CN117909815A - Equipment running state analysis method and device, electronic equipment and storage medium - Google Patents

Abstract

The embodiment of the application relates to a method and a device for analyzing the running state of equipment, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring monitoring data for a target device, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; extracting operation characteristic data related to the operation of the target equipment from the monitoring data; analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment; based on the state analysis result, a processing policy for the target device is determined and the processing policy is executed. The embodiment of the application realizes the multi-aspect analysis of the running state of the target equipment by utilizing various data, and can automatically and accurately adjust the running state of the target equipment.

Description

Equipment running state analysis method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and apparatus for analyzing an operating state of a device, an electronic device, and a computer readable storage medium.

Background

With the development of the internet of things technology, the control of various devices based on the internet of things has been applied to aspects of daily life. For example, in the field of smart home, a user can remotely control various home, and can monitor the running state of the home, thereby greatly facilitating the life of the user.

Then, due to equipment failure, unstable network connection, misoperation of a user and the like, abnormal behaviors of equipment accessed to the internet of things such as intelligent home and the like can occur, and the experience of the user is affected. Most of the existing equipment abnormal behavior analysis methods are based on preset rules or fixed thresholds, are difficult to adapt to differences of different environments and equipment, and are single in processing method. Therefore, how to make means such as controlling and monitoring the equipment more intelligent, stronger in adaptability and more diversified in processing method is a problem to be solved at present.

Disclosure of Invention

In view of the above, in order to solve some or all of the above technical problems, embodiments of the present application provide a method, an apparatus, an electronic device, and a computer readable storage medium for analyzing an operating state of a device.

In a first aspect, an embodiment of the present application provides a method for analyzing an operating state of a device, where the method includes: acquiring monitoring data for a target device, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; extracting operation characteristic data related to the operation of the target equipment from the monitoring data; analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment; based on the state analysis result, a processing policy for the target device is determined and the processing policy is executed.

In one possible implementation, determining a processing policy for the target device based on the state analysis result, and executing the processing policy includes: generating abnormal prompt information and outputting the abnormal prompt information in response to the determination that the state analysis result indicates that the target equipment is abnormal in operation; and/or, in response to determining that the state analysis result indicates that the target equipment fails and cannot be repaired automatically, controlling the target equipment to stop running and controlling the standby equipment to run according to a control mode of the target equipment; and/or, in response to determining that the state analysis result indicates that the target device is faulty and can be automatically repaired, starting an automatic repair function for the target device based on a preset automatic repair strategy.

In one possible embodiment, before acquiring the monitoring data for the target device, the method further comprises: receiving authority verification information sent by control equipment for controlling target equipment, and verifying the authority verification information; and in response to determining that the obtained verification result indicates that verification is passed, sending an instruction to the control device, wherein the instruction indicates that the control device is allowed to upload the monitoring data.

In one possible implementation, obtaining monitoring data for a target device includes: receiving initial monitoring data for a target device; and decrypting the initial monitoring data based on a preset decryption algorithm to obtain the monitoring data.

In one possible implementation, after acquiring the monitoring data for the target device, the method further comprises: acquiring a historical operation characteristic data set corresponding to target equipment; performing running state prediction on the running characteristic data and the historical running characteristic data set by using a preset running state prediction model to obtain running state prediction information; acquiring running state reference information corresponding to target equipment; and matching the running state reference information with the running state prediction information, and adjusting the running state of the target equipment based on a matching result.

In one possible embodiment, the method further comprises: receiving processing strategy configuration information sent by a target user terminal; and updating the processing strategy based on the processing strategy configuration information.

In one possible embodiment, the state analysis model is trained in advance based on the following steps: acquiring sample monitoring data and a corresponding labeling state analysis result; extracting sample operation characteristic data from sample monitoring data; inputting the sample operation characteristic data into a preset initial state analysis model to obtain an actual state analysis result; determining an error between an actual state analysis result and a labeling state analysis result, and adjusting parameters of an initial state analysis model based on the error; and determining the initial state analysis model after the parameters are adjusted as a state analysis model in response to the initial state analysis model after the parameters are adjusted meeting the preset training ending conditions.

In a second aspect, an embodiment of the present application provides an apparatus for analyzing an operating state of a device, including: the first acquisition module is used for acquiring monitoring data aiming at the target equipment, wherein the monitoring data comprises at least one of the following components: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; the extraction module is used for extracting operation characteristic data related to the operation of the target equipment from the monitoring data; the analysis module is used for analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment; and the execution module is used for determining a processing strategy aiming at the target equipment based on the state analysis result and executing the processing strategy.

