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

Abstract

The invention discloses an energy consumption statistical optimization method and device for equipment linkage control, a terminal and a medium. The method comprises the steps of obtaining a target rule, and performing multi-device linkage control based on the target rule; when the target environment information meets the triggering condition of the target rule, indicating target equipment to execute a target action; when the target device executes the target action, target event information of the target device is recorded to a target record table, and the target event information comprises a target event timestamp, an event duration and an energy consumption result; and optimizing the multiple devices based on the target record table. According to the method, the problem that overall consideration of overall energy consumption under equipment joint control is lacked in the prior art can be solved, a more reasonable equipment operation strategy is formulated, linkage control between the equipment can further optimize energy consumption, the service life of the equipment is prolonged, and user experience and management efficiency are improved.

Description

Technical Field

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

[0002] Currently, Internet of Things (IoT) technology has been widely used in various smart devices and systems, realizing the interconnection and collaboration between devices. However, in terms of energy consumption statistics optimization, most existing methods are still limited to the collection, analysis and optimization of energy consumption data of a single device, lacking a comprehensive consideration of the overall energy consumption under the joint control of the equipment.

[0003] A major problem caused by this limitation is that even if the energy consumption of a single device is optimized, in the device joint control scenario, the overall energy consumption may not reach the optimal state due to the mutual influence and collaborative work between devices. Therefore, a method is needed to statistically optimize the energy consumption under the device joint control to better achieve the goal of energy saving and emission reduction.

[0004] Therefore, the prior art 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 statistical optimization method, device, terminal and medium for equipment linkage control, aiming to solve the problem that the prior art is still limited to the collection, analysis and optimization of energy consumption data of a single device, and lacks comprehensive consideration of the overall energy consumption under the equipment linkage control.

[0006] In order to achieve the above object, the first aspect of the present invention provides an energy consumption statistics optimization method for device linkage control, wherein the energy consumption statistics optimization method for device linkage control includes:

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

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

[0009] When the target device executes the target action, the target event information of the target device is recorded in the target record table, wherein the target event information includes the target event timestamp, event duration and energy consumption result;

[0010] Multiple devices are optimized based on the target record table.

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

[0012] Acquire the number and location of nodes, target trigger conditions and rule execution logic based on the target rule;

[0013] Based on the number and location of the nodes, the target triggering condition and the rule execution logic, multiple devices are controlled in a linked manner.

[0014] In one implementation, each of the nodes is an independent unit for executing the target rule, and is used to determine whether the target trigger condition is met based on feedback from a local sensor.

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

[0016] A rule delayed execution function, wherein when the target environment information satisfies the target trigger condition, the rule delayed execution function is used to instruct the target device to execute the target action according to a preset delay time;

[0017] A range time execution function, which is used to set a time range for the rule to take effect, so that the target device executes the target action within a 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 execution 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, wherein the enhanced rules support complex logic configuration of multiple rules, including but not limited to the combination of logical "or", logical "and" and logical "not".

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

[0022] Performing data cleaning on the target record table to obtain target data;

[0023] Integrate the target data into a target data set, wherein the target data set includes multiple sets of device-time period-device status-energy consumption data;

[0024] Analyze the target data set to obtain a target abnormality report;

[0025] Multiple devices are optimized based on the target exception report.

[0026] In one implementation, the optimizing multiple devices based on the target exception report includes:

[0027] The abnormal data in the target abnormal report is obtained, and the abnormal equipment corresponding to the abnormal data is obtained and reported to the cloud platform, so that the cloud platform issues a maintenance notice based on the abnormal data.

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

[0029] A control module, used to obtain target rules and perform multi-device linkage control based on the target rules;

[0030] An execution module, used to instruct a target device to execute a target action when the target environment information meets the triggering condition of the target rule;

[0031] A recording module, configured to record target event information of the target device to a target recording table when the target device executes the target action, wherein the target event information includes a target event timestamp, event duration, and energy consumption result;

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

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

[0034] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer storage medium stores one or more programs, and the one or more programs 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 above items.

