CN113283646B - Method and device for controlling equipment operation, storage medium and electronic device - Google Patents
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Abstract
The embodiment of the invention provides a method and a device for controlling equipment to operate, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring historical use detail data of the target device in a preset time period, wherein the historical use detail data comprises historical time for using the target device and a historical index parameter of the target device; predicting a predicted usage time of the target device and a predicted index parameter of the usage target device based on the historical usage detail data; determining a runtime of the target device based on the expected usage time, wherein the runtime is a time prior to the expected usage time; and the control target equipment operates according to the predicted index parameters at the operating time. According to the invention, the problem of poor user experience caused by the fact that equipment needs to be manually controlled to operate in the related technology is solved, the effect of automatically controlling the operation of the equipment is achieved, and the user experience is improved.
Description
Technical Field
The embodiment of the invention relates to the field of communication, in particular to a method and a device for controlling equipment to operate, a storage medium and an electronic device.
Background
When the control device is operated, the control device is usually operated when the device is used, and the following description takes the device as a water heater as an example:
when the gas water heater without the hot water circulating system is used for bathing, a user generally needs to wait for hot water to flow out and wait for a section of water in a pipeline to be wasted. The thermal cycle function in the gas water heater can avoid the problem that the hot water needs to wait for a long time due to the overlong pipeline, but the function needs to be started in advance by a user when the user uses the gas water heater every time, and the gas water heater is very inconvenient.
Therefore, the problem that the user experience is poor due to the fact that the operation of the equipment needs to be controlled manually exists in the related art.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method and a device for controlling equipment to operate, a storage medium and an electronic device, which are used for at least solving the problem of poor user experience caused by the fact that the equipment needs to be manually controlled to operate in the related art.
According to an embodiment of the present invention, there is provided a method of controlling an operation of an apparatus, including: acquiring historical use detail data of target equipment in a preset time period, wherein the historical use detail data comprises historical time for using the target equipment and historical index parameters for using the target equipment; predicting a predicted usage time of the target device and a predicted indicator parameter for using the target device based on the historical usage detail data; determining a runtime of the target device based on the projected time of use, wherein the runtime is a time prior to the projected time of use; and controlling the target equipment to operate according to the predicted index parameter at the operation time.
According to another embodiment of the present invention, there is provided an apparatus for controlling an operation of a device, including: the acquisition module is used for acquiring historical use detail data of target equipment in a preset time period, wherein the historical use detail data comprises historical time for using the target equipment and a historical index parameter for using the target equipment; a prediction module for predicting a predicted usage time of the target device and a predicted index parameter for using the target device based on the historical usage detail data; a determination module to determine a runtime of the target device based on the projected time of use, wherein the runtime is a time prior to the projected time of use; and the control module is used for controlling the target equipment to operate according to the predicted index parameter at the operation time.
According to yet another embodiment of the invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, performs the steps of the method as set forth in any one of the above.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, historical use detail data of the target equipment in a preset time is acquired, the predicted use time of the target equipment is predicted according to the historical use detail data, the predicted index parameter of the target equipment is used, the running time of the target equipment is determined according to the predicted use time, and the target equipment is controlled to run according to the predicted index parameter at the running time. The running time of the target equipment can be determined according to the historical use detail data, and the target equipment is controlled to run at the running time, so that the problem of poor user experience caused by the fact that the equipment needs to be manually controlled to run in the related technology can be solved, the effect of automatically controlling the equipment to run is achieved, and the user experience is improved.
