Energy consumption statistical optimization method and device for equipment linkage control, terminal and medium

Abstract

The application discloses a device linkage control energy consumption statistical optimization method and device, a terminal and a medium. The method comprises the following steps: obtaining a target rule, performing multi-device linkage control based on the target rule; when target environment information meets a trigger condition of the target rule, instructing a target device to perform a target action; when the target device performs the target action, recording target event information of the target device to a target record table, the target event information comprising a target event timestamp, an event duration and an energy consumption result; and optimizing multiple devices based on the target record table. The application can solve the problem that the prior art lacks overall consideration of the overall energy consumption under device linkage control, and can make more reasonable device operation strategies, so that linkage control between devices can further optimize energy consumption, prolong the service life of the devices, and improve user experience and management efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and particularly to an energy consumption statistics optimization method, device, terminal and medium for device linkage control. Background Art

[0002] Currently, Internet of Things (IoT) technology has been widely applied to various intelligent devices and systems, enabling interconnection and collaborative work among devices. However, in terms of energy consumption statistics optimization, most existing methods are still limited to collecting, analyzing and optimizing energy consumption data of individual devices, lacking overall consideration of the overall energy consumption under device linkage control.

[0003] One of the main problems caused by this limitation is that even if the energy consumption of an individual device is optimized, in the scenario of device linkage control, due to the mutual influence and collaborative work among devices, the overall energy consumption may not reach the optimal state. Therefore, a method for statistically optimizing the energy consumption under device linkage control is needed to better achieve the goal of energy conservation and emission reduction.

[0004] Therefore, the existing technology still needs to be improved and enhanced. Summary of the Invention

[0005] The main purpose of the present invention is to provide an energy consumption statistics optimization method, device, terminal and medium for device linkage control, aiming to solve the problem that the existing technology is still limited to collecting, analyzing and optimizing energy consumption data of individual devices, lacking overall consideration of the overall energy consumption under device linkage control.

[0006] To achieve the above object, in the first aspect of the present invention, an energy consumption statistics optimization method for device linkage control is provided, wherein the energy consumption statistics optimization method for device linkage control includes:

[0007] Obtain a target rule, and perform multi-device linkage control based on the target rule;

[0008] When the target environmental information meets the trigger condition of the target rule, instruct the target device to execute the target action;

[0009] When the target device executes the target action, record the target event information of the target device into a target record table, and the target event information includes a target event timestamp, an event duration and an energy consumption result;

[0010] Optimize multiple devices based on the target record table.

[0011] In one implementation, the performing multi-device linkage control based on the target rule includes:

[0012] Based on the target rules, obtain the number and location of nodes, target triggering conditions, and rule execution logic;

[0013] Multiple devices are controlled in a coordinated manner based on the number and location of the nodes, the target triggering conditions, and the rule execution logic.

[0014] In one implementation, each node is an independent unit for executing the target rule, used to determine whether the target triggering condition is met based on feedback from local sensors.

[0015] In one implementation, the target rule includes:

[0016] The rule-delayed execution function is used to instruct the target device to perform the target action after a delay according to a preset delay time when the target environment information meets the target triggering condition.

[0017] The range-time execution function is used to set the time range in which the rule takes effect, so that the target device executes the target action within the target time range corresponding to the target rule;

[0018] The scheduled execution function is used to set the time point when the rule takes effect, so that the target device sets the start time of the target rule according to the month, week or Cron expression.

[0019] In one implementation, the target rule further includes:

[0020] Enhanced rules, which support complex logical configurations for multiple rules, including but not limited to combinations of logical "OR", logical "AND" and logical "NOT".

[0021] In one implementation, the optimization of multiple devices based on the target record table includes:

[0022] The target record table is cleaned to obtain the target data;

[0023] The target data is integrated into a target dataset, which includes multiple sets of device-time period-device status-energy consumption data;

[0024] The target dataset is analyzed to obtain a target anomaly report;

[0025] Optimize multiple devices based on the target anomaly report.