In a third aspect, an embodiment of the present application provides an electronic device, including: a memory for storing a computer program; a processor, configured to execute a computer program stored in the memory, and when the computer program is executed, implement a method according to any one of the embodiments of the device operation state analysis method according to the first aspect of the present application.

In a fourth aspect, an embodiment of the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the embodiments of the method for analyzing an operating state of a device according to the first aspect described above.

In a fifth aspect, embodiments of the present application provide a computer program comprising computer readable code which, when run on a device, causes a processor in the device to implement a method as in any of the embodiments of the device operational state analysis method of the first aspect described above.

According to the equipment operation state analysis method, the equipment operation state analysis device, the electronic equipment and the computer readable storage medium, the operation characteristic data related to the operation of the target equipment are extracted from the monitoring data aiming at the target equipment, then the operation characteristic data are analyzed by utilizing the pre-trained state analysis model to obtain the state analysis result representing the current operation condition of the target equipment, finally the processing strategy aiming at the target equipment is determined based on the state analysis result, and the processing strategy is executed, so that the operation state of the target equipment is analyzed in multiple aspects by utilizing various data, the analyzed data are more abundant in types, the state analysis result is more accurate, the scene adaptability is stronger, the operation state of the target equipment can be automatically and accurately adjusted, the adverse effect of equipment abnormality on a user is reduced, and the convenience of the user in using the target equipment is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.

One or more embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements, and in which the figures of the drawings are not to be taken in a limiting sense, unless otherwise indicated.

FIG. 1 is a schematic flow chart of a method for analyzing an operation state of a device according to an embodiment of the present application;

FIG. 2 is a flow chart of another method for analyzing an operation state of a device according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of another method for analyzing an operation state of a device according to an embodiment of the present application;

FIG. 4 is a schematic flow chart of another method for analyzing an operation state of a device according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of another method for analyzing an operation state of a device according to an embodiment of the present application;

FIG. 6 is a flowchart illustrating a training step of a state analysis model according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of an apparatus for analyzing an operation state of a device according to an embodiment of the present application;

Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Various exemplary embodiments of the application will now be described in detail with reference to the accompanying drawings, it being apparent that the described embodiments are some, but not all embodiments of the application. It should be noted that: the relative arrangement of the parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless it is specifically stated otherwise.

It will be appreciated by those skilled in the art that terms such as "first," "second," and the like in the embodiments of the present application are used merely to distinguish between different steps, devices or modules and the like, and do not represent any particular technical meaning or logical sequence therebetween.

It should also be understood that in this embodiment, "plurality" may refer to two or more, and "at least one" may refer to one, two or more.

It should also be appreciated that any component, data, or structure referred to in an embodiment of the application may be generally understood as one or more without explicit limitation or the contrary in the context.

In addition, the term "and/or" in the present application is merely an association relationship describing the association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In the present application, the character "/" generally indicates that the front and rear related objects are an or relationship.

It should also be understood that the description of the embodiments of the present application emphasizes the differences between the embodiments, and that the same or similar features may be referred to each other, and for brevity, will not be described in detail.

The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.

Techniques, circuits, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.

It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.

It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. For an understanding of embodiments of the present application, the present application will be described in detail below with reference to the drawings in conjunction with the embodiments. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In order to solve the technical problems of single method and low accuracy of the state monitoring of equipment in the prior art, the application provides the equipment operation state analysis method which can improve the accuracy of monitoring the equipment operation state by analyzing various data in multiple aspects.

Fig. 1 is a flow chart of an analysis method for an operation state of a device according to an embodiment of the present application. The method can analyze and control the running states of various types of target equipment (such as intelligent home equipment of intelligent air conditioners, intelligent televisions, electric lamps, electric curtains, electronic door locks and the like). It should be noted that the number of the target devices in the embodiment of the present application may be one or more, for example, various smart home in a room may be target devices, and a user may operate each target device at the same time.

The execution subject of the method can be the target equipment, or can be other various types of electronic equipment, such as a special device such as a single control chip, a circuit board containing the control chip and the like, or can be general-purpose electronic equipment such as a smart phone, a notebook computer, a desktop computer, a portable computer, a server and the like which are in communication connection with the target equipment. The main execution body of the method may be hardware or software. When the execution body is hardware, the execution body may be one or more of the electronic devices. For example, a single electronic device may perform the method, or multiple electronic devices may cooperate with one another to perform the method. When the execution subject is software, the method may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module. The present invention is not particularly limited herein.