Drawings
Fig. 1 is a block diagram of a hardware configuration of a mobile terminal of a method of controlling an apparatus according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of controlling operation of a device according to an embodiment of the present invention;
FIG. 3 is a flowchart of predicting an expected usage time of a target device and an expected index parameter of the target device for use based on historical usage detail data, according to an exemplary embodiment of the invention;
FIG. 4 is a flowchart of determining valid particulars included in historical usage particulars data in accordance with an exemplary embodiment of the present invention;
FIG. 5 is a flowchart one of determining valid schedule data included in historical usage schedule data based on a second time period in accordance with an exemplary embodiment of the present invention;
FIG. 6 is a flowchart II of determining valid schedule data included in historical usage schedule data based on a second time period, according to an exemplary embodiment of the present invention;
FIG. 7 is a flowchart of predicting an expected usage time of a target device and an expected metric parameter of using the target device based on valid detail data, according to an exemplary embodiment of the present invention;
FIG. 8 is a flowchart of a control-target device operating at runtime according to a projected index parameter, according to an exemplary embodiment of the present invention;
FIG. 9 is a flowchart of a method of controlling operation of a device according to an exemplary embodiment of the present invention;
FIG. 10 is a logic diagram of a method of controlling the operation of a device in accordance with an exemplary embodiment of the present invention;
fig. 11 is a block diagram of an apparatus for controlling an operation of a device according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking the operation on the mobile terminal as an example, fig. 1 is a hardware structure block diagram of the mobile terminal of a method for controlling the operation of the device according to the embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those of ordinary skill in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the method for controlling the operation of the device in the embodiment of the present invention, and the processor 102 executes the computer programs stored in the memory 104 to thereby execute various functional applications and data processing, i.e., to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices via a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, a method for controlling an apparatus to operate is provided, and fig. 2 is a flowchart of a method for controlling an apparatus to operate according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, obtaining historical use detail data of a target device in a preset time period, wherein the historical use detail data comprises historical time for using the target device and a historical index parameter for using the target device;
step S204, predicting the predicted use time of the target equipment and the predicted index parameter of the target equipment based on the historical use detail data;
step S206, determining the running time of the target equipment based on the expected using time, wherein the running time is the time before the expected using time;
and step S208, controlling the target equipment to operate according to the predicted index parameter at the operation time.
In the above embodiment, the target device may be a smart home device such as a water heater and an air conditioner, and the historical usage detail data may be usage detail data of the target device in 30 days in the history. The historical usage particulars data may be historical water heater usage data for 30 days when the target device is a water heater, and historical usage particulars data may be historical air conditioner usage data for 30 days when the target device is an air conditioner. I.e. the predetermined period of time may be 30 days of history. Of course, the predetermined period of time may also be 7 days, 15 days, a quarter, etc., of the history, which is not limited by the present invention. The historical use detail data comprises the use historical time and the historical index parameters of the target device, and the expected use time and the expected index parameters of the target device are predicted by learning the historical use detail data. After determining the expected usage time, the target device may be operated in advance, i.e., at run time according to the expected index parameter. The operation time may be a time before the expected usage time, for example, the first 5min, 10min, 20min, etc. of the expected usage time. The operation time may be determined according to parameters such as power of the target device, which is not limited in the present invention.
It should be noted that the predetermined time period may further include a predetermined sub-time period, where the predetermined sub-time period is a fixed time period per day, for example, 6. The predetermined sub-period may be determined according to the habit of the user. Determining that the user often uses the target device at 6. That is, the usage detail data of the whole day may be acquired within a predetermined time, a predetermined sub-period in which the target device is frequently used may be determined through machine learning, and when the expected usage time and the expected index parameter are determined later, only the data within the predetermined sub-period may be acquired.
In the embodiment, when the target equipment is the water heater, the water consumption habit of the user can be learned, external factors which possibly influence the water consumption habit of the user are integrated, week and season factors and the like are merged into the prediction algorithm, the water consumption time of the user is accurately predicted, the hot water circulation function is started for the user in advance, the user experience is improved, and meanwhile the final purpose of energy waste is achieved.
In the above embodiment, the target device may bind the APP in the terminal device of the user, upload the historical usage detail data to the server through the WIFI module, and subscribe the big data kafka to the message data for big data learning. The WIFI module uploads the historical use detail data to the server according to a preset time period. The predetermined time period may include 1 day, 2 days, 7 days, etc.
