Energy consumption optimization method and device, electronic equipment, readable storage medium and program product

Abstract

The invention relates to an energy consumption optimization method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring energy consumption monitoring data of a target space, and acquiring activity habit data of a target object in the target space; the energy consumption monitoring data comprises data acquired in the process of monitoring the energy consumption of each piece of Internet of Things equipment in the target space; the activity habit data is used for indicating activity habits of the target object; according to the activity habit data and the energy consumption monitoring data, detecting an invalid enabling event existing in the target space; a power saving action is performed that matches the invalidation enable event. By adopting the method, the intelligence of energy consumption management and control of the space can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of the Internet of Things, and in particular, to an energy consumption optimization method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] With the development of technologies such as computers, control, and communication, an intelligent space that integrates technologies such as computers, control, and communication with living or working spaces has emerged. How to efficiently manage and control the energy consumption of the intelligent space is a key concern for managers.

[0003] These intelligent spaces usually have problems in energy management and control, including a large amount of electrical appliance usage, mechanical management of electrical equipment, and a relatively common waste phenomenon; managers cannot comprehensively control the overall energy consumption in the intelligent space and cannot formulate reasonable energy control strategies.

[0004] Therefore, in the related technologies, there is a problem that the energy consumption management and control of intelligent spaces is not intelligent enough. Summary of the Invention

[0005] Based on this, in order to solve the above technical problems, it is necessary to provide an energy consumption optimization method, device, electronic device, computer-readable storage medium, and computer program product that can improve the intelligence of energy consumption management and control in a space.

[0006] In a first aspect, the present application provides an energy consumption optimization method, including:

[0007] Obtaining energy consumption monitoring data of a target space, and obtaining activity habit data of a target object in the target space; the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object;

[0008] Detecting invalid enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data;

[0009] Performing an energy-saving action matching the invalid enabling event.

[0010] In a second aspect, the present application further provides an energy consumption optimization device, including:

[0011] An obtaining module, configured to obtain energy consumption monitoring data of a target space, and obtain activity habit data of a target object in the target space; the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object;

[0012] A detection module, configured to detect an ineffective enabling event existing in the target space according to the activity habit data and the energy consumption monitoring data;

[0013] An execution module, configured to execute an energy-saving action matching the ineffective enabling event.

[0014] In a third aspect, the present application further provides an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0016] In a fifth aspect, the present application further provides a computer program product. The computer program product includes a computer program, and when the computer program is executed by the processor, the steps of the above method are implemented.

[0017] For the above energy consumption optimization method, device, electronic device, computer-readable storage medium and computer program product, by obtaining the energy consumption monitoring data of the target space, and obtaining the activity habit data of the target object in the target space; the energy consumption monitoring data includes the data collected during the energy consumption monitoring of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object; according to the activity habit data and the energy consumption monitoring data, detect the ineffective enabling events existing in the target space; execute the energy-saving actions matching the ineffective enabling events.

[0018] In this way, by obtaining the energy consumption monitoring data collected during the energy consumption monitoring of each Internet of Things device in the target space, the energy consumption situation of each Internet of Things device can be accurately obtained. By obtaining the activity habit data used to indicate the activity habits of the target object in the target space, during the process of analyzing the energy consumption monitoring data, combined with the activity habit data, it is possible to accurately detect the ineffective enabling events in the target space that consume electrical energy but do not achieve actual effects or do not bring actual value. Thus, by executing the energy-saving actions matching the ineffective enabling events, personalized energy-saving services are provided according to the activity habits of the target object, and the flexibility and intelligence of energy consumption optimization in the target space are improved. Moreover, this energy-saving service not only reduces the unnecessary energy consumption in the target space, but also does not require the user to manually execute or configure energy-saving operations, which can help the target space achieve more efficient and intelligent energy consumption control, reduce energy consumption costs, and effectively improve the intelligence of energy consumption control in the target space. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0020] Figure 1 It is an application environment diagram of an energy consumption optimization method in an embodiment;

[0021] Figure 2 It is a schematic flowchart of an energy consumption optimization method in an embodiment;

[0022] Figure 3 It is a schematic diagram of an energy consumption data calculation in an embodiment;

[0023] Figure 4 It is a schematic diagram of another energy consumption data calculation in an embodiment;

[0024] Figure 5 It is a schematic diagram of an energy consumption analysis page in an embodiment;

[0025] Figure 6 It is a schematic diagram of an energy consumption analysis page in another embodiment;

[0026] Figure 7 It is a schematic diagram of an energy consumption analysis page in yet another embodiment;

[0027] Figure 8 It is a schematic diagram of a second energy consumption reminder page in an embodiment;

[0028] Figure 9 It is a schematic diagram of an energy consumption analysis report generated by an artificial intelligence model in an embodiment;

[0029] Figure 10 It is a schematic diagram of converting a data packet in an embodiment;

[0030] Figure 11 It is a framework diagram of an energy consumption optimization method in an embodiment;

[0031] Figure 12 It is a schematic flowchart of an energy consumption optimization method in another embodiment;

[0032] Figure 13 It is a structural block diagram of an energy consumption optimization device in an embodiment;

[0033] Figure 14 It is an internal structure diagram of an electronic device in an embodiment. Detailed implementation manners

[0034] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0036] In one embodiment, Figure 1 it may be a schematic diagram of the implementation environment involved in the energy consumption optimization method. The implementation environment at least includes a user terminal 110, a smart device 130, a server end 170, and a network device. In Figure 1 this case, the network device includes a gateway 150 and a router 190, but this is not a specific limitation here.

[0037] Among them, the user terminal 110, which can also be regarded as the user side or the terminal, can deploy (or understand as install) the client associated with the smart device 130. This user terminal 110 can be an electronic device such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart control panel, or other devices with display and control functions, and is not limited here.

[0038] Among them, the client, which is associated with the smart device 130, essentially means that the user registers an account in the client and configures the smart device 130 in the client. For example, the configuration includes adding a device identifier to the smart device 130, etc., so that when the client runs in the user terminal 110, it can provide functions such as device display and device control for the user. This client can be in the form of an application program or in the form of a web page. Correspondingly, the interface for the client to display the device can be in the form of a program window or in the form of a web page, and this is not limited here either.

[0039] The intelligent device 130 is deployed in the gateway 150 and communicates with the gateway 150 through its own configured communication module, and thus is controlled by the gateway 150. It should be understood that the intelligent device 130 generally refers to one of multiple intelligent devices 130. The embodiments of the present application only take the intelligent device 130 as an example for illustration. That is, the embodiments of the present application do not limit the number and device type of the intelligent devices deployed in the gateway 150. In an application scenario, the intelligent device 130 accesses the gateway 150 through a local area network, so as to be deployed in the gateway 150. The process of the intelligent device 130 accessing the gateway 150 through the local area network includes: the gateway 150 first establishes a local area network, and the intelligent device 130 joins the local area network established by the gateway 150 by connecting to the gateway 150. This local area network includes but is not limited to: ZIGBEE or Bluetooth. Among them, the intelligent device 130 can be an intelligent printer, an intelligent fax machine, an intelligent camera, an intelligent air conditioner, an intelligent door lock, an intelligent light, or a human body sensor, a door and window sensor, a temperature and humidity sensor, a water immersion sensor, a natural gas alarm, a smoke alarm, a wall switch, a wall socket, a wireless switch, a wireless wall sticker switch, a magic cube controller, a curtain motor, a millimeter wave radar, etc. configured with a communication module. In actual applications, the gateway device and the intelligent device 130 can be connected through communication methods such as Bluetooth, WiFi, ZigBee, Matter, etc. Of course, the connection method between the gateway device and the intelligent device 130 can be not specifically limited in the embodiments of the present application.

