Intelligent function adjustment method and device, electronic equipment and storage medium

Abstract

The invention discloses an intelligent function adjustment method and device, electronic equipment and a storage medium, and relates to the technical field of Internet of Things. The method comprises the steps of determining a to-be-started target intelligent function from N intelligent functions based on predicted important values of the N intelligent functions in a next time period; determining P intelligent functions to be closed from the M intelligent functions based on the predicted important values of the M intelligent functions in the next time period and the computing power required by the target intelligent function; and if the predicted important value of the target intelligent function is greater than the total predicted important value of the P intelligent functions, generating an adjustment instruction for indicating that the target intelligent function is turned on and indicating that the P intelligent functions are turned off, so that the NVR adaptively adjusts the intelligent functions.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things technology, and particularly to an intelligent function adjustment method, device, electronic device and storage medium. Background Art

[0002] With the continuous development of intelligent technology, the intelligent level of Network Video Recorder (NVR) is also constantly improving. An NVR usually supports multiple intelligent functions (such as intelligent recognition, intelligent diagnosis, intelligent classification, intelligent analysis, etc.) to meet the monitoring requirements in different scenarios.

[0003] In the actual use process, the computing power of NVR is generally limited and it is unable to enable all intelligent functions simultaneously. It is necessary to manually configure certain fixed intelligent functions for the NVR. However, since the NVR is generally in a dynamic state, if the intelligent functions are configured manually, there is a lack of dynamic adjustment ability in the computing power allocation process and it is unable to adapt to the real-time changing computing power requirements. It may not be able to provide sufficient computing power resources to support more intelligent functions during the peak demand period, and the activation of some useless intelligent functions during the low demand period also causes waste of computing power resources. For example, the intelligent function configured for a certain NVR at the mall entrance is to continuously perform the analysis of the number of people. However, during a certain period at night when the mall is closed, the more required intelligent function is to identify whether there is any intrusion near the mall entrance for subsequent video retrieval.

[0004] Therefore, how to intelligently adjust the intelligent functions of NVR is an urgent problem to be solved currently. Summary of the Invention

[0005] The present application provides an intelligent function adjustment method, device, electronic device and storage medium for the NVR to adaptively adjust the intelligent functions.

[0006] In a first aspect, an intelligent function adjustment method is provided, including:

[0007] Determining a target intelligent function to be enabled from the N intelligent functions based on the predicted importance values of each of the N intelligent functions in the next time period; where N is an integer greater than 0;

[0008] Determining P intelligent functions to be disabled from the M intelligent functions based on the predicted importance values of each of the M intelligent functions in the next time period and the computing power required by the target intelligent function; where M is an integer greater than 0;

[0009] If the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions, generating an adjustment instruction indicating the activation of the target intelligent function and the deactivation of the P intelligent functions.

[0010] In the embodiments of the present application, since the predicted importance value of the intelligent function in the next time period can be predicted, therefore, the target intelligent function that needs to be turned on most in the future can be determined from N kinds of intelligent functions based on this; and since P kinds of intelligent functions to be turned off are determined from M kinds of intelligent functions based on the predicted importance values of the M kinds of intelligent functions in the next time period and the computing power required by the target intelligent function, it can be ensured that there is sufficient computing power to run the target intelligent function in the future; and if the predicted importance value of the target intelligent function is greater than the total predicted importance values of the P kinds of intelligent functions, an adjustment instruction for indicating the turn-on of the target intelligent function and indicating the turn-off of the P kinds of intelligent functions is generated, so as to dynamically adjust the turn-on or turn-off of different intelligent functions in different time periods, and effectively and reasonably utilize the computing power resources of the NVR.

[0011] In some embodiments, before determining the target intelligent function to be turned on from the N kinds of intelligent functions based on the predicted importance values of the N kinds of intelligent functions in the next time period, it further includes:

[0012] Obtain the initial weight of any first intelligent function among the N kinds of intelligent functions;

[0013] Calculate the trigger frequency of the first intelligent function being triggered and turned on in the current time period of the current day;

[0014] Based on the trigger frequency of being triggered and turned on in the current time period of the current day, predict the predicted frequency of the first intelligent function being triggered and turned on in the current time period of the current day;

[0015] Based on the predicted frequency and the initial weight, calculate the predicted importance value of the first intelligent function.