[0035] Beneficial effects: Compared with the prior art, the present invention provides an energy consumption statistics optimization method, device, terminal and medium for equipment linkage control. In the energy consumption statistics optimization method for equipment linkage control provided by the present invention, when performing multi-device linkage control, the target rule is first obtained, and multi-device linkage control is performed based on the target rule. When the target environment information meets the triggering condition 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, and the target event information includes the target event timestamp, event duration and energy consumption result. Finally, multiple devices are optimized based on the target record table. The present invention provides users with an energy consumption statistics optimization method for equipment linkage control, which solves the problem that the prior art is still limited to the collection, analysis and optimization of energy consumption data of a single device, and lacks overall consideration of the overall energy consumption under equipment linkage control. In the present invention, by recording the target event information of the target device to the target record table and optimizing multiple devices based on the target record table, a more reasonable equipment operation strategy can be formulated, so that the linkage control between devices can further optimize energy consumption, and can also extend the service life of the equipment, reduce the cost of replacing equipment, and improve user experience and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of an embodiment of the method for optimizing energy consumption statistics of equipment linkage control provided by the present invention;

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

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

[0039] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0040] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the 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 refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit 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 (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0042] The energy consumption statistics optimization method for device linkage control provided by the present invention can be applied to a terminal with computing capability, and the terminal can execute the energy consumption statistics optimization method for device linkage control provided by the present invention to optimize the overall energy consumption under device linkage control.

[0043] Embodiment 1

[0044] The energy consumption statistics optimization method for device linkage control provided in this embodiment may be executed by a terminal, and the terminal may be, but is not limited to, an intelligent computer, etc., and an intelligent computer is taken as an example for description below.

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

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

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

[0048] Acquire the number and location of nodes, target trigger conditions and rule execution logic based on the target rule;

[0049] Based on the number and location of the nodes, the target triggering condition and the rule execution logic, multiple devices are controlled in a linked manner.

[0050] Each of the nodes is an independent unit for executing the target rule, and is used to determine whether the target trigger condition is met based on feedback from a local sensor.

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

[0052] Based on these detailed and real-time local sensor data, the rule node can quickly and accurately determine whether the current environment or device status has reached the trigger condition preset 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 within milliseconds, ensuring the timeliness and accuracy of the system response.

[0053] Specifically, after obtaining the number and location of the target rule acquisition nodes, the corresponding rule nodes are deployed in the system based on the number and location information of the nodes provided in the target rule. These rule nodes can be logic judgment points at the software level, or they can be sensors or actuators at the hardware level. The number may be one or more, depending on the complexity and coverage of the rules. 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 the window status, as well as one or more logic judgment nodes (possibly in the smart hub) to process the combination logic of the unmanned state and the window status.

[0054] For example, for complex logic scenarios, multiple sensors are deployed in the scenario, and each sensor is used as a rule node. Each rule node can perform actions independently, such as reading the temperature and determining whether there is someone. After the action is executed, it is judged against the preset conditions, and if the conditions are met, the node state is set to true. Other nodes are judged in turn according to this requirement, and when all nodes meet the conditions, subsequent actions are executed, such as turning off lights and air conditioners.

[0055] Specifically, the user first configures multiple rules through a graphical interface or a programming interface, each rule includes at least one rule node as the rule node, each rule node is associated with at least one sensor or device, and defines the triggering condition and execution action of the rule node. In this embodiment, the task execution method is implemented based on the target rule given by the user.

[0056] Obtaining the target rule is the first step of the entire process, which usually means selecting a specific rule from a preset rule library or a dynamically generated rule set as the operating benchmark. The target rule defines in detail the key information required for subsequent steps, including but not limited to the number and location of nodes, the target trigger condition, and the rule execution logic.

[0057] Specifically, the user configures the target rule 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 trigger conditions and rule execution logic based on the target rule.

[0059] That is to say, firstly, the user-configured rules are parsed based on the target rules, the rule execution logic is generated, and the rule execution logic is stored in the rule database to obtain the number and position of nodes, the target trigger condition and the rule execution logic. Then, multiple devices can be controlled in linkage based on the number and position of nodes, the target trigger condition and the rule execution logic.

[0060] In this embodiment, the target rules include:

[0061] A rule delayed execution function, wherein when the target environment information satisfies the target trigger condition, the rule delayed execution function is used to instruct the target device to execute the target action according to a preset delay time;

[0062] A range time execution function, which is used to set a time range for the rule to take effect, so that the target device executes the target action within a 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 execution time of the target rule according to the month, week or Cron expression.

[0064] Furthermore, the target rule also includes:

[0065] Enhanced rules, wherein the enhanced rules support complex logic configuration of multiple rules, including but not limited to the combination of logical "or", logical "and" and logical "not".