Optionally, the execution subject of the above steps may be a background processor, a server, or other devices with similar processing capabilities, and may also be a machine integrated with at least a data processing device, where the data processing device may include a terminal such as a computer, a mobile phone, and the like, but is not limited thereto.
According to the invention, historical use detail data of the target equipment in a preset time is acquired, the predicted use time of the target equipment is predicted according to the historical use detail data, the predicted index parameter of the target equipment is used, the running time of the target equipment is determined according to the predicted use time, and the target equipment is controlled to run according to the predicted index parameter at the running time. The running time of the target equipment can be determined according to the historical use detail data, and the target equipment is controlled to run at the running time, so that the problem of poor user experience caused by the fact that the equipment needs to be manually controlled to run in the related technology can be solved, the effect of automatically controlling the equipment to run is achieved, and the user experience is improved.
In an exemplary embodiment, a flow chart for predicting the expected usage time of the target device and the expected target parameter using the target device based on the historical usage detail data may be seen in fig. 3, as shown in fig. 3, the flow chart comprising:
step S302, determining valid detail data included in the historical usage detail data, where the valid detail data is usage data of the target device in a target usage state;
step S304, predicting the predicted use time of the target device and the predicted index parameter of the target device based on the effective detail data.
In the above embodiment, the historical usage detail data may be data of each day in a predetermined time, that is, the historical usage detail data includes invalid usage data, and therefore, the invalid usage data included in the historical usage detail data may be eliminated to determine valid detail data, where the valid detail data represents data generated when the target device is in a target usage state, and the target usage state may include a continuous usage state, an intermittent usage state, and the like. After the effective detail data are determined, the estimated use time and the estimated index parameters are determined according to the effective detail data.
In the above-described embodiment, the historical usage detail data may be determined according to the state of the target device. For example, when the target device is a water heater, data included in the usage data of the water heater when the water heater is in a heating state and a state with water flow rate at the same time may be determined as the historical usage detail data. Table 1 shows usage data of the target device reported by the target device, and historical usage detail data may be determined according to an attribute value, i.e., a state, reported by the device. Namely, the device reports the attribute value flameStatus = true and flowStatus = true simultaneously, and considers that the user uses water; and determining the data in the period of time as historical use detail data.
TABLE 1
| flameStatus (flame) | flowStatus (Water flow) | Reporting time | Description of the invention |
| False | False | 11:23:35 | Time of water consumption to no effect |
| True | True | 11:23:39 | Water use start time point |
| True | True | 11:23:42 | Effective water spot |
| True | True | 11:23:43 | End point of Water consumption |
| False | False | 11:23:45 | Water point without effect |
| False | True | 11:23:47 | Water point without effect |
| False | False | 11:23:48 | Water point without effect |
| True | True | 11:23:52 | Start point of water consumption |
In an exemplary embodiment, a flowchart for determining valid detail data included in the historical usage detail data may be found in FIG. 4, as shown in FIG. 4, comprising:
a step S402 of determining a first time period for stopping using the target device based on the historical usage detail data;
step S404, determining a first time length for stopping using the target device each time based on the first time period;
step S406, determining a second time length which is included in the first time length and is longer than a first preset time length, and determining a second time period corresponding to the second time length;
step S408 of determining the valid detail data included in the historical usage detail data based on the second time period.
In the above embodiment, a first time period during which the target device is stopped to be used may be determined according to the historical usage detail data, a first time duration corresponding to the first time period may be determined, a second time duration which is included in the first time duration and longer than a first preset time duration and is corresponding to the second time duration may be determined, and the valid detail data may be determined according to the second time duration. For example, the historical usage detail data is usage detail data for a certain day, the usage start time, the stop time, and the status of the device are described in detail in the usage detail data (for example, when the target device is a water heater, the status of the device may be a heating status, a heat retention status, or the like, and when the target device is an air conditioner, the status of the device may be a cooling status, a heating status, a dehumidifying status, a temperature, or the like), and the valid detail data may be determined based on the start time, the stop time, the length of time of use started, and the time of stop of use of the device. The first preset time period may be 3min and 5min (the value is only an exemplary illustration, and the first preset time period may also be 10min and 15min, which is not limited in the present invention).