[0040] The interaction between the user terminal 110 and the intelligent device 130 can be realized through a local area network or through a wide area network. In an application scenario, the user terminal 110 establishes a wired or wireless communication connection with the gateway 150 through the router 190. For example, the wired or wireless method includes but is not limited to WIFI, etc., so that the user terminal 110 and the gateway 150 are deployed in the same local area network, and thus the user terminal 110 can realize the interaction with the intelligent device 130 through the local area network path. In another application scenario, the user terminal 110 establishes a wired or wireless communication connection with the gateway 150 through the server 170. For example, the wired or wireless method includes but is not limited to 2G, 3G, 4G, 5G, WIFI, etc., so that the user terminal 110 and the gateway 150 are deployed in the same wide area network, and thus the user terminal 110 can realize the interaction with the intelligent device 130 through the wide area network path.

[0041] Among them, the server 170 can also be regarded as the cloud, cloud platform, platform side, server side, etc. This server 170 can be a single server, or a server cluster composed of multiple servers, or a cloud computing center composed of multiple servers, so as to better provide background services to a large number of user terminals 110. For example, the background services include energy consumption optimization services.

[0042] In an application scenario, the server 170 can obtain energy consumption monitoring data of the target space and obtain activity habit data of the target object in the target space; the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object; then, the server 170 can detect invalid enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data; thereby, execute energy-saving actions matching the invalid enabling events.

[0043] Of course, in other application scenarios, the above process of energy consumption analysis completed by the server 170 can also be implemented by the user terminal 110 or the gateway 150.

[0044] In one embodiment, as Figure 2 shown, an energy consumption optimization method is provided. In this embodiment, this method is exemplified by being applied to an electronic device. The electronic device can specifically be Figure 1 the user terminal 110, the server 170 or the gateway 150 in. In this embodiment, the method includes the following steps:

[0045] Step S210, obtain energy consumption monitoring data of the target space and obtain activity habit data of the target object in the target space.

[0046] Among them, the target space can be a space that needs to optimize energy consumption. The target space can be a relatively large open space, such as large spaces like office places, lecture halls, cinemas, etc., or a relatively small closed space, such as small spaces like the living room and bedroom in a house. Here, the size and shape of the target space are not limited, and the target space can be set according to the actual situation.

[0047] Among them, the target object refers to a living body. In this embodiment, the target object refers to a person who moves in the target space.

[0048] Among them, the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space.

[0049] In practical applications, IoT devices report a large amount of data at all times. This data includes heartbeat data packets, functional data, device state transitions, and environmental data, which can be cleaned out by a big data system from a vast amount of resource point value data for the required computational and statistical data information. Among them, the IoT device can be Figure 1 the intelligent device 130 in it. In this application, the data reported by the IoT device can be used as the data collected during the energy consumption monitoring of IoT devices in the target space.

[0050] Among them, IoT devices have various data reporting mechanisms, such as reporting data according to device state transitions, heartbeat mechanisms, numerical changes exceeding thresholds, etc.

[0051] Among them, IoT devices periodically report their own data to the gateway and then to the server side. This part of the data can be called heartbeat data packets.

[0052] Among them, each IoT device can have resource value definitions for start and end (this definition is maintained in the lowest-level device function definition logic). IoT devices report resource value data in multiple states. For example, when the device state changes, from the on state to the off state, or from the off state to the on state, or the device periodically reports its own state via heartbeat. The device will report this data to the server side through the gateway. The big data system can parse the data based on the cloud to obtain energy consumption monitoring data.

[0053] Among them, for the resource value definitions of start and end, specifically, it means that IoT devices have corresponding definition rules for their various states and functions. For example, the value of the resource value corresponding to the on / off state of a switch is an enumerated value, which is 0 and 1, representing off and on respectively. When the IoT device is in the on state, it reports 1, and when the device changes to the off state, it reports 0. This can be a kind of definition of the resource value.

[0054] Among them, IoT devices can include Internet of Things intelligent devices. Internet of Things intelligent devices refer to various devices that are connected to the Internet and equipped with various sensors and / or actuators, and can collect, exchange, process, and execute operations. These devices use their built-in sensors and / or actuators to interact with users, other devices, and the environment, providing users with a more convenient and intelligent lifestyle. In this application, IoT devices can include Figure 1 the intelligent devices in it.

[0055] In some embodiments, the energy consumption monitoring data can include at least one of the device state data of each IoT device and the environmental data of the location where the IoT device is located.

[0056] Among them, the device status data of the IoT device can characterize the operating condition of the IoT device at a specific time point. Exemplarily, the device status data of the IoT device can include the operating status of the device, the corresponding timestamp data, location information, etc.

[0057] Among them, the operating status refers to various working conditions and characteristics presented by the IoT device during operation, which can include multiple aspects such as whether the device is running, the mode of operation, the operating parameters, and the interaction with the external environment and other devices. For example, the operating status of a TV can include the on state and the off state; the operating status of a smart bulb can include the on state and the off state; the operating status of a smart door lock can include the unlocked state and the locked state; the operating status of a human presence sensor can include the detection state (occupied or unoccupied) and the working mode state (normal working mode or sleep mode).

[0058] Among them, the environmental data can include, but is not limited to, temperature and humidity data, light intensity data, etc.

[0059] Among them, the activity habit data is used to indicate the activity habits of the target object. For example, information such as wake-up time and bedtime, activity periods, device usage frequency, access information, and activity trajectories.

[0060] In a specific implementation, the electronic device can obtain the energy consumption monitoring data of the target space and obtain the activity habit data of the target object in the target space.

[0061] Step S220, detect the invalid enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data.

[0062] Among them, the invalid enabling event refers to an event in which the IoT device consumes electrical energy but does not achieve an actual effect or does not bring actual value. For example, when the family members in the room are in a sleep state, but IoT devices such as TVs and the main lights in the room are still in a power-consuming state; for another example, when the family members in the room go out, but devices such as TVs and air conditioners are still in a power-consuming state for a long time.

[0063] In a specific implementation, the electronic device can detect the invalid enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data.

[0064] Specifically, the electronic device can determine the energy consumption data of the IoT device that the target object does not need to use at the current time according to the activity habit data and the energy consumption monitoring data. If the energy consumption data is greater than the preset energy consumption threshold, it is determined that there is an invalid enabling event in the target space.

[0065] For example, if the activity habit data of the target object indicates that during the daytime on weekdays, all family members are not at home, then the air conditioner or TV does not need to be used. If the current time belongs to the daytime on weekdays and the energy consumption data of the air conditioner or TV is greater than the preset energy consumption threshold and the duration exceeds a certain threshold (e.g., 5 minutes), it can be determined as an invalid enabling event. Another example, if the activity habit data of the target object indicates the washing and bathing time of family members (e.g., from 7:00 to 8:00 and from 20:00 to 22:00 in the morning), and if the current time does not belong to the washing and bathing time of family members (such as 1:00 in the early morning) and the energy consumption data of the water heater is greater than the preset energy consumption threshold, it can be determined as an invalid enabling event. Still another example, if the activity habit data of the target object indicates the sleeping time of family members (such as 22:00 - 7:00), and if the current time belongs to the sleeping time of family members (such as 2:00 at the current time) and the energy consumption data of the TV and the main light is greater than the preset energy consumption threshold, it can be determined as an invalid enabling event.