[0026] In one implementation, the optimization of multiple devices based on the target anomaly report includes:

[0027] Obtain abnormal data from the target abnormality report, obtain the abnormal device corresponding to the abnormal data and report it to the cloud platform, so that the cloud platform can issue a maintenance notice based on the abnormal data.

[0028] A second aspect of the present invention provides an energy consumption statistical optimization device for equipment linkage control, comprising:

[0029] The control module is used to acquire target rules and perform multi-device linkage control based on the target rules;

[0030] The execution module is used to instruct the target device to perform the target action when the target environment information meets the triggering conditions of the target rule;

[0031] The recording module is used to record the target event information of the target device to the target record table when the target device performs the target action. The target event information includes the target event timestamp, event duration, and energy consumption result.

[0032] An optimization module is used to optimize multiple devices based on the target record table.

[0033] A third aspect of the present invention provides a terminal, wherein the terminal includes: a memory, a processor, and a device linkage control energy consumption statistics optimization program stored in the memory and executable on the processor, wherein when the device linkage control energy consumption statistics optimization program is executed by the processor, it implements the steps of the device linkage control energy consumption statistics optimization method as described above.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the energy consumption statistical optimization method for device linkage control as described in any of the preceding claims.

[0035] Beneficial Effects: Compared with existing technologies, this invention provides a method, device, terminal, and medium for energy consumption statistical optimization of equipment linkage control. In the energy consumption statistical optimization method for equipment linkage control provided by this invention, when performing multi-device linkage control, a target rule is first obtained. Based on the target rule, multi-device linkage control is performed. When the target environmental information meets the triggering condition of the target rule, the target device is instructed to execute the target action. When the target device executes the target action, the target event information of the target device is recorded in a target record table. The target event information includes the target event timestamp, event duration, and energy consumption result. Finally, the multi-device system is optimized based on the target record table. This invention provides users with a method for energy consumption statistical optimization of equipment linkage control, solving the problem that existing technologies are limited to collecting, analyzing, and optimizing energy consumption data for individual devices, lacking a comprehensive consideration of overall energy consumption under equipment linkage control. In this invention, by recording the target event information of the target device in a target record table and optimizing multiple devices based on the target record table, more reasonable equipment operation strategies can be formulated. This allows for further optimization of energy consumption in the linkage control between devices, extends the service life of equipment, reduces the cost of replacing equipment, and improves user experience and management efficiency. Attached Figure Description

[0036] Figure 1 A flowchart illustrating an embodiment of the energy consumption statistical optimization method for equipment linkage control provided by the present invention;

[0037] Figure 2 A schematic diagram of the structural principle of an embodiment of the energy consumption statistical optimization device for equipment linkage control provided by the present invention;

[0038] Figure 3 A schematic diagram of the operating environment of an embodiment of the terminal provided by the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention 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 merely illustrative of the invention and are not intended to limit the invention.

[0040] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0041] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0042] The energy consumption statistical optimization method for equipment linkage control provided by this invention can be applied to terminals with computing capabilities. The terminal can execute the energy consumption statistical optimization method for equipment linkage control provided by this invention to perform overall energy consumption optimization under equipment linkage control.

[0043] Example 1

[0044] The energy consumption statistics optimization method for device linkage control provided in this embodiment can be executed by a terminal, which can be, but is not limited to, an intelligent computer. The following explanation uses an intelligent computer as an example.

[0045] Specifically, such as Figure 1 As shown, the energy consumption statistical optimization method for equipment linkage control provided in this embodiment includes the following steps:

[0046] S100. Obtain the target rule and perform multi-device linkage control based on the target rule.

[0047] The multi-device linkage control based on the target rule includes:

[0048] Based on the target rules, obtain the number and location of nodes, target triggering conditions, and rule execution logic;

[0049] Multiple devices are controlled in a coordinated manner based on the number and location of the nodes, the target triggering conditions, and the rule execution logic.

[0050] Each node is an independent unit for executing the target rule, used to determine whether the target triggering condition is met based on feedback from local sensors.

[0051] Specifically, each rule node plays an indispensable and highly autonomous independent unit role in the execution of the target rule. They are not merely relay stations for data flow or signal processing, but also a crucial link in intelligent decision-making. The rule nodes can directly receive and parse real-time feedback data from local sensors, which typically covers information on environmental parameters, device status, user behavior patterns, and other aspects.