[0016] Through the above method, consider the time dimension in the selection of intelligent functions, calculate the predicted importance values of intelligent functions that should be triggered and turned on in different time periods, and based on this, it can be arranged which intelligent functions will be more valuable to turn on in the future.

[0017] In some embodiments, determining P kinds of intelligent functions to be turned off from the M kinds of intelligent functions based on the predicted importance values of the M kinds of intelligent functions in the next time period and the computing power required by the target intelligent function includes:

[0018] Based on the predicted importance values of the M kinds of intelligent functions, arrange the M kinds of intelligent functions in ascending order to obtain the M kinds of intelligent functions arranged in sequence, and calculate the remaining computing power after turning off one intelligent function in sequence until the remaining computing power is greater than or equal to the computing power required by the target intelligent function.

[0019] In the above manner, by sorting the respective predicted importance values, it is possible to determine exactly how many intelligent functions to turn off so as to maintain the subsequent normal operation of the target intelligent function, and the value (positive gain) generated by the turned-off intelligent functions in the future is also relatively small, effectively utilizing the computing power resources of the device.

[0020] In some embodiments, the method further includes:

[0021] Adding a compensation frequency for the intelligent functions that are not enabled; wherein the compensation frequency is used to compensate the weight of the intelligent functions that are not enabled being selected at the next adjustment time of the intelligent function adjustment.

[0022] In the above manner, adding a compensation frequency for the intelligent functions that are not enabled to prevent some intelligent functions from never being enabled, resulting in waste.

[0023] In some embodiments, the method further includes:

[0024] If the predicted importance value of the target intelligent function is less than or equal to the total predicted importance value of the P intelligent functions, then maintain the enabling action of the M intelligent functions until the next adjustment time of the intelligent function adjustment.

[0025] In a second aspect, there is provided an intelligent function adjustment device, including:

[0026] A first determination module, configured to determine a target intelligent function to be enabled from the N intelligent functions based on the predicted importance values of the N intelligent functions in the next time period respectively; wherein N is an integer greater than 0;

[0027] A second determination module, configured to determine P intelligent functions to be turned off from the M intelligent functions based on the predicted importance values of the M intelligent functions in the next time period respectively, and the computing power required by the target intelligent function; wherein M is an integer greater than 0;

[0028] An adjustment module, configured to, if the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions, generate an adjustment instruction for instructing the enabling of the target intelligent function and for instructing the turning off of the P intelligent functions.

[0029] In a third aspect, there is provided an electronic device, including:

[0030] A memory for storing a computer program; a processor, configured to implement the method according to any one of the first aspect when executing the computer program stored on the memory.

[0031] In a fourth aspect, a computer-readable storage medium is provided, in which a computer program is stored, and when the computer program is executed by a processor, the method described in any one of the first aspects is implemented.

[0032] For the various aspects in the second to fourth aspects above and the possible technical effects that each aspect may achieve, please refer to the technical effects that can be achieved by the above-mentioned first aspect or various possible solutions in the first aspect for description, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Schematic diagram of an application scenario applicable to an embodiment of the present application;

[0034] Figure 2 Flowchart of an intelligent function adjustment method provided by an embodiment of the present application;

[0035] Figure 3 Flowchart of predicting an important value of a computing intelligent function provided by an embodiment of the present application;

[0036] Figure 4 Logical schematic diagram of enabling an intelligent function provided by an embodiment of the present application;

[0037] Figure 5 Structural schematic diagram of an intelligent function adjustment device provided by an embodiment of the present application;

[0038] Figure 6 Structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application. Without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily. And although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0040] In the description and claims of this application and the above-mentioned drawings, the terms "first" and "second" are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products or devices. "Multiple" in this application may mean at least two, for example, it may be two, three or more, and the embodiments of this application do not make any restrictions.