[0066] Step S230, perform an energy-saving action matching the invalid enabling event.

[0067] Among them, the energy-saving action is an action to reduce the energy consumption of the target space.

[0068] In specific implementation, when it is detected that there is an invalid enabling in the target space, an energy-saving action matching the invalid enabling event can be performed, such as turning off the relevant Internet of Things devices and reducing the power of the relevant Internet of Things devices.

[0069] For example, continuing with the above example, if it is detected that there is an invalid enabling event for the air conditioner, the air conditioner can be adjusted to a standby mode with a lower power to reduce energy consumption; if it is detected that there is an invalid enabling event for the water heater, the water heater can be turned off.

[0070] In the above energy consumption optimization method, by obtaining the energy consumption monitoring data of the target space and obtaining the activity habit data of the target object in the target space; the energy consumption monitoring data includes the data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object; according to the activity habit data and the energy consumption monitoring data, detect the invalid enabling events existing in the target space; perform an energy-saving action matching the invalid enabling event.

[0071] In this way, by obtaining the energy consumption monitoring data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space, the energy consumption situation of each Internet of Things device can be accurately obtained. By obtaining the activity habit data used to indicate the activity habits of the target object in the target space, during the process of analyzing the energy consumption monitoring data, combined with the activity habit data, it is possible to accurately detect ineffective enabling events in the target space that consume electrical energy but do not achieve actual effects or bring actual value. Thus, by executing energy-saving actions matching the ineffective enabling events, personalized energy-saving services are provided according to the activity habits of the target object, improving the flexibility and intelligence of energy consumption optimization in the target space. Moreover, this kind of energy-saving service not only reduces unnecessary energy consumption in the target space, but also eliminates the need for users to manually execute or configure energy-saving operations, which can help the target space achieve more efficient and intelligent energy consumption control, reduce energy consumption costs, and effectively improve the intelligence of energy consumption control in the target space.

[0072] In one embodiment, obtaining the activity habit data of the target object in the target space includes: obtaining the historical activity data of the target object in the target space; performing big data analysis on the historical activity data to obtain the activity habit data.

[0073] Among them, the historical activity data is used to indicate the activities of the target object in the target space during the historical time period, such as activities like sleeping, going out, and being at home. For example, target object A goes out at 19:00 in the evening. At this time, the intelligent door lock and the human presence sensor will generate corresponding device logs. The device log of the intelligent door lock at least includes the target object (target object A), device identifier (intelligent door lock), operating state (unlocked state), time of going out (19:00 in the evening), etc.; the device log of the human presence sensor at least includes the target object (target object A), device identifier (human presence sensor), operating state (no one detected), time (19:00 in the evening).

[0074] Target object A returns home at 20:00 in the evening. At this time, the intelligent door lock and the human presence sensor will generate corresponding device logs. The device log of the intelligent door lock at least includes the target object (target object A), device identifier (intelligent door lock), operating state (locked state), time (20:00 in the evening), etc.; the device log of the human presence sensor at least includes the target object (target object A), device identifier (human presence sensor), operating state (someone detected), time (20:00 in the evening).

[0075] The sleep detection device detects that target object B is in a sleep state and generates a corresponding device log, which at least includes the target object (target object B), device identifier (sleep detection device), time (such as 22:00 - 7:00), and operating state (sleep state detected).

[0076] In some embodiments, the historical activity data may further include data on the historical behavior of the target object in controlling the IoT device. For example, the target object A takes a bath using the water heater at 20:30 in the evening. At this time, the water heater will generate corresponding device logs, which at least include the target object (target object A), the device identifier (device identifier), the operating status (on status), the usage time (20:30 in the evening), etc.

[0077] Thus, these device logs can be uploaded to the background for storage. Taking Figure 1 the illustrated implementation environment as an example, in one application scenario, the above device logs are reported to the gateway device through the local area network path and forwarded by the gateway device to the server side. In another application scenario, the above device logs are reported to the server side through the wide area network path. Based on this, for the server side, after receiving the above device logs, it can store the received device logs into the historical activity data of the corresponding target object.

[0078] Thus, big data analysis can be performed on the historical activity data of the target object to obtain the activity habit data of the target object.

[0079] The technical solution of this embodiment is to obtain the historical activity data of the target object in the target space; the historical activity data is used to indicate the activities of the target object in the target space during the historical time period; perform big data analysis on the historical activity data to obtain the activity habit data. Thus, through the historical activity data used to indicate the activities of the target object in the target space during the historical time period, by performing big data analysis on the historical activity data, the activity habit data used to indicate the activity habits of the target object in the target space can be accurately obtained.

[0080] In one embodiment, performing big data analysis on the historical activity data to obtain the activity habit data includes: extracting features from the historical activity data to obtain historical activity features; performing activity habit mining based on the historical activity features to obtain the activity habit data.

[0081] Among them, the historical activity features include at least one of the activity time, the location of the target object, and the operating status of the IoT device.

[0082] Among them, the location of the target object can be used to indicate whether the target object is in the target space. If it is in the target space, the location of the target object can further indicate the subspace where the target object is located in the target space.

[0083] Among them, the target space includes at least one subspace. For example, the target space can be a certain office building, and the subspaces can be the various offices in the office building.

[0084] In a specific implementation, when the electronic device performs big data analysis on historical activity data to obtain activity habit data, it can extract features from the historical activity data to obtain historical activity features; the historical activity features include at least one of the activity time, the location of the target object, and the operating state of the Internet of Things device. Specifically, the historical activity features can be extracted according to the device logs stored in the historical activity data of the target object.

[0085] In this way, activity habit mining can be performed based on the historical activity features to determine the rules and habits of the target object in terms of activities, and activity habit data can be obtained. Specifically, as can be seen from the above, as time accumulates, the number of historical activity data stored in the background also increases. These historical activity data describe the activities of the target object in the target space. Then, by extracting features from the historical activity data of the target object, historical activity features are obtained, and these historical activity features will be able to reflect the activity habits of the corresponding target object. For example, through the historical activity features corresponding to the historical activity data obtained based on the smart lock and the human presence sensor, it can be known that the target object has a relatively high frequency of not being at home from 19:00 to 20:00 every night, indicating that the target object has the habit of going out from 19:00 to 20:00 at night; through the historical activity features corresponding to the historical activity data obtained based on the water heater, it can be known that the target object uses the water heater to take a bath from 20:00 to 21:00 every night, indicating that the target object has the habit of taking a bath from 20:00 to 21:00 every night; through the historical activity features corresponding to the historical activity data obtained based on the sleep monitoring device, it can be known that the target object is in a sleep state from 22:00 to 7:00 every day, indicating that the target object has the habit of sleeping from 22:00 at night to 7:00 in the morning.

[0086] The technical solution of this embodiment extracts historical activity features by extracting features from historical activity data; the historical activity features include at least one of the activity time, the location of the target object, and the operating state of the Internet of Things device; activity habit mining is performed based on the historical activity features to obtain activity habit data. In this way, by extracting features and performing activity habit mining on these historical activity data, the personalized activity habits of the target object can be better understood.