[0052] Based on this detailed and real-time local sensor data, rule nodes can quickly and accurately determine whether the current environment or device state has met the preset trigger conditions in the target rule. This process often involves complex logical operations and condition matching, but thanks to advanced algorithm optimization and hardware support, these judgments can be completed in milliseconds, ensuring the timeliness and accuracy of the system response.

[0053] Specifically, after obtaining the number and location of the target rule acquisition nodes, corresponding rule nodes are deployed in the system based on the node number and location information provided in the target rule. These rule nodes can be logical judgment points at the software level or sensors or actuators at the hardware level. The number may be one or multiple, depending on the complexity and coverage of the rule. Taking smart home security rules as an example, it may be necessary to deploy one or more sensor nodes (such as door and window sensors) to monitor window status, and one or more logical judgment nodes (possibly within the smart hub) to handle the combined logic of unattended status and window status.

[0054] For example, in a complex logic scenario, multiple sensors are deployed, each acting as a rule node. Each rule node can independently perform an action, such as reading the temperature or determining if someone is present. After performing the action, it is compared with preset conditions; if the conditions are met, the node's state is set to true. This process is repeated for other nodes, and once all nodes meet the conditions, subsequent actions are executed, such as turning off lights or air conditioning.

[0055] Specifically, the user first configures multiple rules through a graphical interface or programming interface. Each rule contains at least one rule node, and each rule node is associated with at least one sensor or device. The triggering conditions and execution actions of the rule nodes are also defined. In this embodiment, the task execution method is implemented based on the target rules provided by the user.

[0056] Obtaining the target rule is the first step in the entire process, which typically means selecting a specific rule from a pre-defined rule base or a dynamically generated rule set as the operational benchmark. The target rule defines in detail the key information required for subsequent steps, including but not limited to the number and location of the nodes, the target triggering conditions, and the rule execution logic.

[0057] Specifically, users configure the target rules to the enhanced rule engine system through a graphical interface or a programming interface.

[0058] After receiving the target rule, the enhanced rule engine system obtains the number and location of nodes, target triggering conditions, and rule execution logic based on the target rule.

[0059] In other words, the user-configured rules are first parsed based on the target rules to generate rule execution logic. This logic is then stored in a rule database to obtain the number and location of nodes, the target triggering conditions, and the rule execution logic. Multiple devices can then be controlled in a coordinated manner based on the number and location of nodes, the target triggering conditions, and the rule execution logic.

[0060] In this embodiment, the target rule includes:

[0061] The rule-delayed execution function is used to instruct the target device to perform the target action after a delay according to a preset delay time when the target environment information meets the target triggering condition.

[0062] The range-time execution function is used to set the time range in which the rule takes effect, so that the target device executes the target action within the target time range corresponding to the target rule;

[0063] The scheduled execution function is used to set the time point when the rule takes effect, so that the target device sets the start time of the target rule according to the month, week or Cron expression.

[0064] Furthermore, the target rule also includes:

[0065] Enhanced rules, which support complex logical configurations for multiple rules, including but not limited to combinations of logical "OR", logical "AND" and logical "NOT".

[0066] The rule-delayed execution function is an advanced automated control mechanism that is flexible and practical, designed to precisely control the timing of device operations based on specific conditions. The core of this function lies in its ability to monitor the information status of the target environment. Once this information meets preset trigger conditions, it does not immediately trigger the device's action but waits for a pre-set delay before instructing the target device to execute the corresponding target action. This design allows the system to more finely control the operation sequence to adapt to various complex application scenarios, such as smart homes, industrial automation, and security monitoring.

[0067] The time-scoped execution feature is a powerful time management capability that allows users or system administrators to set a specific time frame for automation rules, ensuring that these rules are only effective within the specified time period. The core value of this feature lies in its ability to precisely control when target devices execute target actions based on these rules, thereby meeting various complex business needs and everyday scenarios.