[0041] The following describes exemplary embodiments of this application in conjunction with the accompanying drawings. Various details of the embodiments of this application are included to facilitate understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the disclosure of this application. Similarly, for the sake of clarity and conciseness, the description below omits the description of well-known functions and structures. It should be noted that in the embodiments of this application, certain industry-existing solutions such as certain software, components, models, etc. may be mentioned, and they should be considered exemplary. Their purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0042] First, a brief introduction to the application scenarios applicable to the technical solution of the embodiments of this application will be given below. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and not for limitation. In specific implementation, the technical solution provided by the embodiments of this application can be flexibly applied according to actual needs.

[0043] Figure 1 FIG. is a schematic diagram of an application scenario applicable to the embodiments of this application. As shown in the figure, this application scenario mainly includes: NVR100, network cameras (1, 2, 3,..., M). Among them, NVR100 is respectively connected to the network cameras (1, 2, 3,..., M) to build a large-scale video surveillance system to meet the surveillance requirements in various scenarios. The embodiments of this application do not impose any restrictions on the number of the above devices. As Figure 1 shown, only NVR100 and network cameras (1, 2, 3,..., M) are taken as examples for description. Below, a brief introduction to the above devices and their respective functions will be given.

[0044] Network cameras (1, 2, 3,..., M) are cameras that can be connected to the network and send video streams to the NVR or other video receiving devices through the network to achieve remote video surveillance.

[0045] The NVR100 can be used as a central management device, responsible for receiving, decoding, processing, and storing video streams from network cameras (1, 2, 3, … M). It has multiple intelligent functions such as video display, feature recognition, storage, playback and retrieval, alarm and event management, etc. Further, an intelligent function adjustment device can be mounted on the NCR100, which is used to determine the target intelligent function to be enabled from N intelligent functions based on the predicted importance values of each of the N intelligent functions in the set next time period; determine P intelligent functions to be disabled from M intelligent functions based on the predicted importance values of each of the M intelligent functions in the next time period and the computing power required by the target intelligent function; if the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions, generate an adjustment instruction to indicate the enabling of the target intelligent function and the disabling of the P intelligent functions, so that the NVR can adaptively adjust the enabling or disabling status of the intelligent functions of different network cameras, make more effective use of computing power resources, and provide more effective information for users.

[0046] To further illustrate the technical solutions provided by the embodiments of the present application, the following will be described in detail with reference to the accompanying drawings and specific implementation manners. Although the embodiments of the present application provide method operation steps as shown in the following embodiments or drawings, more or fewer operation steps may be included in the method based on routine or non-creative labor. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. When the method is actually processed or executed by the device, it can be executed or executed in parallel according to the method order shown in the embodiments or drawings.

[0047] Figure 2 It is a flowchart of an intelligent function adjustment method provided by an embodiment of the present application. This process can be executed by an intelligent function adjustment device, which can be implemented in software and / or hardware ways to adaptively adjust the intelligent functions of the NVR. As Figure 2 shown, this process may include the following steps:

[0048] 201: Determine the target intelligent function to be enabled from N intelligent functions based on the predicted importance values of each of the N intelligent functions in the next time period.

[0049] In some embodiments, determining the predicted importance values of each of N (N is an integer greater than 0) intelligent functions in the next time period can be as Figure 3 shown, which exemplarily shows a flowchart for calculating the predicted importance value of an intelligent function.

[0050] 301: Obtain the initial value of any first intelligent function among N intelligent functions.

[0051] In an actual scenario, the various intelligent functions of network cameras may have different values for users. Therefore, corresponding initial weights (denoted as VS[i][j], representing the j-th intelligent function of the i-th network camera) can be configured separately for the various intelligent functions of different network cameras. This initial weight can be user-defined or configured based on historical experience data to distinguish the important differences in the intelligent functions of different network cameras. As shown in Table 1, an example table of the initial weights of the intelligent functions of network cameras is exemplarily shown.

[0052] Table 1: Example Table of Initial Weights of Intelligent Functions of Network Cameras

[0053]

[0054] It should be noted that for the convenience of the following description, in the embodiments of the present application, the first intelligent function is described by taking the intelligent function 2 of network camera 2 in Table 1 as an example. The above Table 1 is just an example. In an actual scenario, there can be more intelligent functions, and the present application does not limit this here.