[0087] In one embodiment, performing an energy-saving action that matches an invalid enable event includes: displaying a first energy consumption reminder page corresponding to the target space. The first energy consumption reminder page is used to receive a confirmation operation for enabling the automation solution; the automation solution is used to perform an energy-saving action that matches the invalid enable event.

[0088] Among them, the first energy consumption reminder page refers to an interactive page for reminding the user that there is an invalid enabling event in the target space. Exemplarily, the electronic device can remind the user in the first energy consumption reminder page that there is an invalid enabling event in the target space, and display in the first energy consumption reminder page an automation solution that matches the currently existing invalid enabling event, as well as a confirmation entry for receiving a confirmation operation to enable the automation solution. When a trigger operation on the confirmation entry is received, it is determined that the automation solution needs to be enabled, and in response to the trigger operation, the automation solution is enabled to perform an energy-saving action that matches the invalid enabling event.

[0089] In this embodiment, the automation solution is used to automatically perform an energy-saving action that matches the invalid enabling event, that is, the execution action in the automation solution in this embodiment is an energy-saving action for reducing the energy consumption of the target space. Specifically, the automation solution in this embodiment is an automation solution associated with the intelligent device in which there is an invalid enabling event in the target space, where the intelligent device with the invalid enabling event acts as a controlled device to perform the energy-saving action. For example, if it is recognized that there is an invalid enabling event in the TV in the target space (such as the TV is still on during the time when the target object is out), the automation solution can automatically control the TV to turn off; if it is recognized that there is an invalid enabling event in the water heater in the target space (such as the energy consumption data of the water heater is greater than the preset energy consumption threshold during the time when the target object is not taking a bath or washing), the automation solution can automatically control the water heater to turn off or control the water heater to switch to the low-power mode.

[0090] In this way, by displaying the first energy consumption reminder page corresponding to the target space, the user can clearly understand the invalid enabling situation in the target space and the corresponding automation solution for energy saving. The user can independently choose whether to enable the automation solution according to their own needs and actual situations, preventing safety problems caused by system misjudgment or improper execution of the automation solution, and improving safety and human-computer interaction intelligence.

[0091] In some other embodiments, in addition to the solution of allowing the user to confirm whether to perform the energy-saving action, the energy-saving action can also be actively performed. Specifically, after obtaining the automation solution associated with the currently existing invalid enabling event in the target space, a control instruction can be directly sent to the target IoT device, where the target IoT device is the IoT device in which there is an invalid enabling event in the target space, and the control instruction is used to instruct the target IoT device to perform the energy-saving action indicated by the automation solution. It can be understood that the automation solution in this embodiment can be the same as the automation solution displayed in the first energy consumption reminder page in the above embodiment.

[0092] In this way, when an invalid enabling event is detected in the target space, by obtaining the automation solution associated with the invalid enabling event, the IoT devices with the invalid enabling event are actively controlled to perform energy-saving actions, improving the convenience and timeliness of energy consumption management.

[0093] In one embodiment, the target space includes at least one subspace, and the method further includes: displaying an energy consumption analysis page corresponding to the target space; and displaying total energy consumption data and / or energy-saving data in the energy consumption analysis page according to different energy consumption analysis dimensions.

[0094] Among them, the energy consumption analysis page is a front-end page for displaying the energy consumption analysis results.

[0095] Among them, the energy consumption analysis results include displaying total energy consumption data and / or energy-saving data according to different energy consumption analysis dimensions.

[0096] Among them, the energy consumption analysis dimension can refer to various angles based on which the energy consumption monitoring data is analyzed.

[0097] Among them, the energy consumption analysis dimension includes at least one of a time dimension, a subspace dimension, and a device type dimension.

[0098] Among them, the time dimension can include at least one of year, month, day, and hour, and can also include dimensions of working days and non-working days.

[0099] Among them, the subspace dimension is used to perform energy consumption analysis according to the subspaces where the IoT devices are located in the target space, and the energy consumption monitoring data of each subspace is analyzed separately.

[0100] Among them, the device type dimension is used to perform energy consumption analysis according to the device types corresponding to the IoT devices.

[0101] Among them, the energy consumption monitoring data includes at least the device status data corresponding to each IoT device in the target space.

[0102] In specific implementation, the electronic device can analyze the energy consumption monitoring data of the target space according to different energy consumption analysis dimensions to obtain the total energy consumption data and / or energy-saving data of the target space under each energy consumption analysis dimension.

[0103] Among them, the total energy consumption data is obtained through big data analysis based on the energy consumption monitoring data.

[0104] Specifically, in the process of obtaining the total energy consumption data of the target space under each energy consumption analysis dimension, the energy consumption data of each IoT device can be obtained first, and then the energy consumption data of each IoT device can be summarized according to different energy consumption analysis dimensions to obtain the total energy consumption data of different subspaces, the total energy consumption data of different device types, and the total energy consumption data of different time granularities.

[0105] Among them, for the energy consumption data of IoT devices, for IoT devices with power calculation, the energy consumption of each time period will be calculated in real time inside the IoT device, and when the energy consumption changes, it will be reported. For example, if the energy consumption value of 1800 kWh is reported at 19:00 and the value of 1900 kWh is reported at 19:30, it means that the energy consumption data in the half hour from 19:00 to 19:30 is 100 kWh.

[0106] For IoT devices without power calculation, a method based on power * time is also provided to calculate the energy consumption data. If all the IoT devices in the target space are used, the rated power of the IoT devices has been built into the management system of the target space, and the power data in the management system can be directly used to obtain it. In addition, if third-party IoT devices are connected, the management system also provides relevant web pages, and users can input the power of these third-party IoT devices for subsequent energy consumption and energy-saving calculations. Energy consumption data calculation formula: Energy consumption (kWh) = Equipment power (kW) × Usage duration (h).

[0107] Among them, the usage duration can be the continuous duration when the IoT device is in the on state.

[0108] Among them, as mentioned above, IoT devices can report data in scenarios such as device state switching. When reporting data, the current timestamp will be carried. Therefore, based on the switch state data and the corresponding timestamp data in the reported device state data, the usage duration of the IoT device in each time period can be calculated.

[0109] For example, if the timestamps when the IoT device reports data five times are T1, T2, T3, T4, and T5 in sequence, and the corresponding switch states are on state, on state, off state, on state, and off state respectively. From this, the continuous duration (i.e., usage duration) Ta of the IoT device in the on state in the time period from T1 to T5 can be calculated as Ta = (T3 - T1) + (T5 - T4).

[0110] Among them, for the case where an energy consumption monitoring module is connected to the line, for example, by connecting to a circuit breaker device, the total energy consumption data of the line can be known, such as Figure 3 As shown, a schematic diagram of energy consumption data calculation is provided: For the circuit breaker device B with energy consumption calculation function, the IoT device A on this line does not have the energy consumption calculation function but can report the switch state, and the energy consumption data calculated by the circuit breaker device B is the energy consumption data of the IoT device A.