[0068] Specifically, the range-based time execution function allows users to define the start and end times for a rule to take effect, forming a valid time window. Within this time window, if other conditions for the target rule (such as environmental detection, state changes, etc.) are also met, the target device will execute the corresponding action as instructed by the rule. However, once the time exceeds this range, even if other conditions are met, the rule will not trigger the device's action.

[0069] Furthermore, the range-time execution function can be combined with other automation rules to create more complex and refined control strategies. For example, by combining light sensors and human motion detectors, rules can be further refined to automatically adjust the heating or cooling system based on temperature conditions only in rooms where people are active at night.

[0070] In summary, the range-time execution function provides powerful time management capabilities for automated control systems, enabling the system to flexibly adjust the timing of equipment operation according to actual needs, thereby optimizing resource utilization and improving user experience.

[0071] Scheduled execution is also a highly flexible and powerful automation control feature. It allows users or system administrators to set precise execution times for specific rules, ensuring that target devices can accurately perform the corresponding actions at the preset time. The core of this function lies in its ability to define the specific time when rules take effect based on complex time scheduling mechanisms such as monthly, weekly, or Cron expressions, thereby meeting various complex business needs and life scenarios.

[0072] Specifically, the scheduled execution function supports multiple time setting methods. Among them, the month-based time setting allows users to specify a day or several days in a month as the execution day for the rule; the week-based time setting allows users to select a day or several days in a week, as well as a specific time period within a day, as the execution time; while Cron expressions provide a more flexible and powerful time setting method, allowing users to define complex execution plans through specific string formats, including multiple time conditions such as minutes, hours, days, months, and days of the week.

[0073] In summary, the timed execution function provides powerful time scheduling capabilities for automated control systems, enabling the system to flexibly adjust the timing of equipment operation according to actual needs, thereby optimizing resource utilization and improving user experience. Whether in home automation, industrial automation, or other fields, the timed execution function plays a crucial role.

[0074] Enhanced rules can extend the flexibility and complexity of rule configuration. By supporting complex logic configurations for multiple rules, enhanced rules allow users or system administrators to create combined rules that include various logical operations such as OR, AND, and NOT. The combined use of these logical operators enables the system to implement more refined and complex control logic based on multiple conditions and variables.

[0075] The logical "OR" operator allows a rule to trigger an action when either condition is met, while the logical "AND" operator requires all conditions to be met simultaneously to trigger an action. The logical "NOT" operator is used to reverse the state of a condition, triggering an action when the condition is not met, or not triggering an action when the condition is met. By combining these logical operators, users can create rules that can handle a variety of complex scenarios.

[0076] With enhanced rule configuration, smart home systems can more accurately understand user needs and provide intelligent services in various complex scenarios. Whether in home automation, industrial automation, or other fields, enhanced rules provide users with stronger control capabilities and more flexible configuration options, enabling automated control systems to better adapt to various practical application scenarios.

[0077] S200: When the target environment information meets the triggering conditions of the target rule, instruct the target device to perform the target action.

[0078] Specifically, when the target environmental information meets the specific triggering conditions of the preset target rule, the system automatically triggers a series of instructions to precisely instruct the corresponding target device to perform the preset target action. This process is a core component of automated control in intelligent systems or Internet of Things (IoT) environments. Based on real-time monitoring and analysis of environmental information and pre-set rule logic, it achieves intelligent management of device behavior.

[0079] Let's take a smart home system as an example for a detailed explanation:

[0080] Suppose the target rule is set to "automatically turn on the air conditioner to lower the indoor temperature to 26 degrees Celsius when the indoor temperature exceeds 28 degrees Celsius, and simultaneously turn on the fan to enhance air circulation." In this case, the target environmental information is the indoor temperature, and the trigger condition is a temperature exceeding 28 degrees Celsius.

[0081] When the temperature sensor in the smart home system detects that the indoor temperature reaches or exceeds 28 degrees Celsius, this environmental information will be captured by the system in real time. The system will then check whether this information meets the preset target rule trigger conditions. Once the conditions are confirmed to be met, the system will immediately execute the corresponding command operation:

[0082] Instruct the air conditioning unit to start cooling mode and set the temperature to 26 degrees Celsius to begin lowering the indoor temperature.