In an exemplary embodiment, a flowchart for determining the valid detail data included in the historical usage detail data based on the second time period may refer to fig. 5, as shown in fig. 5, which includes:
step S502, under the condition that the second time period comprises a time period, determining a third time period from the beginning of using the target device to the beginning time of the second time period before the second time period;
step S504, determining a second duration of the third time period;
step S506, when the second time duration is greater than a second preset time duration, or the time duration of any one sub-time duration included in the second time duration is greater than the second preset time duration, determining the detail data corresponding to the third time duration as the valid detail data, where the sub-time duration is a time duration included in the third time duration and used by the target device.
In the above embodiment, when the second time period only includes one time period, that is, when only one time period in which the target device is stopped being used is included in the historical usage detail data exceeds the first predetermined time period, the historical usage detail data before the second time period is acquired, the time period is determined as the third time period, and when the total time length of each time period included in the third time period is greater than the second predetermined time length, or any one of the usage time lengths included in the third time period is greater than the second predetermined time length, the detail data corresponding to the third time period is determined as the valid detail data. The third time period may include a time period for using the target device and a time period for stopping using the target device, and a duration of the time period for stopping using the target device is less than a first preset duration. The second preset time period may be 10min and 15min (this value is only an exemplary illustration, and the first preset time period may also be 20min and 25min, which is not limited in the present invention).
In an exemplary embodiment, a flowchart for determining the valid detail data included in the historical usage detail data based on the second time period may refer to fig. 6, as shown in fig. 6, the flowchart including:
step S602, in a case where a plurality of time periods are included in the second time period, determining a fourth time period and a fifth time period which are adjacent to each other and included in the second time period, where the fifth time period is a time period before the fourth time period;
step S604, determining a sixth time period for using the target device and a seventh time period for stopping using the target device between the fourth time period and the fifth time period, wherein the duration of the seventh time period is less than the first preset duration;
step S606, determining the ending time of the fifth time period and the starting time of the fourth time period as valid time periods when the sixth time period comprises a time period with a time length larger than a third preset time length or the sum of the time lengths of the sixth time period and the seventh time period is larger than a fourth preset time length;
step S608, determining the detail data corresponding to the valid time period as the valid detail data.
In the above embodiment, the historical usage detail data can be seen in table 2, where T: represents the water start time point, t: represents the water use end time point; a represents a water use time period, ai = Ti-Ti, a1= T1-T1, a2= T2-T2 \8230; respectively, represents a water use time period of the ith time (i.e., a time period in which the target device is used); b represents the water stopping time, bi = Ti +1-Ti; the method can be subdivided into b1= T2-T1, b2= T3-T2 \8230, and if bi is less than or equal to 5min and any one of { a1, a2, \8230;, ai is more than 10min or a1+ b1+ a2+ b2 \8230; + ai is more than 10min }, the effective water using time period is T1-ti. When bi is more than 5min, the time period ti to T (i + 1) is an invalid water using time period, and the next valid water using time period (corresponding to the valid time period) is calculated from the next water boiling time point.
TABLE 2
In an exemplary embodiment, a flow chart for predicting the expected usage time of the target device and the expected metric parameter of using the target device based on the valid detail data may refer to fig. 7, as shown in fig. 7, the flow chart comprising:
step S702, analyzing the effective detail data by using a target model to determine the predicted service time and the predicted index parameter data, wherein the target model is trained by machine learning using a plurality of sets of training data, and each set of data in the plurality of sets of training data includes: the historical effective detail data and the use time and index parameters corresponding to the historical effective detail data.