[0066] The rule delay execution function is an advanced automation control mechanism that is flexible and practical, and is designed to precisely control the timing of device operations according to specific conditions. The core of this function is its ability to monitor the information status of the target environment. Once this information meets the preset trigger conditions, it will not immediately trigger the device's action execution, but will wait according to the pre-set delay time, and then instruct the target device to perform the corresponding target action. This design allows the system to control the operation sequence more finely to adapt to various complex application scenarios, such as in smart homes, industrial automation, security monitoring and other fields.

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

[0068] Specifically, the range time execution function allows users to define the start time and end time of the rule to form an effective time window. Within this time window, if other conditions of the target rule (such as environmental detection, state change, etc.) are also met, the target device will perform the corresponding action as instructed by the rule. Once the time exceeds this range, even if other conditions are met, the rule will not trigger the device's action execution.

[0069] In addition, the range time execution function can be combined with other automation rules to create more complex and sophisticated control strategies. For example, combined with light sensors and human motion detectors, the rules can be further refined to automatically adjust the heating or cooling system according to temperature conditions only in rooms with people at night.

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

[0071] The scheduled execution function is also a highly flexible and powerful automatic control feature. It allows users or system administrators to set precise execution time points for specific rules, ensuring that the target device can accurately execute the corresponding actions at the preset time. The core of this function is that it can define the specific time when the rules take effect based on complex time scheduling mechanisms such as months, weeks 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 monthly time setting allows users to specify a certain day or days in a month as the execution day for the rule; the weekly time setting allows users to select a certain day or days in a week, as well as a specific time period in a day as the execution time; and the Cron expression provides a more flexible and powerful time setting method, which allows users to define complex execution plans through a specific string format, including time conditions in multiple dimensions such as minutes, hours, days, months, and days of the week.

[0073] In general, the scheduled execution function provides a powerful time scheduling capability for the automation control system, allowing the system to flexibly adjust the operation timing of the equipment according to actual needs, thereby optimizing resource utilization and improving user experience. Whether it is home automation, industrial automation or other fields, the scheduled execution function plays a vital role.

[0074] Enhanced rules can expand the flexibility and complexity of rule configuration. By supporting complex logic configuration of multiple rules, enhanced rules allow users or system administrators to create combination rules containing multiple logical operations such as logical "OR", logical "AND" and logical "NOT". The combination of these logical operators enables the system to implement more sophisticated and complex control logic based on multiple conditions and variables.

[0075] Logical "OR" allows the rule to trigger an action when any one condition is met, while logical "AND" requires all conditions to be met at the same time to trigger an action. Logical "NOT" is used to invert the state of a condition, that is, trigger an action when the condition is not met, or not trigger 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] Through enhanced rule configuration, the smart home system can more accurately understand the needs of users and provide intelligent services in various complex scenarios. Whether it is home automation, industrial automation or other fields, enhanced rules provide users with more powerful control capabilities and more flexible configuration options, allowing the automation control system to better adapt to various practical application scenarios.

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

[0078] Specifically, when the target environment information meets the specific triggering conditions of the preset target rules, the system will automatically trigger a series of instructions to accurately instruct the corresponding target device to perform the preset target action. This process is the core link of automated control in intelligent systems or Internet of Things (IoT) environments. It realizes intelligent management of device behavior based on real-time monitoring and analysis of environmental information and pre-set rule logic.

[0079] Take the smart home system as an example for specific explanation:

[0080] Assume that the target rule is set to "when the indoor temperature exceeds 28 degrees Celsius, automatically turn on the air conditioner to lower the indoor temperature to 26 degrees Celsius, and turn on the fan to increase air circulation." In this case, the target environment information is the indoor temperature, and the trigger condition is that the temperature value exceeds 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. Then, the system will check whether this information meets the preset target rule trigger conditions. Once it is confirmed that the conditions are 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 fan equipment is instructed to turn on to enhance indoor air circulation and improve the cooling effect.

[0084] Through this series of automated command operations, the smart home system can quickly respond to changes in ambient temperature and provide 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 actual applications, the target rules and the target trigger conditions can also be flexibly set according to the specific needs and preferences of the user. 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 the personnel activities are frequent" to meet the work needs of office personnel and ensure the adequacy of indoor lighting.

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

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

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

[0089] The target event timestamp is an accurate mark that records the time when the event occurred, usually 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, evaluation of response speed, and identification of potential problems.