[0055] 302: Calculate the trigger frequency at which the first intelligent function is triggered and turned on during the current time period of the current day.

[0056] In this step, a three-dimensional table (F[i][j][t]) can be used to record the trigger frequency of the network camera according to the set statistical rules. For example, taking a year as the unit, 1 day can be divided into multiple time periods according to a set time interval (such as 1 hour, half an hour, 2 hours). This three-dimensional table can record the trigger frequency at which the j-th intelligent function of the i-th network camera is triggered and turned on during the t-th time period of a year. Further, for some time periods where the corresponding trigger frequency has not been statistically obtained, the initial value of the table can be assigned 0 or -1, indicating that there is no relevant record.

[0057] Suppose that in the period from 8:30 to 9:00 on January 1st (the 8th time period), the network camera triggered the 2nd intelligent function 3 times. Then F[1][2][8] = 3 / 0.5 = 6 times. When it reaches 8 o'clock on January 1st of the next year, F[1][2][8] is overwritten with the newly calculated trigger frequency.

[0058] 303: Predict the predicted frequency at which the first intelligent function is triggered and turned on during the current time period of the current day based on the trigger frequency at which it is triggered and turned on during the current time period of the current day.

[0059] In some embodiments, the prediction frequency of predicting that the first intelligent function is triggered and turned on in the current time period of the current day may specifically be: based on the trigger frequency and weight of the first intelligent function being triggered and turned on in the current time period of the current day, the trigger frequency and weight of the first intelligent function being triggered and turned on in adjacent time periods of the current time period, and the trigger frequency and weight of the first intelligent function being triggered and turned on in the current time period and adjacent time periods of adjacent days, predict the prediction frequency of the first intelligent function being triggered and turned on in the current time period of the current day.

[0060] For example, taking the three time periods t-1, t, t+1 before and after the current day, the three time periods t-1-24, t-24, t+1-24 at the same time the day before yesterday, and the three time periods t-1+24, t+24, t+1+24 at the same time the day after tomorrow as examples, the weights corresponding to each time point being triggered and turned on are shown in Table 2 below.

[0061] Table 2: Example table of weights for the same intelligent function being triggered and turned on at different time points

[0062] The previous day (-1) The current day (0) The next day (1) Time period t-1 1 2 1 Time period t 2 3 2 Time period t+1 1 2 1

[0063] Further, PF[i][j][t] is obtained by multiplying the parts with values greater than zero in F[i][j][t-1], F[i][j][t], F[i][j][t+1], F[i][j][t-1-24], F[i][j][t-24], F[i][j][t+1-24], F[i][j][t-1+24], F[i][j][t+24], F[i][j][t+1+24] by their respective weights in Table 2 above and then dividing by the sum of the weights.

[0064] It should be noted that the weights in Table 2 above can be set by the user or calculated based on the historical frequencies of the intelligent function being triggered and turned on at different time points. The embodiments of the present application do not limit this here.

[0065] Similarly, for the intelligent functions of different network cameras, the calculation of the prediction importance value can also be performed according to the Figure 3 method shown, and the embodiments of the present application will not repeat the description here.

[0066] 304: Calculate the prediction importance value of the first intelligent function based on the prediction frequency and the initial weight.

[0067] In this step, the prediction importance value of the first intelligent function being triggered and turned on in the next time period can be obtained by multiplying the prediction frequency (PF[i][j][t]) by the initial importance value (VS[i][j]).

[0068] Further, to determine the target intelligent function to be enabled from N intelligent functions, the predicted importance values of each of the N intelligent functions can be sorted, and the intelligent function with the largest predicted importance value can be selected as the target intelligent function to be enabled.

[0069] In the embodiments of the present application, based on Figure 3 the method shown, considerations in the time dimension are made for the selection of intelligent functions, and the predicted importance values for which the intelligent functions should be triggered and enabled in different time periods are calculated. Based on this, it can be determined which intelligent functions will be more valuable to enable in the future.