[0083] At the same time, the system instructs the fan equipment to be turned on to enhance indoor air circulation and improve the cooling effect.

[0084] Through this series of automated commands, smart home systems can quickly respond to changes in ambient temperature, providing users with a more comfortable living environment. At the same time, this rule-based automated control method also helps improve energy efficiency and reduce unnecessary energy consumption.

[0085] It is worth noting that, in practical applications, the target rules and the target triggering conditions can be flexibly set according to the user's specific needs and preferences. For example, in an office environment, the target rule may be set to "automatically turn on the lighting equipment when the indoor light is insufficient and people are frequently active" to meet the work needs of office workers and ensure sufficient indoor lighting.

[0086] S300. When the target device performs the target action, the target event information of the target device is recorded in the target record table. The target event information includes the target event timestamp, event duration, and energy consumption result.

[0087] Once the target device receives the instruction and executes the target action, the system will immediately initiate a recording process to record all relevant target event information of the target device in detail into a specific target record table.

[0088] Specifically, in this embodiment, the target event information includes the target event timestamp, event duration, and energy consumption results.

[0089] The target event timestamp is a precise marker recording the time when the event occurred, typically presented in the form of year, month, day, hour, minute, and second. The accuracy of the timestamp is crucial for subsequent analysis of device behavior patterns, assessment of response speed, and identification of potential problems.

[0090] The event duration refers to the time period from the start of the target device performing the target action to the completion of the action. This data is of great significance for evaluating equipment efficiency, predicting maintenance cycles, and optimizing operating procedures.

[0091] The energy consumption results reflect the energy consumption of the target equipment during the execution of the target action, which may include the consumption and rate of various energy types such as electricity, water, and gas. Energy consumption results are an important reference for measuring equipment energy efficiency, formulating energy conservation and emission reduction strategies, and optimizing energy use.

[0092] Let's take the air conditioning unit in a smart home system as an example for a specific explanation:

[0093] Suppose the target rule is set to "automatically turn on the air conditioner to cooling mode and set the temperature to 25 degrees Celsius when the indoor temperature sensor detects that the temperature exceeds 30 degrees Celsius." When the indoor temperature sensor detects that the temperature exceeds 30 degrees Celsius, the smart home system will automatically start the air conditioner according to the preset rule.

[0094] At this time, the system will record the following target event information to the target record table:

[0095] Target event timestamp: For example, "May 18, 2023, 14:30:00" indicates the exact time when the air conditioning equipment began to perform cooling operations.

[0096] Event duration: Assuming the air conditioner runs continuously for 2 hours, or "120 minutes", it represents the time elapsed from start-up to stopping cooling.

[0097] Energy consumption results: Assuming that the air conditioner consumes 2.5 kWh of electricity in 2 hours of operation in cooling mode, this data will be recorded as an energy consumption result.

[0098] By recording this target event information, smart home systems can not only monitor device status in real time but also provide users with detailed energy consumption reports, helping them better understand and manage household energy consumption and make more informed energy-saving decisions. At the same time, this data also provides valuable reference for system optimization and device upgrades.

[0099] S300. Optimize the target rules based on the target record table.

[0100] The optimization of the target rules based on the target record table includes:

[0101] The target record table is cleaned to obtain the target data;

[0102] The target data is integrated into a target dataset, which includes multiple sets of device-time period-device status-energy consumption data;

[0103] The target dataset is analyzed to obtain a target anomaly report.

[0104] Optimize multiple devices based on the target anomaly report.

[0105] Specifically, the target record table is first cleaned to remove invalid, erroneous, or redundant data to ensure the accuracy of subsequent analysis.

[0106] Specifically, this includes identifying and handling missing values, correcting erroneous data, and removing redundant data. Identifying and handling missing values ​​involves checking the target record table for missing data items and, depending on the situation, choosing to fill in (e.g., using the mean, median, etc.) or delete missing records. Correcting erroneous data involves checking and correcting obvious errors, such as unreasonable energy consumption values ​​or incorrect timestamps. Removing redundant data involves deleting duplicate records or unnecessary fields to reduce the size and complexity of the dataset. In this way, the cleaned target data can be obtained, making it more accurate and complete, suitable for subsequent analysis.