In the embodiment, the big data learns the water consumption behavior of the user according to the detail data reported by the equipment and by using a clustering algorithm and conditions such as relevant week and season, and the next water consumption time of the user is predicted. The big data subscription equipment reports running state data of the cloud, dirty data (namely invalid data) are filtered out through ETL processing, and information such as next water using time and water consumption of a user is predicted by applying a machine learning algorithm and other influence factors. And the output equipment commands to issue a calling interface and related index data for the service party and the big data to use.
In an exemplary embodiment, a flowchart for controlling the target device to operate according to the predicted indicator parameter at the operation time may refer to fig. 8, as shown in fig. 8, where the flowchart includes:
step S802, encrypting the identification information of the target device included in the history use detail data;
step S804, sending the encrypted identification information, the running time, and the expected index parameter to a service party connected to the target device, so as to instruct the service party to control the target device to run according to the expected index parameter at the running time.
In the above embodiment, a flowchart of a method for controlling the operation of a device may be shown in fig. 9, as shown in fig. 9, where the flowchart includes a device layer, an application layer, and a cloud services layer.
Equipment layer: a user binds a gas water heater, equipment operation detailed data are uploaded to the cloud end through a wifi module by an equipment bottom plate, and big data kafka subscribe message data for big data learning;
an application layer: storing a user water usage habit prediction algorithm learned by big data, and enabling a service party to call an equipment issuing command interface provided by the big data to realize a zero-cold-water self-learning function of the equipment and pre-heat the equipment for a user in advance;
big data cloud service layer: and (4) learning the water consumption behavior of the user by using a clustering algorithm and conditions such as associated week and season according to detailed data reported by the equipment by the big data, and predicting the next water consumption time of the user. And outputting the prediction algorithm to an application layer by the big data, and predicting the water consumption time of the equipment and the index data of the equipment. Meanwhile, the big data can be used for subsequent operation work of the big data and a business party after self-learning of relevant index display, intelligent equipment query and the like by the front-end page function display equipment.
The user equipment state report detail comprises information related to user privacy, such as machine edition, MAC and the like of the equipment, and the MAC information is encrypted in interface detail and index statistical information opened to a service party. The prediction algorithm is combined with habits of the user such as water consumption week and season, prediction accuracy is improved, and it is ensured that when the information is pushed to the user and a preheating instruction is issued to the equipment, daily life of the user is not affected, and unnecessary complaints are reduced.
In the above embodiment, referring to fig. 10, a logic diagram of a method for controlling the operation of the device may be shown, and as shown in fig. 10, when the online statuses of the target devices are different, the same clustering algorithm may be used, or different clustering algorithms may be used, and the predicted usage time and the predicted indicator parameter are determined by combining with the week and season rules.
In the foregoing embodiment, when the user using the gas water heater takes a bath, the gas water heater is located at a certain distance from the bath site. When a user uses water, the temperature in the pipe is low, and thus much water is wasted. In order to improve user experience and save energy, the next water using time of the user is predicted based on the big data cloud platform by learning the water using habit of the user, and the water using time is issued to the equipment through the application interface, so that the user is actively heated in advance. The user does not need to take a bath again, so that the time of the user is saved and the bath is more convenient. Thereby improving the water consumption experience of users and achieving the purposes of saving energy and time.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for controlling the operation of the apparatus is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and the description of the device that has been already made is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 11 is a block diagram of a structure of an apparatus for controlling an operation of a device according to an embodiment of the present invention, as shown in fig. 11, the apparatus including:
an obtaining module 1102, configured to obtain historical usage detail data of a target device in a predetermined time period, where the historical usage detail data includes a historical time for using the target device and a historical index parameter for using the target device;
a prediction module 1104 for predicting a predicted usage time of the target device and a predicted index parameter for using the target device based on the historical usage detail data;
a determining module 1106 configured to determine a runtime of the target device based on the expected usage time, wherein the runtime is a time prior to the expected usage time;
a control module 1108 configured to control the target device to operate at the operation time according to the predicted indicator parameter.