[0111] For the case where there are multiple IoT devices on the line, such as Figure 4As shown in the figure, a schematic diagram of energy consumption data calculation is provided: The air switch device B has the function of energy consumption calculation. The IoT device A on the line does not have the function of energy consumption calculation, but can report the switch state. However, the energy consumption data calculated by the air switch device B is not all the energy consumption data of the IoT device A. Only when the IoT device C on the line is closed, it is all the energy consumption data of the IoT device A. The IoT device C on the line does not have the function of energy consumption calculation, but can report the switch state. However, the energy consumption data calculated by the air switch device B is not all the energy consumption data of the IoT device C. Only when the IoT device A on the line is closed, it is all the energy consumption data of the IoT device C. Therefore, when there are multiple IoT devices on the line, the switch states of each IoT device can be combined. When the IoT device A is in the on state and the IoT device C is in the off state, the energy consumption data calculated by the air switch device B is all the energy consumption data of the IoT device A. Combining with the usage duration data of the IoT device A, the power of the IoT device A can be calculated. The calculation methods of the energy consumption and power of the IoT device C are the same.

[0112] In practical applications, diverse tags can be set for each IoT device based on the time dimension, subspace dimension, and device type dimension, and each time period can be divided into smaller granularities. Combining the energy consumption data of the IoT devices, energy consumption analysis can be carried out from multiple dimensions:

[0113] In the subspace dimension, according to the deployment location of the IoT devices in the target space, the energy consumption data of each IoT device is aggregated into the corresponding subspace. For each subspace, its total energy consumption data is calculated to obtain the energy consumption curve of the subspace.

[0114] In the device type dimension, according to the device type, the energy consumption data of the IoT devices is aggregated onto the device type. Through the energy consumption data comparison chart of the device type, the high-energy-consuming device types can be obtained, so that key energy-saving optimizations can be carried out for these high-energy-consuming device types.

[0115] In the time dimension, time periods and whether it is a working day can be divided. If it is determined that there is still high energy consumption on a non-working day, key optimizations are required.

[0116] In some embodiments, the energy-saving data can be analyzed based on the energy consumption saved by the IoT devices that perform energy-saving actions. Specifically, the energy-saving data can be the energy consumption data saved by the IoT devices during the energy-saving duration.

[0117] Among them, the energy-saving duration refers to the duration from when the IoT device finishes performing the energy-saving action to the next startup. Through the energy-saving duration and device power of the IoT device, the energy-saving data can be calculated. Then, according to different energy consumption analysis dimensions, the energy-saving data of the IoT devices is aggregated to obtain the energy-saving data of different subspaces, the energy-saving data of different device types, and the energy-saving data of different time granularities.

[0118] For the convenience of those skilled in the art to understand, Figure 5 a schematic diagram of an energy consumption analysis page is provided. As Figure 5 shown, it can display the total energy consumption data of the target space according to the time dimension, sub - space dimension, and device type dimension, and can further display the energy consumption trend of the target space.

[0119] For the convenience of those skilled in the art to understand, Figure 6 another schematic diagram of an energy consumption analysis page is provided. As Figure 6 shown, it can display energy - saving data according to the time dimension in the energy consumption analysis page, and count the most energy - saving sub - space, the most energy - saving Internet of Things devices, and the most energy - saving energy - saving actions. It can further display the execution time and energy - saving data of each energy - saving action. Further, Figure 7 yet another schematic diagram of an energy consumption analysis page is provided. As Figure 7 shown, it can perform energy - saving statistics in the energy consumption analysis page, including daily energy - saving data, monthly energy - saving data, energy - saving data of different sub - spaces, energy - saving data of each Internet of Things device, as well as the energy - saving ranking of each energy - saving action, recent energy - saving data, and other information.

[0120] The technical solution of this embodiment displays the energy consumption analysis page corresponding to the target space; in the energy consumption analysis page, it displays the total energy consumption data and / or energy - saving data according to different energy consumption analysis dimensions; the energy consumption analysis dimensions include at least one of the time dimension, sub - space dimension, and device type dimension; the total energy consumption data is obtained through big data analysis based on energy consumption monitoring data; the energy consumption monitoring data at least includes the device status data corresponding to each Internet of Things device in the target space; the energy - saving data is obtained by analyzing the energy consumption saved by the Internet of Things devices that perform energy - saving actions. In this way, according to different energy consumption analysis dimensions, big data analysis and processing can be performed on the energy consumption monitoring data of each Internet of Things device, realizing a more comprehensive analysis of the operation conditions of the Internet of Things devices in the target space, so as to obtain the total energy consumption data and / or energy - saving data of the target space under each energy consumption analysis dimension, providing a more accurate and finer - grained energy consumption analysis result to more comprehensively analyze the energy consumption distribution of the target space; and by displaying the total energy consumption data and / or energy - saving data according to different energy consumption analysis dimensions in the energy consumption analysis page, it can provide users with a quick view of the energy consumption monitoring data, realizing the convenient management of space energy consumption.

[0121] In one embodiment, the method further includes: obtaining total energy consumption data of the target space during the target time period according to the energy consumption monitoring data; displaying a second energy consumption reminder page corresponding to the target space when the total energy consumption data exceeds the preset energy consumption threshold associated with the target time period; the second energy consumption reminder page includes an energy consumption analysis entry; in response to a trigger operation on the energy consumption analysis entry, instructing an artificial intelligence model to perform energy consumption analysis on the energy consumption monitoring data and activity habit data to display an energy consumption analysis report for the target space; the energy consumption analysis report includes energy-saving optimization measures for the target space.

[0122] Wherein, the second energy consumption reminder page is an interactive page for reminding the user that the total energy consumption data of the target space during the target time period exceeds the preset energy consumption threshold associated with the target time period. For example, if the target time period is 0:00 - 24:00 every day and the total energy consumption data of the target space on the current day exceeds the preset daily energy consumption threshold, an energy consumption overage reminder will be shown on the second energy consumption reminder page; for example, if the target time period is a whole month and the total energy consumption data of the target space in that month exceeds the preset monthly energy consumption threshold, an energy consumption overage reminder will be shown on the second energy consumption reminder page.

[0123] Wherein, the energy consumption analysis entry refers to an entry for triggering the artificial intelligence model to perform energy consumption analysis.

[0124] Wherein, the energy consumption analysis entry can be an operation element. For example, specifically, it can be a button, an icon, a text box, etc.

[0125] In this application, the energy consumption analysis entry can display an energy consumption overage reminder text.

[0126] For example, the energy consumption analysis entry can be a text box, or a combination of an icon and a text box, and the energy consumption overage reminder text is displayed on the text box.

[0127] For the convenience of those skilled in the art to understand, Figure 8 a schematic diagram of a second energy consumption reminder page is provided. As Figure 8 shown, the second energy consumption reminder page can display energy consumption analysis entries 810 corresponding to different target time periods, and the energy consumption analysis entries 810 can display energy consumption overage reminder texts, such as "Daily power consumption overage reminder: Today's power consumption has exceeded 13 kwh, please check in time"; "Monthly power consumption overage reminder, this month's power consumption has exceeded 400 kwh, please check in time".

[0128] The user can click on the energy consumption analysis entry. In response to the trigger operation on the energy consumption analysis entry, the electronic device instructs the artificial intelligence model to perform energy consumption analysis on the energy consumption monitoring data and activity habit data, so as to display an energy consumption analysis report for the target space. The energy consumption analysis report includes energy-saving optimization measures for the target space, and the energy-saving optimization measures are used to reduce the energy consumption of the target space. Among them, the artificial intelligence model can include a large model. In the field of artificial intelligence, a large model refers to a machine learning model with a large number of parameters and a complex computing structure.