[0107] The cleaned data is then integrated into a structured dataset for easier subsequent analysis and optimization. This includes: constructing the data structure: designing the data structure based on the analysis requirements, including fields such as device ID, time period, device status, and energy consumption; data integration: integrating the cleaned data according to the designed structure to form the target dataset; and data validation: validating the integrated data to ensure its integrity and consistency. This results in multiple sets of device-time period-device status-energy consumption data.

[0108] Finally, through data analysis, abnormal states and high-energy-consumption periods of the equipment are identified, providing a basis for optimization, and multiple devices are optimized based on the target anomaly report.

[0109] Specifically, this includes preprocessing the target dataset, such as data standardization and normalization, to facilitate subsequent analysis, followed by anomaly detection: using statistical methods (such as the 3σ principle, box plots, etc.) or machine learning algorithms (such as clustering algorithms, anomaly detection algorithms, etc.) to detect abnormal data. Energy consumption analysis: calculating the average energy consumption, peak energy consumption, and other indicators for each device, and analyzing the energy consumption distribution and trends. Afterwards, the target anomaly report can be generated. Specifically, the anomaly detection results and energy consumption analysis results are integrated into the target anomaly report, including a list of abnormal devices, abnormal time periods, descriptions of abnormal states, and energy consumption comparisons, to optimize multiple devices based on the target anomaly report.

[0110] The optimization of multiple devices based on the target anomaly report includes:

[0111] Obtain abnormal data from the target abnormality report, obtain the abnormal device corresponding to the abnormal data and report it to the cloud platform, so that the cloud platform can issue a maintenance notice based on the abnormal data.

[0112] Specifically, the identified abnormal device information (including abnormal data and basic device information) is reported to the cloud platform. This cloud platform is typically a system that centrally manages, monitors, and analyzes a large number of IoT devices, possessing powerful data processing and communication capabilities. The purpose of reporting abnormal information is to enable the cloud platform to monitor the device's operating status in real time, allowing for timely issuance of maintenance notices.

[0113] Then, the cloud platform issues maintenance notices based on the abnormal data. Specifically, after receiving information about abnormal equipment, the cloud platform automatically generates maintenance notices according to preset rules and procedures. These notices may include:

[0114] Target audience: Department to which the equipment belongs, maintenance team, or specific repair personnel.

[0115] Notification content: Equipment anomaly information (such as anomaly type and severity), suggested maintenance measures, possible causes of failure, maintenance priority, etc.

[0116] Notification methods include email, SMS, app push notifications, and phone calls, ensuring that maintenance personnel receive notifications promptly.

[0117] Maintenance period: Set reasonable maintenance time limits based on the severity and urgency of the abnormality.

[0118] Furthermore, the cloud platform can track the execution of maintenance tasks, including start and end times, personnel involved, and results. This information is crucial for evaluating maintenance efficiency, optimizing processes, and developing subsequent equipment maintenance strategies.

[0119] Through the above steps, optimizing multiple devices based on the target anomaly report not only enables the timely detection and reporting of the abnormal devices, but also ensures the timely release of maintenance notices and the effective execution of maintenance tasks through the support of the cloud platform, thereby improving the reliability and energy efficiency of multi-device linkage control in the Internet of Things.

[0120] Furthermore, this embodiment also includes a network disconnection reconnection strategy:

[0121] If a device disconnects due to network issues during rule execution, and then reconnects within the rule's valid time period and meets the triggering conditions, the system can automatically resume rule execution and report the event normally. If the network disconnection and reconnection spans the rule's valid time period, the system will no longer report the event, but will still ensure that the device's actions can be executed normally. This network disconnection and reconnection strategy ensures the system's stability and reliability.