In an exemplary embodiment, the prediction module 1104 may predict the expected usage time of the target device and the expected index parameter of using the target device based on the historical usage detail data by: determining valid detail data included in the historical use detail data, wherein the valid detail data is use data when the target device is in a target use state; predicting an expected usage time of the target device and an expected index parameter of using the target device based on the valid detail data.
In an exemplary embodiment, the prediction module 1104 may determine valid detail data included in the historical usage detail data by: determining a first time period for ceasing use of the target device based on the historical usage details data; determining a first duration for each cessation of use of the target device based on the first time period; determining a second time length which is included in the first time length and is longer than a first preset time length, and determining a second time period corresponding to the second time length; determining the valid particulars data included in the historical usage particulars data based on the second time period.
In an exemplary embodiment, the prediction module 1104 may determine the valid schedule data included in the historical usage schedule data based on the second time period by: determining a third time period from the start of the use of the target device to the start time of the second time period before the second time period when the second time period comprises one time period; determining a second duration of the third time period; and when the second time length is greater than a second preset time length, or the time length of any sub-time length included in the second time length is greater than the second preset time length, determining the detail data corresponding to the third time length as the effective detail data, wherein the sub-time length is the time length of using the target device included in the third time length.
In an exemplary embodiment, the prediction module 1104 may determine the valid schedule data included in the historical usage schedule data based on the second time period by: in a case where a plurality of time periods are included in the second time period, determining a fourth time period and a fifth time period which are adjacent and included in the second time period, wherein the fifth time period is a time period before the fourth time period; determining a sixth time period for using the target device and a seventh time period for stopping using the target device between the fourth time period and the fifth time period, wherein the duration of the seventh time period is less than the first preset duration; determining the end time of the fifth time period and the start time of the fourth time period as valid time periods under the condition that the sixth time period comprises a time period with the duration being greater than a third preset time period or the sum of the durations of the sixth time period and the seventh time period is greater than a fourth preset time period; and determining the detail data corresponding to the effective time period as the effective detail data.
In an exemplary embodiment, the prediction module 1104 may predict the expected usage time of the target device and the expected indicator parameter of the usage of the target device based on the valid detail data by: analyzing the effective detail data by using a target model to determine the predicted use time and the predicted index parameter data, wherein the target model is trained by machine learning by using a plurality of sets of training data, and each set of data in the plurality of sets of training data comprises: the historical effective detail data and the use time and index parameters corresponding to the historical effective detail data.
In an exemplary embodiment, the control module 1108 may control the target device to operate at the runtime according to the predicted indicator parameter by: encrypting the identification information of the target device included in the historical use detail data; and sending the encrypted identification information, the running time and the expected index parameter to a service party connected with the target equipment to indicate the service party to control the target equipment to run according to the expected index parameter at the running time.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as set forth in any of the above.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention further provide an electronic device, comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the foregoing embodiments and exemplary implementations, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing device, they may be centralized in a single computing device or distributed across a network of multiple computing devices, and they may be implemented in program code that is executable by a computing device, such that they may be stored in a memory device and executed by a computing device, and in some cases, the steps shown or described may be executed in an order different from that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps therein may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method of controlling operation of a device, comprising:
acquiring historical use detail data of target equipment in a preset time period, wherein the historical use detail data comprises historical time for using the target equipment and a historical index parameter for using the target equipment;
predicting a predicted usage time of the target device and a predicted indicator parameter for using the target device based on the historical usage detail data;
determining a runtime of the target device based on the projected time of use, wherein the runtime is a time prior to the projected time of use;
controlling the target equipment to operate according to the predicted index parameter at the operation time;
predicting an expected usage time of the target device and an expected index parameter of usage of the target device based on the historical usage detail data includes: determining valid detail data included in the historical use detail data, wherein the valid detail data is use data when the target device is in a target use state; predicting a predicted usage time of the target device and a predicted indicator parameter for using the target device based on the valid detail data;
determining valid particulars data included in the historical usage particulars data includes: determining a first time period for ceasing use of the target device based on the historical usage details data; determining a first duration for each cessation of use of the target device based on the first time period; determining a second time length which is included in the first time length and is longer than a first preset time length, and determining a second time period corresponding to the second time length; determining the valid schedule data included in the historical usage schedule data based on the second time period;
determining the valid schedule data included in the historical usage schedule data based on the second time period includes: determining a third time period from the start of the use of the target device to the start time of the second time period before the second time period when the second time period comprises one time period; determining a second duration of the third time period; and when the second time length is greater than a second preset time length, or the time length of any sub-time length included in the second time length is greater than the second preset time length, determining the detail data corresponding to the third time length as the effective detail data, wherein the sub-time length is the time length of using the target device included in the third time length.