[0129] For example, if it is analyzed from the energy consumption monitoring data that the electric water heater is powered on for 24 hours and the average daily standby power consumption is 1.5 degrees; and if it is determined from the activity habit data that the target object uses hot water at 7:00 and 20:00 regularly, then the energy-saving optimization measures can include the measure of cutting off the power during non-use periods; if the activity trajectory of the target object in the evening is determined according to the activity habit data, then the energy-saving optimization measures can include automatically turning off the lighting fixtures in the areas where there is no one moving. For example, when the user enters the bedroom from the living room and more than 15 minutes have passed, the lighting in the living room is automatically turned off.

[0130] In some embodiments, the artificial intelligence model can also, according to the activity habits of the target object and in combination with the energy consumption monitoring data of the target space, recommend the best electricity usage time period or remind to turn off the devices that have not been used for a long time. For example, through analysis, it is known that the valley electricity period in the area where a certain family is located is from 10 pm to 6 am the next morning, and the family members usually go to bed after 11 pm and get up at about 7 am. The artificial intelligence model, based on these data and habits, recommends turning on high-power devices such as washing machines and electric water heaters after 10 pm to utilize the valley electricity period to reduce the electricity cost. At the same time, for some appliances that can set a reservation function in advance, such as rice cookers, it is recommended to start preheating to prepare breakfast during the valley electricity period, which not only saves electricity bills but also does not affect normal use. Another example is that it is monitored that the TV in the living room is still in the standby state after the family members go to bed, and the average daily standby time is up to 8 hours. In addition, there is a high probability that the table lamp in the bedroom is still on after the owner leaves the room for more than 30 minutes. Based on these situations, a reminder can be sent to the family members through the mobile phone APP or the smart home central control system to inform them to turn off the TV and table lamp that have not been used for a long time to avoid unnecessary energy consumption.

[0131] For the convenience of those skilled in the art to understand, Figure 9 a schematic diagram of an energy consumption analysis report generated by an artificial intelligence model is provided. As Figure 9As shown, the artificial intelligence model can perform energy consumption analysis based on energy consumption monitoring data and activity habit data, and generate an energy consumption analysis report for the target space. Exemplarily, the content included in the energy consumption analysis can include "The energy consumption is relatively high at night. It is recommended to adopt energy-saving measures. The energy consumption during the evening and late-night periods is relatively high, accounting for a considerable proportion of the total energy consumption. This may be due to the use of more electrical appliances or increased lighting requirements. To reduce energy consumption, it is possible to consider optimizing evening electricity usage behavior and adopting energy-saving measures: automatically turn off lighting fixtures in areas where there is no activity according to the user's activity trajectory in the evening. For example, when the user enters the bedroom from the living room and more than 15 minutes have passed, automatically turn off the lighting in the living room."

[0132] In the technical solution of this embodiment, the total energy consumption data of the target space in the target time period is obtained according to the energy consumption monitoring data; when the total energy consumption data exceeds the preset energy consumption threshold associated with the target time period, a second energy consumption reminder page corresponding to the target space is displayed; the second energy consumption reminder page includes an energy consumption analysis entry; in response to a trigger operation on the energy consumption analysis entry, an artificial intelligence model is instructed to perform energy consumption analysis on the energy consumption monitoring data and activity habit data, so as to display an energy consumption analysis report for the target space; the energy consumption analysis report includes energy-saving optimization measures for the target space.

[0133] In this way, by obtaining the total energy consumption data of the target space in the target time period and comparing it with the preset energy consumption threshold, once the total energy consumption data exceeds the threshold, the second energy consumption reminder page is displayed to remind the user that the energy consumption situation of the target space deviates from the normal range. At the same time, the second energy consumption reminder page is provided with an energy consumption analysis entry. Through the trigger operation, the user can instruct the artificial intelligence model to perform energy consumption analysis on the energy consumption monitoring data and activity habit data, so as to output an energy consumption analysis report containing energy-saving optimization measures for the target space, realizing the provision of personalized energy-saving suggestions for the target object and improving the efficiency and accuracy of energy management.

[0134] In one embodiment, obtaining the energy consumption monitoring data of the target space includes: receiving data packets reported by each Internet of Things device in the target space through a preset protocol interface; converting the data format of each data packet according to the preset data format corresponding to the protocol interface to obtain a converted data packet; and obtaining the energy consumption monitoring data according to the converted data packet.

[0135] Among them, the data packet includes at least device status data and environmental data.

[0136] In a specific implementation, the electronic device receives data packets reported by each Internet of Things device in the target space through a preset protocol interface; converts the data format of each data packet according to the preset data format corresponding to the protocol interface to obtain a converted data packet; and after performing data cleaning on the converted data packet, the energy consumption monitoring data can be obtained.

[0137] For the convenience of those skilled in the art to understand, Figure 10 a schematic diagram for converting data packets is provided. For IoT devices corresponding to different device brands and different communication protocols, it is necessary to make their data reporting mechanisms consistent through the access method as Figure 10 shown, so that they can be controlled and analyzed in the management system of the target space.

[0138] Among them, the purpose of the preset protocol interface is to connect IoT devices of different brands, different protocols, and different systems to the management system of the target space. The management system of the target space can control the data packets reported by the IoT devices through the preset protocol interface and normalize the data formats of the data packets. Since the resource value identifiers defined by different brands and different protocols are inconsistent, conversion is performed through the preset protocol interface to make them maintain a unified resource value identifier and a unified data structure.

[0139] The technical solution of this embodiment receives the data packets reported by each IoT device in the target space through the preset protocol interface; the data packets include device status data and environmental data; the data formats of the data packets are converted according to the preset data format corresponding to the protocol interface to obtain the converted data packets; the energy consumption monitoring data is obtained according to the converted data packets. In this way, the unified access and data format compatibility of IoT devices of different brands and different protocols are realized, the compatibility problem caused by the large number of device types and scattered brands in the target space is solved, which is convenient for subsequent storage, analysis and management, makes the data processing process more efficient and accurate, and avoids errors and confusion caused by inconsistent data formats.

[0140] In one embodiment, the activity habit data is associated with a corresponding activity time. According to the activity time corresponding to the activity habit data, the IoT devices that the target object in the target space does not need to use at the current time are identified; the running state of the IoT devices that do not need to be used is determined according to the energy consumption monitoring data; if the running state indicates that the IoT devices that do not need to be used are in the first mode, it is determined that there is an invalid enabling event in the target space.

[0141] Among them, the activity time associated with the activity habit data refers to the activity time of the activity habit of the target object indicated by the activity habit data. For example, the washing and bathing time of the target object, the sleeping time of the target object.

[0142] In specific implementation, according to the activity time corresponding to the activity habit data, the IoT devices that the target object in the target space does not need to use at the current time can be identified. For example, if the target object has the habit of washing and bathing from 7:00 to 8:00 and from 20:00 to 22:00 in the morning, and the current time is 15:00, then the water heater does not need to be used at the current time. For another example, if the target object has the habit of going out from 19:00 to 20:00 every night, and the current time is 19:10, then IoT devices such as the TV and the main light do not need to be used at the current time.

[0143] In this way, according to the energy consumption monitoring data, the operating state of the IoT device that does not need to be used at the current time can be determined. If the operating state indicates that the IoT device that does not need to be used is in the first mode, it is determined that there is an invalid enabling event in the target space. Among them, the first mode can refer to the normal working mode.