[0122] In summary, this embodiment provides an energy consumption statistical optimization method for device linkage control. When performing multi-device linkage control, a target rule is first obtained. Based on this target rule, multi-device linkage control is performed. When the target environmental information meets the triggering condition of the target rule, the target device is instructed to execute a target action. When the target device executes the target action, the target event information of the target device is recorded in a target record table. The target event information includes the target event timestamp, event duration, and energy consumption result. Finally, the multi-device system is optimized based on the target record table. This embodiment provides users with an energy consumption statistical optimization method for device linkage control, solving the problem that existing technologies are limited to collecting, analyzing, and optimizing energy consumption data for individual devices, lacking a comprehensive consideration of overall energy consumption under device linkage control. In this embodiment, by recording the target event information of the target device in a target record table and optimizing multiple devices based on the target record table, a more reasonable device operation strategy can be formulated. This allows for further optimization of energy consumption in the linkage control between devices, extends the service life of devices, reduces the cost of replacing devices, and improves user experience and management efficiency.

[0123] It should be understood that although the steps in the flowcharts shown in the accompanying drawings are displayed sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of the steps in this invention, and these steps can be executed in other orders. Moreover, at least a portion of the steps in this invention may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0124] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program using signal-related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0125] Example 2

[0126] Based on the above embodiments, the present invention also provides an energy consumption statistical optimization device for equipment linkage control, the functional module diagram of which is shown below. Figure 2 As shown, the energy consumption statistics and optimization device for the linkage control of this equipment includes:

[0127] The control module is used to acquire target rules and perform multi-device linkage control based on the target rules, as described in Embodiment 1.

[0128] The execution module is used to instruct the target device to perform a target action when the target environment information meets the triggering conditions of the target rule, as described in Embodiment 1.

[0129] The recording module is used to record the target event information of the target device to the target record table when the target device performs the target action. The target event information includes the target event timestamp, event duration and energy consumption result, as described in Embodiment 1.

[0130] The optimization module is used to optimize multiple devices based on the target record table, as described in Embodiment 1.

[0131] Example 3

[0132] like Figure 3 As shown, based on the above-mentioned energy consumption statistical optimization method for device linkage control, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 3 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0133] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores an energy consumption statistics and optimization program 40 for device linkage control, which can be executed by the processor 10 to implement the energy consumption statistics and optimization method for device linkage control in this application.

[0134] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing energy consumption statistical optimization methods for the device linkage control.

[0135] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a device bus.

[0136] In one embodiment, when the processor 10 executes the energy consumption statistics and optimization program 40 for device linkage control in the memory 20, the following steps are performed:

[0137] Obtain the target rules and perform multi-device linkage control based on the target rules;

[0138] When the target environment information meets the triggering conditions of the target rule, the target device is instructed to perform the target action;

[0139] When the target device performs the target action, the target event information of the target device is recorded in the target record table. The target event information includes the target event timestamp, event duration, and energy consumption result.

[0140] Optimize multiple devices based on the target record table.

[0141] The multi-device linkage control based on the target rule includes:

[0142] Based on the target rules, obtain the number and location of nodes, target triggering conditions, and rule execution logic;

[0143] Multiple devices are controlled in a coordinated manner based on the number and location of the nodes, the target triggering conditions, and the rule execution logic.

[0144] Each node is an independent unit for executing the target rule, used to determine whether the target triggering condition is met based on feedback from local sensors.

[0145] The target rules include:

[0146] The rule-delayed execution function is used to instruct the target device to perform the target action after a delay according to a preset delay time when the target environment information meets the target triggering condition.

[0147] The range-time execution function is used to set the time range in which the rule takes effect, so that the target device executes the target action within the target time range corresponding to the target rule;

[0148] The scheduled execution function is used to set the time point when the rule takes effect, so that the target device sets the start time of the target rule according to the month, week or Cron expression.

[0149] The target rules also include:

[0150] Enhanced rules, which support complex logical configurations for multiple rules, including but not limited to combinations of logical "OR", logical "AND" and logical "NOT".

[0151] The optimization of multiple devices based on the target record table includes:

[0152] The target record table is cleaned to obtain the target data;

[0153] The target data is integrated into a target dataset, which includes multiple sets of device-time period-device status-energy consumption data;

[0154] The target dataset is analyzed to obtain a target anomaly report;

[0155] Optimize multiple devices based on the target anomaly report.