As shown in fig. 1, the method specifically includes:

step 101, obtaining monitoring data for a target device.

In this embodiment, the monitoring data includes at least one of: the method comprises the steps of aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment.

The above-described operation data may be various data generated by operating the target device, and for example, when the target device includes an air conditioner, the operation data may include a temperature set by the air conditioner, an on-off time, a wind speed, an air conditioning mode, and the like. The operational data may also include voice control instructions to control the target device (e.g., "turn on living room lights", "turn off windows", etc.), mobile application operational instructions (i.e., instructions to operate the target device through a mobile phone application), timed task instructions (e.g., set 10 pm to turn off the target device), etc.

The above-mentioned operating state data may be various parameters, such as an operating voltage, an operating current, an internal temperature of the device, etc., which are collected while the target device is operating.

The environment detection data may be data obtained by detecting the environment in which the target device is located using various sensors. For example, when the target device is an air conditioner, the environment detection data may include an indoor temperature, an outdoor temperature, an indoor humidity, an outdoor humidity, an illumination intensity, an air quality, a noise condition, and the like.

And 102, extracting operation characteristic data related to the operation of the target equipment from the monitoring data.

In this embodiment, the operation feature data may be data obtained by preprocessing the operation data, the working state data, and the environment detection data in a data cleaning and formatting manner. For example, the above-described various data may be converted into a form of a vector.

And step 103, analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment.

In this embodiment, the state analysis model is used to represent the correspondence between the operation feature data and the state analysis result. The state analysis model can be a neural network model, and can also be in the forms of a calculation formula, a corresponding relation table and the like.

As an example, the state analysis model may be built based on a convolutional neural network, which may include modules of a convolutional layer, a fully-connected layer, a pooling layer, a classifier, and the like. The state analysis module calculates and classifies the input operation characteristic data and outputs a state analysis result which indicates whether all aspects of the target equipment are operated normally.

The state analysis results may indicate whether aspects of the target device operation are normal. As an example, when a user adjusts the temperature of the air conditioner (e.g., decreases the temperature), the generated operation characteristic data may be analyzed by the state analysis model to determine whether the temperature adjustment operation performed by the user meets the current environmental state (e.g., the outdoor temperature is low).

For another example, the state analysis result may indicate that the internal state (such as the flow rate and the temperature of the refrigerant) of the air conditioner does not reach the normal state under the condition of the set temperature, and the air conditioner is in a higher illumination intensity environment, and the state analysis result at this time includes first information indicating whether the internal state of the air conditioner is normal or not and second information indicating that the illumination intensity is too high.

Step 104, determining a processing strategy for the target device based on the state analysis result, and executing the processing strategy.

In this embodiment, different state analysis results may correspond to different processing strategies, and the correspondence between the state analysis results and the processing strategies may be preset. It should be appreciated that the processing strategies herein may be implemented by executing corresponding programs.

As an example, when the state analysis result indicates that the internal state (such as the flow rate of the refrigerant, the temperature, etc.) of the air conditioner does not reach the normal state under the condition of the set temperature, and the air conditioner is in the environment of higher illumination intensity, the motorized window treatment may be controlled to block sunlight at this time, and it should be understood that the air conditioner and the motorized window treatment in this example are both target devices.

According to the equipment operation state analysis method provided by the embodiment of the application, the operation characteristic data related to the operation of the target equipment is extracted from the monitoring data aiming at the target equipment, then the operation characteristic data is analyzed by utilizing the pre-trained state analysis model to obtain the state analysis result representing the current operation condition of the target equipment, finally the processing strategy aiming at the target equipment is determined based on the state analysis result, and the processing strategy is executed, so that the operation state of the target equipment is analyzed in multiple ways by utilizing various data, the analyzed data are more abundant in types, the state analysis result is more accurate, the scene adaptability is stronger, the operation state of the target equipment can be automatically and accurately adjusted, the adverse effect on a user caused by equipment abnormality is reduced, and the convenience of the user in using the target equipment is improved.

In some optional implementations of the present embodiment, in step 104, the execution subject of the method may execute at least one of the following processing strategies:

And (3) according to the strategy I, generating abnormal prompt information and outputting the abnormal prompt information in response to the fact that the state analysis result indicates that the target equipment operates abnormally.