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

[0091] The energy consumption results reflect the energy consumption of the target device in the process of executing the target action, which may include the consumption and consumption rate of various energy types such as electricity, water, gas, etc. The energy consumption results are an important reference for measuring the energy efficiency of equipment, formulating energy-saving and emission-reduction strategies, and optimizing energy use.

[0092] Take the air conditioning equipment in the smart home system as an example for specific explanation:

[0093] Assume that the target rule is set to "when the indoor temperature exceeds 30 degrees Celsius, 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, 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, "2023-05-18 14:30:00", which indicates the exact time when the air conditioner starts to perform cooling action.

[0096] Event duration: Assuming the air conditioner runs continuously for 2 hours, "120 minutes" means the time from when the air conditioner starts to when the cooling stops.

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

[0098] By recording these target event information, the smart home system can not only monitor the status of the equipment in real time, but also provide users with detailed energy consumption reports, helping users to better understand and manage home energy consumption, so as to make more intelligent energy-saving decisions. At the same time, these data also provide valuable reference for system optimization and equipment upgrades.

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

[0100] The optimizing the target rule based on the target record table includes:

[0101] Performing data cleaning on the target record table to obtain target data;

[0102] Integrate the target data into a target data set, wherein the target data set includes multiple sets of device-time period-device status-energy consumption data;

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

[0104] Multiple devices are optimized based on the target exception report.

[0105] Specifically, firstly, data cleaning is performed on the target record table to remove invalid, erroneous or redundant data to ensure the accuracy of subsequent analysis.

[0106] Specifically, it includes identifying and processing missing values, correcting erroneous data, and removing redundant data. Identifying and processing missing values ​​includes checking whether there are missing data items in the target record table, and choosing to fill (such as using the average, median, etc.) or delete missing records according to the specific situation; correcting erroneous data includes checking and correcting obvious erroneous data, such as unreasonable energy consumption values, timestamp errors, etc.; removing redundant data includes deleting duplicate records or unnecessary fields to reduce the size and complexity of the data set. In this way, the cleaned target data can be obtained, and the target data is more accurate and complete, and suitable for subsequent analysis.

[0107] The cleaned data is then integrated into a structured data set for subsequent analysis and optimization. This includes building a data structure: designing a data structure based on 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 a target data set; data verification: verifying the integrated data to ensure data integrity and consistency. Multiple sets of device-time period-device status-energy consumption data are obtained.

[0108] Finally, through data analysis, the abnormal status and high energy consumption periods of the equipment are identified to provide a basis for optimization, so that multiple devices can be optimized based on the target abnormality report.

[0109] Specifically, it includes preprocessing the target data set, such as data standardization and normalization, to facilitate subsequent analysis, and then performing 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: Calculate indicators such as the average energy consumption and peak energy consumption of each device, and analyze the energy consumption distribution and change trends. After that, the target anomaly report can be generated. Specifically, the anomaly detection results and energy consumption analysis results are integrated into a target anomaly report, including a list of abnormal devices, abnormal time periods, abnormal status descriptions, and energy consumption comparisons, so as to optimize multiple devices based on the target anomaly report.

[0110] The optimizing of multiple devices based on the target abnormality report includes:

[0111] The abnormal data in the target abnormal report is obtained, and the abnormal equipment corresponding to the abnormal data is obtained and reported to the cloud platform, so that the cloud platform issues 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. The cloud platform is usually a system that centrally manages, monitors and analyzes a large number of IoT devices, and it has powerful data processing and communication capabilities. The purpose of reporting abnormal information is to enable the cloud platform to grasp the operating status of the equipment in real time so as to issue maintenance notices in a timely manner.

[0113] Then, the cloud platform issues maintenance notifications based on the abnormal data. Specifically, after receiving abnormal equipment information, the cloud platform will automatically generate maintenance notifications according to preset rules and processes. These notifications may include:

[0114] Notification object: the department to which the equipment belongs, the maintenance team or specific maintenance personnel.

[0115] Notification content: equipment abnormality information (such as abnormality type, abnormality degree), recommended maintenance measures, possible fault causes, maintenance priority, etc.

[0116] Notification method: email, SMS, APP push, phone notification, etc., to ensure that maintenance personnel can receive notifications in time.

[0117] Maintenance period: Set a reasonable maintenance time limit based on the degree and urgency of the abnormality.