[0156] The optimization of multiple devices based on the target anomaly report includes:

[0157] Obtain abnormal data from the target abnormality report, obtain the abnormal device corresponding to the abnormal data and report it to the cloud platform, so that the cloud platform can issue a maintenance notice based on the abnormal data.

[0158] Example 4

[0159] The present invention also provides a computer-readable storage medium having stored thereon one or more programs that can be executed by one or more processors to implement the steps of the energy consumption statistical optimization method for device linkage control described in the above embodiments.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy consumption statistical optimization in equipment linkage control, characterized in that, The energy consumption statistics optimization method for the equipment linkage control includes: Obtain the target rules and perform multi-device linkage control based on the target rules; When the target environment information meets the triggering conditions of the target rule, the target device is instructed to perform the target action; When the target device performs the target action, the target event information of the target device is recorded in the target record table. The target event information includes the target event timestamp, event duration, and energy consumption result. Optimize multiple devices based on the target record table; The optimization of multiple devices based on the target record table includes: The target record table is cleaned to obtain the target data; The target data is integrated into a target dataset, which includes multiple sets of device-time period-device status-energy consumption data; The target dataset is analyzed to obtain a target anomaly report; Optimize multiple devices based on the target anomaly report.

2. The energy consumption statistical optimization method for equipment linkage control according to claim 1, characterized in that, The multi-device linkage control based on the target rule includes: Based on the target rules, obtain the number and location of nodes, target triggering conditions, and rule execution logic; Multiple devices are controlled in a coordinated manner based on the number and location of the nodes, the target triggering conditions, and the rule execution logic.

3. The energy consumption statistical optimization method for equipment linkage control according to claim 2, characterized in that, Each node is an independent unit for executing the target rule, used to determine whether the target triggering condition is met based on feedback from local sensors.

4. The energy consumption statistical optimization method for equipment linkage control according to claim 1, characterized in that, The target rules include: The rule-delayed execution function is used to instruct the target device to perform the target action after a delay according to a preset delay time when the target environment information meets the target triggering condition. The range-time execution function is used to set the time range in which the rule takes effect, so that the target device executes the target action within the target time range corresponding to the target rule; The scheduled execution function is used to set the time point when the rule takes effect, so that the target device sets the start time of the target rule according to the month, week or Cron expression.

5. The energy consumption statistical optimization method for equipment linkage control according to claim 1, characterized in that, The target rules also include: Enhanced rules, which support complex logical configurations for multiple rules, including combinations of logical "OR", logical "AND", and logical "NOT".

6. The energy consumption statistical optimization method for equipment linkage control according to claim 1, characterized in that, The optimization of multiple devices based on the target anomaly report includes: Obtain abnormal data from the target abnormality report, obtain the abnormal device corresponding to the abnormal data and report it to the cloud platform, so that the cloud platform can issue a maintenance notice based on the abnormal data.

7. An energy consumption statistical optimization device for equipment linkage control, characterized in that, The device includes: The control module is used to acquire target rules and perform multi-device linkage control based on the target rules; The execution module is used to instruct the target device to perform the target action when the target environment information meets the triggering conditions of the target rule; The recording module is used to record the target event information of the target device to the target record table when the target device performs the target action. The target event information includes the target event timestamp, event duration, and energy consumption result. The optimization module is used to optimize multiple devices based on the target record table; The optimization of multiple devices based on the target record table includes: The target record table is cleaned to obtain the target data; The target data is integrated into a target dataset, which includes multiple sets of device-time period-device status-energy consumption data; The target dataset is analyzed to obtain a target anomaly report; Optimize multiple devices based on the target anomaly report.

8. A smart terminal, characterized in that, The smart terminal includes a memory, a processor, and a device linkage control energy consumption statistics optimization program stored in the memory and executable on the processor. When the device linkage control energy consumption statistics optimization program is executed by the processor, it implements the steps of the device linkage control energy consumption statistics optimization method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an energy consumption statistics optimization program for device linkage control. When the energy consumption statistics optimization program for device linkage control is executed by a processor, it implements the steps of the energy consumption statistics optimization method for device linkage control as described in any one of claims 1-6.

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