Specifically, the target device operation abnormality may include a user operation abnormality, a target device internal state abnormality, an environment abnormality, and the like.

The user operation anomaly may include: abnormal instructions, namely, if abnormal instructions occur in the user operation data, such as abnormal switching-on and switching-off time of equipment, excessively high or excessively low temperature setting and the like; unauthorized operations, i.e. if unauthorized operating instructions are found, such as someone attempting to control a device in the home via a mobile application, these data may also represent illegal operations; consecutive failed instructions, i.e., if multiple instructions are sent in succession but the target device does not respond or respond with errors, may indicate a device failure or an exception in the instruction data.

The target device internal state exception may include: too high or too low a current, voltage, internal temperature, etc. of the target device.

Environmental anomalies may include: a temperature anomaly, i.e. if the indoor temperature is too high or too low, this may indicate an environmental anomaly, e.g. in summer an indoor temperature that may indicate an air conditioning malfunction or an outdoor temperature anomaly; humidity anomalies, for example, excessive indoor humidity during a wet season may indicate a dehumidification plant malfunction or outdoor humidity anomalies; an illumination anomaly, i.e. an excessive or insufficient indoor illumination intensity, is indicative of an environmental anomaly, e.g. an excessive low indoor illumination intensity on overcast days may be indicative of a lighting malfunction or outdoor weather anomaly; abnormal air quality: i.e. if the indoor air quality is poor, e.g. the carbon dioxide concentration is too high or the carbon monoxide concentration is out of specification, this may be indicative of an environmental anomaly; noise anomalies, i.e. if the indoor noise level is too high or an anomaly noise suddenly appears, may indicate an environmental anomaly, e.g. a sudden occurrence of a noisy sound at a quiet night may indicate an anomaly occurrence.

The generated hint information may be of various types, such as audio, images, text, etc. The prompt information can be output by playing voice, sending to the user terminal and the like, so as to achieve the purpose of prompting the user.

And (3) a second strategy, wherein in response to the fact that the state analysis result indicates that the target equipment fails and cannot be repaired automatically, the target equipment is controlled to stop running, and the standby equipment is controlled to run according to the control mode of the target equipment.

Specifically, the status analysis result may indicate the type of fault occurring in the target device, and if the fault belongs to the type of fault (such as open circuit, etc.) that cannot be automatically repaired, the fault may be switched to the standby device, and the standby device is used to replace the target device to operate.

And a third strategy, wherein the automatic repair function of the target equipment is started based on a preset automatic repair strategy in response to the fact that the state analysis result indicates the fault of the target equipment and can be automatically repaired.

Specifically, if the fault type belongs to the fault type capable of being automatically repaired, the automatic repair function can be executed. For example, when a failure occurs in which the temperature cannot be set in the air conditioner, the control program may be restored to an initial state to prevent the occurrence of a crash situation. For another example, when the lighting device fails, the system can automatically adjust the brightness of other lights to compensate for the problem of insufficient illumination.

Optionally, if the number of times of execution of the automatic repair function exceeds the number of times threshold, the state analysis result still indicates that the target device is faulty, it may be determined that the target device cannot be automatically repaired, and the policy two and/or the policy one may be executed.

According to the embodiment, when the state analysis result shows that the target equipment is abnormal or fails, the corresponding processing strategy is executed, so that the target equipment is timely processed when the target equipment is abnormal, the target equipment can be timely repaired, and the running stability of the target equipment is improved.

In some alternative implementations of the present embodiment, as shown in fig. 2, before step 101, the method further includes:

Step 105, receiving the authority verification information sent by the control device for controlling the target device, and verifying the authority verification information.

In this embodiment, the executing body may verify the control authority of the target device based on the authority verification information. The control device may be disposed in the target device, or may be another device that is communicatively connected to the target device, for example, a mobile phone, a tablet computer, or the like of the user. The execution main body of the method can judge the authorization condition of the control equipment, and if the control equipment is authorized, the target equipment can be controlled.

And step 106, in response to determining that the obtained verification result indicates that verification is passed, sending an instruction for allowing the control device to upload the monitoring data to the control device.

Specifically, the digital certificate may be sent to the control device, so as to authorize the control device, and the control device may further control the target device, and may obtain the above-mentioned monitoring data, and upload the monitoring data to the execution subject of the method.

The embodiment realizes that the monitoring data is acquired from the authorized equipment by verifying the authorization condition of the control equipment, thereby being beneficial to realizing the safety of data transmission and equipment control.