[0070] 202: Based on the predicted importance values of each of the M intelligent functions in the next time period, and the computing power required for the target intelligent function, determine P intelligent functions to be disabled from the M intelligent functions.

[0071] In this step, M is an integer greater than 0. Specifically, based on the predicted importance values of each of the M intelligent functions in a time period, the M intelligent functions are sorted in ascending order to obtain the M intelligent functions arranged in sequence, and the remaining computing power after disabling each intelligent function in sequence is calculated until the remaining computing power is greater than or equal to the computing power required for the target intelligent function, so as to determine how many intelligent functions to disable specifically, which can maintain the normal operation of the target intelligent function in the future, and the value (positive gain) generated by the disabled intelligent functions in the future is also relatively small, effectively utilizing the computing power resources of the device. As Figure 4 shown, the currently enabled intelligent functions are intelligent function 1, intelligent function 2, intelligent function 3, and intelligent function 4, and the target intelligent function planned to be enabled is intelligent function 5; it is calculated that the predicted important values of intelligent function 1, intelligent function 2, intelligent function 3, and intelligent function 4 in the next time period increase in sequence. Starting from intelligent function 1, each intelligent function is disabled in sequence, and the corresponding remaining computing power is calculated for each disabled intelligent function. If it is assumed that the remaining computing power after disabling intelligent function 4 in sequence is M - m1 - m2 - m3 - m4 >= m5, then intelligent function 5 can be directly enabled subsequently; if it is assumed that the remaining computing power after disabling intelligent function 4 in sequence is M - m1 - m2 - m3 - m4 < m5, then some intelligent functions with smaller predicted importance values need to be continuously disabled in sequence before enabling m5. In Figure 3 the example, the remaining computing power after disabling up to intelligent function 2 can support the normal operation of intelligent function 5. Therefore, first disable up to m2 and then enable m5; where M is the total computing power of the device. Of course, if the remaining computing power of the currently enabled intelligent functions does not need to be disabled to support the operation of intelligent function 5, intelligent function 5 can be directly enabled.

[0072] It should be noted that the M enabled intelligent functions can also calculate their predicted importance values in the next time period according to the method shown above Figure 3 in this application, and the embodiments of the present application will not repeat the description.

[0073] 203: If the predicted importance value of the target intelligent function is greater than the total predicted importance values of P intelligent functions, an adjustment instruction for indicating the activation of the target intelligent function and the deactivation of the P intelligent functions is generated.

[0074] In this step, after generating the adjustment instruction for indicating the activation of the target intelligent function and the deactivation of the P intelligent functions, it can be sent to the corresponding network camera (such as Figure 1 the network camera 2 shown), and the network camera can perform corresponding data capture according to the newly activated intelligent functions.

[0075] In some embodiments, if the predicted importance value of the target intelligent function is greater than the total predicted importance values of P intelligent functions, it further indicates the importance of the target intelligent function that should be activated subsequently. Then, an adjustment instruction for indicating the activation of the target intelligent function and the deactivation of the P intelligent functions can be generated to adaptively adjust the activation or deactivation of different intelligent functions in the future time period.

[0076] In other embodiments, if the predicted importance value of the target intelligent function is less than or equal to the total predicted importance values of P intelligent functions, it indicates that the value brought by the subsequent activation of the target intelligent function is lower than the value brought by the deactivation of the P intelligent functions. Then, the activation actions of M intelligent functions are maintained until the next adjustment time for intelligent function adjustment, and then it transfers to 201 for a new round of judgment. It should be noted that the next adjustment time can be the same as the starting time point of the next time period, or a custom - set time point, or a time point set at intervals of several time periods, and can be flexibly set according to the actual scenario. The embodiments of the present application do not limit this here.

[0077] In some embodiments, a compensation frequency (PEF[i][j]) can also be added to the intelligent functions that have not been activated to compensate for the weight of the intelligent functions that have not been activated being selected at the next adjustment time for intelligent function adjustment, so as to prevent some intelligent functions from never being activated. The compensation frequency can be set to 1 or 2, and the specific value can be set according to the actual scenario. For the intelligent functions with the introduced compensation frequency, when calculating its predicted importance value, PV[i][j][t]=(PF[i][j][t]+PEF[i][j])*VS[i][j].

