system of claim 8, wherein, The target electrical equipment set is used to perform member individualization management on multiple household areas to obtain an adjusted electrical equipment set, and the method comprises: extracting household areas in sequence in the multiple household areas; performing household member identification on the household areas to obtain a household member ID table, wherein the household member ID table comprises multiple household member IDs; extracting household member IDs in sequence in the household member ID table, and extracting a target member preference vector in a pre-constructed pre-configured member preference vector table based on the household member ID; summarizing the target member preference vector to obtain multiple target member preference vectors, performing vector averaging based on the multiple target member preference vectors to obtain an average preference vector, wherein the average preference vector comprises one or more of a temperature preference value, a light preference value and a humidity preference value; confirming a regional electrical equipment set of the household area in the target electrical equipment set, wherein the regional electrical equipment set comprises multiple regional electrical equipment; confirming an adjustable electrical equipment set in the regional electrical equipment set based on the average preference vector; adjusting each adjustable electrical equipment in the adjustable electrical equipment set according to the average preference vector to obtain a single-area electrical equipment set; summarizing the single-area electrical equipment sets corresponding to the household areas to obtain the adjusted electrical equipment set.

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