[0144] In this way, the activity habit data is associated with the corresponding activity time. According to the activity time corresponding to the activity habit data, the IoT devices that the target object in the target space does not need to use at the current time can be accurately identified. Then, according to the energy consumption monitoring data, the operating state of the IoT device that does not need to be used at the current time can be determined, so as to judge whether the operating state of the IoT device that does not need to be used at the current time indicates whether the IoT device is out of the first mode, and further, it can be determined whether the IoT device that does not need to be used at the current time consumes power, so as to accurately detect the invalid enabling event in the target space.

[0145] In some embodiments, when the IoT devices that the target object in the target space does not need to use at the current time are identified, the electronic device can send a control instruction to the IoT devices that do not need to be used; the control instruction is used to instruct the IoT devices that do not need to be used to switch from the first mode to the second mode; the energy consumption of the IoT devices that do not need to be used in the second mode is lower than that in the first mode. For example, the second mode can be the shutdown mode or the low power consumption mode.

[0146] In this way, after the IoT devices that the target object in the target space does not need to use at the current time are identified, by sending a control instruction to make the IoT devices that do not need to be used switch from the first mode to the second mode with lower energy consumption, the unnecessary energy consumption in the target space can be effectively reduced, the energy consumption cost can be reduced, and the intelligence of the energy consumption control in the target space can be effectively improved.

[0147] For the convenience of those skilled in the art to understand, Figure 11 a framework diagram of an energy consumption optimization method is provided. Taking the house of a certain family as an example of the target space, energy consumption optimization is achieved through the access of IoT devices, the processing and conversion of preset protocol interfaces, data cleaning and collection, multi-index calculation, electricity-price energy-carbon conversion, multi-dimensional analysis, and the intelligent analysis and prediction of artificial intelligence models.

[0148] Among them, the steps of processing and converting the data packets reported by different IoT devices through a preset protocol interface and performing data cleaning to obtain the energy consumption monitoring data of the target space have been described in the above embodiments and will not be elaborated here.

[0149] Among them, the calculations of various indicators such as usage duration, power calculation, energy consumption calculation, and energy saving calculation have been described in the above embodiments and will not be elaborated here.

[0150] Among them, for the docking of values such as electricity bills and carbon emissions:

[0151] The system currently supports multiple electricity bill measurement methods, for example:

[0152] a. Fixed fee calculation: Calculate the electricity bill according to the electricity consumption: Electricity bill = Electricity consumption × Electricity price per unit.

[0153] Among them, the electricity consumption can be determined according to the total energy consumption data of the IoT devices.

[0154] b. Cumulative billing: Charge in segments according to the electricity consumption, and the electricity price per unit is different for different electricity consumption ranges.

[0155] c. Time-of-use billing: The electricity price per unit is different for different time periods.

[0156] At the same time, the system also provides the conversion of values such as carbon emissions, and also provides the conversion of other measurement methods.

[0157] Among them, through multi-dimensional data analysis, comparative analysis can be carried out based on various custom tags such as time dimension, subspace dimension, and device type dimension, helping users better analyze the household energy consumption situation.

[0158] In addition, the large model analysis ability is also provided to simplify the cumbersome operations of user analysis and settings. The large model is connected to the relevant logs and system data of the background management system. Based on the activity habit data and energy consumption monitoring data, a summary report can be made, and reasonable automated suggestions or other energy saving measures can be given. Continuously feeding historical data and environmental data to the large model can train and generate a prediction model for the household. Based on future weather changes, holidays, and changes in user habits, the system will give reasonable suggestions or specific optimized settings.

[0159] In summary, the energy consumption optimization method of the present application has the advantages of accurate energy consumption measurement, intelligent energy saving optimization, multi-dimensional energy consumption analysis, and trend prediction, providing a more efficient and intelligent solution for household energy consumption management.

[0160] In another embodiment, as Figure 12 shown, an energy consumption optimization method is provided. Taking the application of this method to an electronic device as an example for description, it includes the following steps:

[0161] Step S1202: Receive data packets reported by each Internet of Things device in the target space through a preset protocol interface.

[0162] Step S1204: Convert the data formats of the data packets according to the preset data format corresponding to the protocol interface to obtain the converted data packets.

[0163] Step S1206: Obtain energy consumption monitoring data based on the converted data packets.

[0164] Step S1208: Obtain the historical activity data of the target object in the target space.

[0165] Step S1210: Extract features from the historical activity data to obtain historical activity features.

[0166] Step S1212: Mine activity habits based on the historical activity features to obtain activity habit data.

[0167] Step S1214: Identify the Internet of Things devices that the target object does not need to use at the current time in the target space according to the activity time corresponding to the activity habit data.

[0168] Step S1216: Determine the running status of the Internet of Things devices that do not need to be used at the current time according to the energy consumption monitoring data.

[0169] Step S1218: If the running status indicates that the Internet of Things devices that do not need to be used are in the first mode, it is determined that there is an invalid enabling event in the target space.

[0170] Step S1220: Perform an energy-saving action matching the invalid enabling event.

[0171] It should be noted that the specific limitations of the above steps can be referred to the specific limitations of an energy consumption optimization method described above.

[0172] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0173] Based on the same inventive concept, an embodiment of the present application further provides an energy consumption optimization device for implementing the energy consumption optimization method involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the energy consumption optimization device provided below can refer to the limitations on the energy consumption optimization method in the foregoing, and will not be elaborated herein.

[0174] In an exemplary embodiment, as Figure 13 shown, an energy consumption optimization device is provided, including: an acquisition module 1310, a detection module 1320, and an execution module 1330, where:

[0175] The acquisition module 1310 is configured to acquire energy consumption monitoring data of a target space, and acquire activity habit data of a target object in the target space; the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habit of the target object.

[0176] The detection module 1320 is configured to detect an ineffective enabling event existing in the target space according to the activity habit data and the energy consumption monitoring data.

[0177] The execution module 1330 is configured to execute an energy-saving action matching the ineffective enabling event.

[0178] In one embodiment, the acquisition module 1310 is specifically configured to acquire historical activity data of the target object in the target space; the historical activity data is used to indicate the activity of the target object in the target space during a historical time period; perform big data analysis on the historical activity data to obtain the activity habit data.

[0179] In one embodiment, the acquisition module 1310 is specifically configured to extract features from the historical activity data to obtain historical activity features; the historical activity features include at least one of activity time, the location where the target object is located, and the operating state of the Internet of Things device; perform activity habit mining based on the historical activity features to obtain the activity habit data.

[0180] In one embodiment, the execution module 1330 is specifically configured to display a first energy consumption reminder page corresponding to the target space; the first energy consumption reminder page is used to receive a confirmation operation for enabling an automation solution; the automation solution is used to execute an energy-saving action matching the invalid enabling event; or, obtain an automation solution associated with the invalid enabling event, and send a control instruction to a target Internet of Things device; the control instruction is used to instruct the target Internet of Things device to execute the energy-saving action indicated by the automation solution; the target Internet of Things device is an Internet of Things device in the target space where the invalid enabling event exists.

[0181] In one embodiment, the target space includes at least one subspace, and the device further includes: a display module, configured to display an energy consumption analysis page corresponding to the target space; display total energy consumption data and / or energy-saving data in the energy consumption analysis page according to different energy consumption analysis dimensions; the energy consumption analysis dimensions include at least one of a time dimension, a subspace dimension, and a device type dimension; the total energy consumption data is obtained by performing big data analysis on the energy consumption monitoring data; the energy consumption monitoring data at least includes device status data corresponding to each Internet of Things device in the target space; the energy-saving data is obtained by analyzing the energy consumption saved by the Internet of Things devices that execute the energy-saving actions.