[0078] In the embodiments of the present application, since the predicted importance value of the intelligent function in the next time period can be predicted, therefore, the target intelligent function that most needs to be enabled in the future can be determined from the N intelligent functions based on this; and then, based on the predicted importance values of the M intelligent functions in the next time period respectively, and the computing power required by the target intelligent function, the P intelligent functions to be closed are determined from the M intelligent functions, which can ensure that there is sufficient computing power to run the target intelligent function in the future; and if the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions, an adjustment instruction for indicating the opening of the target intelligent function and indicating the closing of the P intelligent functions is generated, so as to dynamically adjust the opening or closing of different intelligent functions in different time periods, and effectively and reasonably utilize the computing power resources of the NVR.

[0079] Based on the same technical concept, an intelligent function adjustment device is further provided in the embodiments of the present application, and this device can implement the above intelligent function adjustment method flow in the embodiments of the present application.

[0080] Figure 5 It is a schematic structural diagram of an intelligent function adjustment device provided in the embodiments of the present application. As Figure 5 shown, the device includes: a first determination module 501, a second determination module 502, and an adjustment module 503.

[0081] The first determination module 501 is configured to determine a target intelligent function to be enabled from the N intelligent functions based on the predicted importance values of the N intelligent functions in the next time period respectively; where N is an integer greater than 0.

[0082] The second determination module 502 is configured to determine P intelligent functions to be closed from the M intelligent functions based on the predicted importance values of the M intelligent functions in the next time period respectively, and the computing power required by the target intelligent function; where M is an integer greater than 0.

[0083] The adjustment module 503 is configured to generate an adjustment instruction for indicating the opening of the target intelligent function and indicating the closing of the P intelligent functions if the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions.

[0084] In some embodiments, the first determination module 501 is further configured to:

[0085] Obtain the initial weight of any first intelligent function among the N intelligent functions;

[0086] Calculate the trigger frequency at which the first intelligent function is triggered to be enabled in the current time period of the current day;

[0087] Predict the predicted frequency of the first intelligent function being triggered and enabled in the next time period of the day based on the trigger frequency that is triggered and enabled in the current time period of the day.

[0088] Calculate the predicted importance value of the first intelligent function based on the predicted frequency and the initial weight.

[0089] In some embodiments, the first determination module 501 is specifically configured to:

[0090] Predict the predicted frequency of the first intelligent function being triggered and enabled in the current time period of the day based on the trigger frequency and weight of the first intelligent function being triggered and enabled in the current time period of the day, the trigger frequency and weight of the first intelligent function being triggered and enabled in the adjacent time period of the current time period, and the trigger frequency and weight of the first intelligent function being triggered and enabled in the current time period and the adjacent time period of adjacent days.

[0091] In some embodiments, the second determination module 502 is specifically configured to:

[0092] Based on the predicted importance values of each of the M intelligent functions, arrange the M intelligent functions in ascending order, and calculate the remaining computing power after sequentially turning off P intelligent functions until the remaining computing power is greater than or equal to the computing power required by the target intelligent function.

[0093] In some embodiments, the adjustment module 503 is further configured to:

[0094] Add a compensation frequency for the intelligent functions that are not enabled; wherein, the compensation frequency is used to compensate the weight of the intelligent functions that are not enabled being selected at the next adjustment time of the intelligent function adjustment.

[0095] In some embodiments, the adjustment module is further configured to:

[0096] If the predicted importance value of the target intelligent function is less than or equal to the total predicted importance value of the P intelligent functions, maintain the enabling actions of the M intelligent functions until the next adjustment time of the intelligent function adjustment.

[0097] It should be noted here that the above device provided in the embodiments of the present application can implement all the method steps in the above method embodiments and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically described in this embodiment.

[0098] Based on the same technical concept, an electronic device is further provided in the embodiments of the present application, and the electronic device can implement the functions of the foregoing intelligent function adjustment device.