[0118] Furthermore, the cloud platform can also track the execution of maintenance tasks, including maintenance start time, end time, maintenance personnel, maintenance results, etc. This information is very important for evaluating maintenance efficiency, optimizing maintenance processes and subsequent equipment maintenance strategies.

[0119] Through the above steps, optimizing multiple devices based on target abnormality reports not only realizes the timely discovery and reporting of the abnormal devices, but also ensures the timely release of maintenance notifications 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, in this embodiment, a network disconnection and reconnection strategy is also included:

[0121] During the rule execution process, if the device is disconnected due to network problems, when it reconnects to the network within the effective time period of the rule and meets the triggering conditions, the system can automatically recover and continue to execute the rule, and report the event normally. If the disconnection and reconnection span the effective time period of the rule, the system will no longer report the event, but ensure that the action on the device can be executed normally. This disconnection and reconnection strategy ensures the stability and reliability of the system.

[0122] In summary, this embodiment provides an energy consumption statistics optimization method for device linkage control. When performing multi-device linkage control, first obtain the target rule, and perform multi-device linkage control based on the target rule. When the target environment information meets the triggering condition of the target rule, instruct the target device to perform the target action. When the target device performs the target action, record the target event information of the target device to the target record table. The target event information includes the target event timestamp, event duration and energy consumption result. Finally, optimize multiple devices based on the target record table. This embodiment provides users with an energy consumption statistics optimization method for device linkage control, which solves the problem that the prior art is still limited to the collection, analysis and optimization of energy consumption data of a single device, and lacks overall consideration of the overall energy consumption under the device linkage control. In this embodiment, by recording the target event information of the target device to the target record table and optimizing multiple devices based on the target record table, a more reasonable device operation strategy can be formulated, so that the linkage control between devices can further optimize energy consumption, and can also extend the service life of the equipment, reduce the cost of replacing equipment, and improve user experience and management efficiency.

[0123] It should be understood that, although the various steps in the flowcharts given in the accompanying drawings of the present invention are displayed in sequence according to the indications of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of the steps in the present invention is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps of the present invention may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0124] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by signaling related 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present 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. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0125] Embodiment 2

[0126] Based on the above embodiments, the present invention also provides an energy consumption statistics optimization device for equipment linkage control, and its functional module diagram is as follows: Figure 2 As shown, the energy consumption statistics optimization device for the equipment linkage control includes:

[0127] A control module, used to obtain target rules and perform multi-device linkage control based on the target rules, as described in the first embodiment;

[0128] An execution module, used to instruct the target device to execute the target action when the target environment information meets the triggering condition of the target rule, as described in the first embodiment;

[0129] A recording module, configured to record target event information of the target device to a target recording table when the target device executes the target action, wherein the target event information includes a target event timestamp, event duration, and energy consumption result, as described in the first embodiment;

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

[0131] Embodiment 3

[0132] like Figure 3 As shown, based on the above-mentioned energy consumption statistical optimization method of device linkage control, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 3 Only some components of the terminal are shown, but 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 of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores an energy consumption statistics optimization program 40 for device linkage control, and the energy consumption statistics optimization program 40 for device linkage control can be executed by the processor 10, thereby realizing the energy consumption statistics optimization method for device linkage control in the present 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 the program code or process data stored in the memory 20, such as executing the energy consumption statistical optimization method of 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, an OLED (Organic Light-Emitting Diode) touch device, etc. 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 optimization program 40 for device linkage control in the memory 20, the following steps are implemented:

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

[0138] When the target environment information meets the triggering condition of the target rule, instruct the target device to execute the target action;

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

[0140] Multiple devices are optimized based on the target record table.

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

[0142] Acquire the number and location of nodes, target trigger conditions and rule execution logic based on the target rule;

[0143] Based on the number and location of the nodes, the target triggering condition and the rule execution logic, multiple devices are controlled in a linked manner.

[0144] Each of the nodes is an independent unit for executing the target rule, and is used to determine whether the target trigger condition is met based on feedback from a local sensor.

[0145] The target rules include:

[0146] A rule delayed execution function, wherein when the target environment information satisfies the target trigger condition, the rule delayed execution function is used to instruct the target device to execute the target action according to a preset delay time;

[0147] A range time execution function, which is used to set a time range for the rule to take effect, so that the target device executes the target action within a 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 execution time of the target rule according to the month, week or Cron expression.