2. The method of claim 1, wherein determining the valid schedule data included in the historical usage schedule data based on the second time period comprises:
in a case where a plurality of time periods are included in the second time period, determining a fourth time period and a fifth time period which are adjacent and included in the second time period, wherein the fifth time period is a time period before the fourth time period;
determining a sixth time period for using the target device and a seventh time period for stopping using the target device between the fourth time period and the fifth time period, wherein the duration of the seventh time period is less than the first preset duration;
determining the end time of the fifth time period and the start time of the fourth time period as valid time periods under the condition that the sixth time period comprises a time period with the duration being greater than a third preset time period or the sum of the durations of the sixth time period and the seventh time period is greater than a fourth preset time period;
and determining the detail data corresponding to the effective time period as the effective detail data.
3. The method of claim 1, wherein predicting an expected usage time of the target device and an expected metric parameter of using the target device based on the valid detail data comprises:
analyzing the effective detail data by using a target model to determine the predicted using time and the predicted index parameter data, wherein the target model is trained by machine learning by using a plurality of sets of training data, and each set of the plurality of sets of training data comprises: the historical effective detail data and the use time and index parameters corresponding to the historical effective detail data.
4. The method of claim 1, wherein controlling the target device to operate at the run time in accordance with the projected metric parameter comprises:
encrypting the identification information of the target device included in the historical use detail data;
and sending the encrypted identification information, the running time and the expected index parameter to a service party connected with the target equipment to instruct the service party to control the target equipment to run according to the expected index parameter at the running time.
5. An apparatus for controlling operation of a device, comprising:
the acquisition module is used for acquiring historical use detail data of target equipment in a preset time period, wherein the historical use detail data comprises historical time for using the target equipment and a historical index parameter for using the target equipment;
a prediction module for predicting a predicted usage time of the target device and a predicted indicator parameter for using the target device based on the historical usage detail data;
a determination module to determine a runtime of the target device based on the projected time of use, wherein the runtime is a time prior to the projected time of use;
the control module is used for controlling the target equipment to operate according to the predicted index parameter at the operating time;
the prediction module enables prediction of a projected usage time of the target device and a projected metric parameter of usage of the target device based on the historical usage profile data by: determining valid detail data included in the historical use detail data, wherein the valid detail data is use data when the target device is in a target use state; predicting a predicted usage time of the target device and a predicted indicator parameter for using the target device based on the valid detail data;
the prediction module enables determining valid detail data included in the historical usage detail data by: determining a first time period for ceasing use of the target device based on the historical usage details data; determining a first duration for each cessation of use of the target device based on the first time period; determining a second time length which is included in the first time length and is longer than a first preset time length, and determining a second time period corresponding to the second time length; determining the valid schedule data included in the historical usage schedule data based on the second time period;
the prediction module enables determining the valid particulars included in the historical usage particulars data based on the second time period by: determining a third time period from the start of using the target device to the start time of the second time period before the second time period when the second time period includes one time period; determining a second duration of the third time period; and when the second time length is greater than a second preset time length, or the time length of any sub-time period included in the second time period is greater than the second preset time length, determining the detail data corresponding to the third time period as the effective detail data, wherein the sub-time period is the time period for using the target device included in the third time period.
6. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method of one of claims 1 to 4.
7. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 4.
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