[0099] Figure 6The present application provides a schematic structural diagram of an electronic device.

[0100] At least one processor 601 and a memory 602 connected to the at least one processor 601. In the embodiments of the present application, the specific connection medium between the processor 601 and the memory 602 is not limited. Figure 6 In the example, the processor 601 and the memory 602 are connected through a bus 600. The bus 600 is Figure 6 represented by a thick line. The connection manners between other components are only for illustrative purposes and are not restrictive. The bus 600 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 6 only a thick line is used to represent it, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 601 can also be called a controller, and the name is not limited.

[0101] In the embodiments of the present application, the memory 602 stores instructions executable by the at least one processor 601. By executing the instructions stored in the memory 602, the at least one processor 601 can execute an intelligent function adjustment method described above. The processor 601 can implement Figure 5 the functions of each module in the device shown.

[0102] Among them, the processor 601 is the control center of the device, and can connect various parts of the entire control device through various interfaces and lines. By running or executing the instructions stored in the memory 602 and calling the data stored in the memory 602, various functions of the device and process data, so as to monitor the device as a whole.

[0103] In the embodiments of the present application, the processor 601 may include one or more processing units. The processor 601 may integrate an application processor and a modem processor. Among them, the application processor mainly processes the operating system, user interface, application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above modem processor may not be integrated into the processor 601. In some embodiments, the processor 601 and the memory 602 can be implemented on the same chip. In some embodiments, they can also be separately implemented on independent chips.

[0104] The processor 601 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application specific integrated circuit, a field programmable gate array, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of an intelligent function adjustment method disclosed in combination with the embodiments of the present application may be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0105] The memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The memory 602 may include at least one type of storage medium, for example, it may include flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (RAM), a static random access memory (SRAM), a programmable read-only memory (PROM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic memory, a magnetic disk, an optical disk, and so on. The memory 602 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 602 in the embodiments of the present application may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and / or data.

[0106] By designing and programming the processor 601, the code corresponding to the intelligent function adjustment method introduced in the foregoing embodiments can be solidified into the chip, so that the chip can execute Figure 2 the intelligent function adjustment method of the embodiment shown. How to design and program the processor 601 is a well-known technology to those skilled in the art and will not be elaborated here.

[0107] It should be noted here that the above electronic device provided in the embodiments of the present application can implement all the method steps implemented in the above method embodiments and can achieve the same technical effects. The same parts and beneficial effects as those in the method embodiments will not be specifically elaborated in this embodiment.

[0108] Based on the same inventive concept, an embodiment of the present application provides a computer storage medium, which includes: computer program code that, when running on a computer, causes the computer to execute a method for intelligent function adjustment as described in any one of the foregoing discussions. Since the principle of solving problems by the above computer storage medium is similar to that of a method for intelligent function adjustment, the implementation of the above computer storage medium can refer to the implementation of the method, and the repeated parts will not be described again.

[0109] In a specific implementation process, the computer storage medium may include: various storage media that can store program code, such as a Universal Serial Bus Flash Drive (USB), a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc.

[0110] Based on the same inventive concept, an embodiment of the present application further provides a computer program product, which includes: computer program code that, when running on a computer, causes the computer to execute a method for intelligent function adjustment as described in any one of the foregoing discussions. Since the principle of solving problems by the above computer program product is similar to that of a method for intelligent function adjustment, the implementation of the above computer program product can refer to the implementation of the method, and the repeated parts will not be described again.

[0111] The computer program product may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0112] The method in this application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in this application are executed in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user device, a core network device, an OAM, or other programmable devices.

[0113] The computer program or instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer program or instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired or wireless manner. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium can be a volatile or non-volatile storage medium, or can include both volatile and non-volatile types of storage media.

[0114] Those skilled in the art should understand that the embodiments of this application can be provided as a method, a system, or a computer program product. Therefore, this application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0115] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1a device with the functions specified in one or more boxes. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, and the instruction device implements the operations in the process Figure 1 one process or more processes and / or boxes Figure 1 the functions specified in one box or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or more processes and / or boxes Figure 1 one box or more boxes.