[0149] The target rules also include:

[0150] Enhanced rules, wherein the enhanced rules support complex logic configuration of multiple rules, including but not limited to the combination of logical "or", logical "and" and logical "not".

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

[0152] Performing data cleaning on the target record table to obtain target data;

[0153] Integrate the target data into a target data set, wherein the target data set includes multiple sets of device-time period-device status-energy consumption data;

[0154] Analyze the target data set to obtain a target abnormality report;

[0155] Multiple devices are optimized based on the target exception report.

[0156] The optimizing of multiple devices based on the target abnormality report includes:

[0157] The abnormal data in the target abnormal report is obtained, and the abnormal equipment corresponding to the abnormal data is obtained and reported to the cloud platform, so that the cloud platform issues a maintenance notice based on the abnormal data.

[0158] Embodiment 4

[0159] The present invention also provides a computer-readable storage medium on which one or more programs are stored. The one or more programs 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 embodiment.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing energy consumption statistics of equipment linkage control, characterized in that: The energy consumption statistics optimization method of the equipment linkage control includes: Obtain target rules, and perform multi-device linkage control based on the target rules; When the target environment information meets the triggering condition of the target rule, instruct the target device to execute the target action; When the target device executes the target action, the target event information of the target device is recorded in the target record table, wherein the target event information includes the target event timestamp, event duration and energy consumption result; Multiple devices are optimized based on the target record table.

2. The energy consumption statistics optimization method for equipment linkage control according to claim 1 is characterized in that: The multi-device linkage control based on the target rule includes: Acquire the number and location of nodes, target trigger conditions and rule execution logic based on the target rule; Based on the number and location of the nodes, the target triggering condition and the rule execution logic, multiple devices are controlled in a linked manner.

3. The energy consumption statistics optimization method for equipment linkage control according to claim 2 is characterized in that: Each of the nodes is an independent unit for executing the target rule, and is used to determine whether the target trigger condition is met based on feedback from a local sensor.

4. The energy consumption statistics optimization method for equipment linkage control according to claim 1 is characterized in that: The target rules include: A rule delayed execution function, wherein when the target environment information satisfies the target trigger condition, the rule delayed execution function is used to instruct the target device to execute the target action according to a preset delay time; A range time execution function, which is used to set a time range for the rule to take effect, so that the target device executes the target action within a 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 execution time of the target rule according to the month, week or Cron expression.

5. The energy consumption statistics optimization method for equipment linkage control according to claim 1 is characterized in that: The target rules also include: Enhanced rules, wherein the enhanced rules support complex logical configuration of multiple rules, including but not limited to the combination of logical "or", logical "and" and logical "not".

6. The method for optimizing energy consumption statistics of equipment linkage control according to claim 1, characterized in that: The optimizing of multiple devices based on the target record table includes: Performing data cleaning on the target record table to obtain target data; Integrate the target data into a target data set, wherein the target data set includes multiple sets of device-time period-device status-energy consumption data; Analyze the target data set to obtain a target abnormality report; Multiple devices are optimized based on the target exception report.

7. The method for optimizing energy consumption statistics of equipment linkage control according to claim 6, characterized in that: The optimizing of multiple devices based on the target abnormality report includes: The abnormal data in the target abnormal report is obtained, and the abnormal equipment corresponding to the abnormal data is obtained and reported to the cloud platform, so that the cloud platform issues a maintenance notice based on the abnormal data.

8. An energy consumption statistics optimization device for equipment linkage control, characterized in that: The device comprises: A control module, used to obtain target rules and perform multi-device linkage control based on the target rules; An execution module, used to instruct a target device to execute a target action when the target environment information meets the triggering condition of the target rule; A recording module, configured to record target event information of the target device to a target recording table when the target device executes the target action, wherein the target event information includes a target event timestamp, event duration, and energy consumption result; The optimization module is used to optimize multiple devices based on the target record table.

9. An intelligent terminal, characterized in that: The intelligent terminal includes a memory, a processor, and an energy consumption statistics optimization program for device linkage control stored in the memory and executable on the processor. When the energy consumption statistics optimization program for device linkage control is executed by the processor, the steps of the energy consumption statistics optimization method for device linkage control as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an energy consumption statistics optimization program for device linkage control, and when the energy consumption statistics optimization program for device linkage control is executed by a processor, the steps of the energy consumption statistics optimization method for device linkage control as described in any one of claims 1-7 are implemented.

Images