[0182] In one embodiment, the device further includes: an energy consumption analysis module, configured to obtain the total energy consumption data of the target space in a target time period according to the energy consumption monitoring data; when the total energy consumption data exceeds a preset energy consumption threshold associated with the target time period, display a second energy consumption reminder page corresponding to the target space; the second energy consumption reminder page includes an energy consumption analysis entry; in response to a trigger operation on the energy consumption analysis entry, instruct an artificial intelligence model to perform energy consumption analysis on the energy consumption monitoring data and the activity habit data, so as to display an energy consumption analysis report for the target space; the energy consumption analysis report includes energy-saving optimization measures for the target space.

[0183] In one embodiment, the acquisition module 1310 is specifically configured to receive data packets reported by each Internet of Things device in the target space through a preset protocol interface; the data packets include device status data and environmental data; convert the data formats of the data packets according to a preset data format corresponding to the protocol interface to obtain converted data packets; obtain the energy consumption monitoring data according to the converted data packets.

[0184] In one embodiment, activity habit data is associated with corresponding activity times. The detection module 1320 is specifically configured to identify, according to the activity times corresponding to the activity habit data, the Internet of Things devices that the target object in the target space does not need to use at the current time; determine the operating states of the Internet of Things devices that do not need to be used at the current time according to the energy consumption monitoring data; and if the operating state indicates that the Internet of Things devices that do not need to be used are in the first mode, determine that there is an invalid enabling event in the target space.

[0185] In one embodiment, the execution module 1330 is specifically configured to send a control instruction to the Internet of Things device that does not need to be used; the control instruction is used to instruct the Internet of Things device that does not need to be used to switch from the first mode to the second mode; and the energy consumption of the Internet of Things device that does not need to be used in the second mode is lower than that in the first mode.

[0186] Each module in the above energy consumption optimization device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the electronic device in the form of hardware or be independent of the processor, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0187] In an exemplary embodiment, an electronic device is provided. The electronic device may be a terminal, and its internal structure diagram may be as Figure 14 shown. The electronic device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program stored in the non-volatile storage medium to run. The input / output interface of the electronic device is used for the processor to exchange information with external devices. The communication interface of the electronic device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be achieved through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements an energy consumption optimization method. The display unit of the electronic device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or can also be an external keyboard, a touchpad, or a mouse, etc.

[0188] Those skilled in the art can understand that Figure 14 the structure shown in [the figure] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0189] In one embodiment, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0190] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0191] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0192] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0193] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0194] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0195] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. An energy consumption optimization method, characterized in that, The method includes: Obtaining energy consumption monitoring data of a target space, and obtaining activity habit data of a target object in the target space; the energy consumption monitoring data includes data collected during the process of monitoring the energy consumption of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object; Detecting an invalid enabling event existing in the target space according to the activity habit data and the energy consumption monitoring data; Performing an energy-saving action matching the invalid enabling event.

2. The method according to claim 1, wherein The obtaining of the activity habit data of the target object in the target space includes: Obtaining historical activity data of the target object in the target space; the historical activity data is used to indicate the activities of the target object in the target space during a historical time period; Performing big data analysis on the historical activity data to obtain the activity habit data.

3. The method according to claim 2, wherein The performing of big data analysis on the historical activity data to obtain the activity habit data includes: Extracting features from the historical activity data to obtain historical activity features; the historical activity features include at least one of activity time, the location where the target object is located, and the operating state of the Internet of Things device; Mining activity habits based on the historical activity features to obtain the activity habit data.

4. The method according to claim 1, characterized in that, The performing of the energy-saving action matching the invalid enabling event includes: Displaying a first energy consumption reminder page corresponding to the target space; the first energy consumption reminder page is used to receive a confirmation operation for enabling an automation solution; the automation solution is used to perform an energy-saving action matching the invalid enabling event; Or, Obtaining an automation solution associated with the invalid enabling event, and sending a control instruction to a target Internet of Things device; the control instruction is used to instruct the target Internet of Things device to perform the energy-saving action indicated by the automation solution; the target Internet of Things device is an Internet of Things device in the target space where the invalid enabling event exists.

5. The method according to claim 1, wherein The target space includes at least one subspace, and the method further includes: Displaying an energy consumption analysis page corresponding to the target space; Displaying total energy consumption data and / or energy-saving data in the energy consumption analysis page according to different energy consumption analysis dimensions; The energy consumption analysis dimensions include at least one of a time dimension, a subspace dimension, and a device type dimension; the total energy consumption data is obtained by performing big data analysis on the energy consumption monitoring data; the energy consumption monitoring data at least includes device status data corresponding to each Internet of Things device in the target space; the energy-saving data is obtained by analyzing the energy consumption saved by the Internet of Things devices performing the energy-saving action.

6. The method according to claim 1, wherein The method further includes: Obtaining total energy consumption data of the target space in a target time period according to the energy consumption monitoring data; When the total energy consumption data exceeds a preset energy consumption threshold associated with the target time period, displaying a second energy consumption reminder page corresponding to the target space; the second energy consumption reminder page includes an energy consumption analysis entry; In response to a trigger operation on the energy consumption analysis entry, an artificial intelligence model is instructed to perform an energy consumption analysis on the energy consumption monitoring data and the activity habit data to display an energy consumption analysis report for the target space; the energy consumption analysis report includes energy-saving optimization measures for the target space.

7. The method according to claim 1, wherein The obtaining of the energy consumption monitoring data of the target space includes: Receiving data packets reported by each Internet of Things device in the target space through a preset protocol interface; the data packets include device status data and environmental data; Converting the data format of each of the data packets according to the preset data format corresponding to the protocol interface to obtain the converted data packets; Obtaining the energy consumption monitoring data according to the converted data packets.

8. The method according to claim 1, characterized in that, The activity habit data is associated with corresponding activity times. The detecting of the ineffective enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data includes: Identifying the Internet of Things devices in the target space that the target object does not need to use at the current time according to the activity times corresponding to the activity habit data; Determining the operating state of the Internet of Things devices that do not need to be used at the current time according to the energy consumption monitoring data; If the operating state indicates that the Internet of Things devices that do not need to be used are in the first mode, it is determined that the ineffective enabling events exist in the target space.

9. The method according to claim 8, wherein The executing of the energy-saving actions matching the ineffective enabling events includes: Sending a control instruction to the Internet of Things devices that do not need to be used; the control instruction is used to instruct the Internet of Things devices that do not need to be used to switch from the first mode to the second mode; the energy consumption of the Internet of Things devices that do not need to be used in the second mode is lower than that in the first mode.

10. An energy consumption optimization device, characterized in that, The device includes: An obtaining module, configured to obtain the energy consumption monitoring data of the target space and obtain the activity habit data of the target object in the target space; the energy consumption monitoring data includes the data collected during the energy consumption monitoring of each Internet of Things device in the target space; the activity habit data is used to indicate the activity habits of the target object; A detecting module, configured to detect the ineffective enabling events existing in the target space according to the activity habit data and the energy consumption monitoring data; An executing module, configured to execute the energy-saving actions matching the ineffective enabling events.

11. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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