[0117] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of the present invention fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these changes and modifications.

Claims

1. A method for adjusting an intelligent function, characterized in that: The method comprises: Based on the predicted importance values ​​of each of the N intelligent functions in the next time period, determine a target intelligent function to be activated from the N intelligent functions; wherein N is an integer greater than 0; Based on the predicted importance values ​​of each of the M intelligent functions in the time period and the computing power required by the target intelligent function, determine P intelligent functions to be disabled from the M intelligent functions; wherein M is an integer greater than 0; If the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions, an adjustment instruction is generated to instruct the target intelligent function to be turned on and to instruct the P intelligent functions to be turned off.

2. The method according to claim 1, characterized in that Before determining the target intelligent function to be activated from the N intelligent functions based on the predicted importance values ​​of each of the N intelligent functions in the next time period, the method further includes: Obtaining an initial weight of a first intelligent function of any one of the N intelligent functions; Calculate the triggering frequency of the first intelligent function being triggered and turned on in the current time period of the day; Based on the trigger frequency of being triggered and turned on in the current time period of the day, predict the predicted frequency of the first intelligent function being triggered and turned on in the current time period of the day; Based on the predicted frequency and the initial weight, a predicted importance value of the first intelligent function is calculated.

3. The method according to claim 2, characterized in that The predicting, based on the triggering frequency of being triggered to start in the current time period of the day, of the first intelligent function being triggered to start in the current time period of the day, includes: Based on the triggering frequency and weight of the first intelligent function being triggered and turned on in the current time period of the day, the triggering frequency and weight of the first intelligent function being triggered and turned on in the adjacent time period of the current time period, and the triggering frequency and weight of the first intelligent function being triggered and turned on in the current time period and the adjacent time period of adjacent days, the predicted frequency of the first intelligent function being triggered and turned on in the current time period of the day is predicted.

4. The method according to claim 1, characterized in that The determining of P intelligent functions to be disabled from the M intelligent functions based on the predicted importance values ​​of each of the M intelligent functions in the next time period and the computing power required by the target intelligent function includes: Based on the predicted importance value of each of the M intelligent functions in the next time period, the M intelligent functions are arranged in ascending order to obtain the M intelligent functions arranged in sequence, and the remaining computing power after each intelligent function is turned off in sequence is calculated until the remaining computing power is greater than or equal to the computing power required by the target intelligent function.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: A compensation frequency is added for the intelligent function that is not turned on; wherein the compensation frequency is used to compensate for the weight of the intelligent function that is not turned on being selected at the next adjustment time of the intelligent function adjustment.

6. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: If the predicted importance value of the target intelligent function is less than or equal to the total predicted importance value of the P intelligent functions, the activation actions of the M intelligent functions are maintained until the next adjustment time of the intelligent function.

7. An intelligent function adjustment device, characterized in that: include: A first determination module is used to determine a target intelligent function to be activated from the N intelligent functions based on the predicted importance values ​​of each of the N intelligent functions in the next time period; wherein N is an integer greater than 0; A second determination module is used to determine P intelligent functions to be disabled from the M intelligent functions based on the predicted importance values ​​of each of the M intelligent functions in the next time period and the computing power required by the target intelligent function; wherein M is an integer greater than 0; The adjustment module is used to generate an adjustment instruction to instruct the target intelligent function to be turned on and to instruct the P intelligent functions to be turned off if the predicted importance value of the target intelligent function is greater than the total predicted importance value of the P intelligent functions.

8. The device according to claim 7, characterized in that The first determining module is further used for: Obtaining an initial weight of a first intelligent function of any one of the N intelligent functions; Calculate the triggering frequency of the first intelligent function being triggered and turned on in the current time period of the day; Based on the trigger frequency of being triggered to start in the current time period of the day, predicting the predicted frequency of the first intelligent function being triggered to start in the next time period of the day; Based on the predicted frequency and the initial weight, a predicted importance value of the first intelligent function is calculated.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to implement the method according to any one of claims 1 to 6 when executing a computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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