In some alternative implementations of the present embodiment, as shown in fig. 3, step 101 includes:

in step 1011, initial monitoring data is received for the target device.

The initial monitoring data is data encrypted based on a preset encryption algorithm (such as an AES encryption algorithm). The initial monitoring data includes at least one of: initial operation data, initial operating state data, and initial environment detection data.

Step 1012, decrypting the initial monitoring data based on a preset decryption algorithm to obtain the monitoring data.

The decryption algorithm is an algorithm corresponding to the encryption algorithm. For example, the target device (or the control device) may encrypt the collected data with a preset shared key, the executing body (e.g., server) of the method may decrypt the collected data with the same key, and if the keys match, the collected data is legal, and may execute subsequent steps with the decrypted monitored data.

According to the embodiment, the collected monitoring data are encrypted and then sent to the execution main body, and the execution main body decrypts the received data to obtain the monitoring data, so that the safety of data sending and the privacy protection of users are guaranteed.

In some alternative implementations of the present embodiment, as shown in fig. 4, after step 101, the method further includes:

Step 107, acquiring a historical operation characteristic data set corresponding to the target device.

The historical operation characteristic data set may be a set of operation characteristic data obtained by executing each step included in the method at each historical moment.

And step 108, performing running state prediction on the running characteristic data and the historical running characteristic data set by using a preset running state prediction model to obtain running state prediction information.

The running state prediction model can be constructed based on a neural network, and can also be a calculation formula, a corresponding relation table and the like.

For example, an initial operating state prediction model may be constructed using a neural network model (e.g., RNN, LSTM, etc. type of neural network) that processes the sequence data. And training the initial running state prediction model according to a machine learning method by using a training sample (comprising a sample running characteristic data set and corresponding labeling running state prediction information), so as to obtain a running state model. The operational state model may predict the input operational feature data and the historical operational feature data set to obtain operational state prediction information indicative of operational states of aspects of the target device for a period of time in the future.

For example, if the target device is an air conditioner, the operation state prediction information may indicate a temperature setting condition of the air conditioner, a change condition of environmental data such as indoor temperature and humidity, an operation state of the air conditioner, and the like in a future period of time.

Step 109, obtaining the running state reference information corresponding to the target device.

Wherein the operational state reference information may be various types of information representing a relationship with the target device over a future period of time, which may be used to match the operational state prediction information. For example, the operation state reference information may be weather information of a future period, which may be obtained from the internet, or may be predicted according to data collected by an outdoor sensor.

For another example, the operating state reference information may also represent the energy consumption of the target device during each period of time (e.g., one day) under normal conditions, and may be used to match the predicted energy consumption.

Step 110, matching the operation state reference information and the operation state prediction information, and adjusting the operation state of the target device based on the matching result.

Specifically, if the operation state reference information and the operation state prediction information are not matched, the operation parameters of the target device may be automatically adjusted, so as to adjust the operation state of the target device until the operation state reference information and the operation state prediction information are matched.

As an example, when the operation state reference information is weather information for a period of time in the future, if the weather information indicates that the day in the future is high temperature weather, but the operation state prediction information indicates that the temperature of the air conditioner for the day in the future is set to a higher temperature, it is determined that the operation state reference information and the operation state prediction information do not match, the temperature for the day in the future may be adjusted to a lower temperature in advance, and when the adjustment time is reached, the operation state of the air conditioner is adjusted.

For another example, the operation state reference information indicates the energy consumption of the target device in each period of the day under the normal condition, if the operation state prediction information indicates that the predicted energy consumption of the night is high, it is determined that the operation state reference information and the operation state prediction information are not matched, at this time, parameters such as temperature and air supply quantity corresponding to the night can be preset, and when the adjustment time is reached, the operation state of the air conditioner is adjusted.

According to the method and the device, the operation state of the target device is adjusted according to the prediction result by predicting the operation state of the historical operation characteristic data set and the current operation characteristic data, so that the target device is automatically operated in a certain state, the environment adaptability of the target device in operation is improved, the operation times of a user are reduced, and the power consumption is reduced.

In some optional implementations of the present embodiment, as shown in fig. 5, the method further includes:

and step 111, receiving processing strategy configuration information sent by the target user terminal.

The target user terminal may be various types of terminal devices used by the target user, such as a mobile phone, a tablet computer, a notebook computer, and the like. The target user may be an end user using the target device or may be a management user of the target device. The target user terminal can control and manage the target device through a web interface or a mobile application program.

The processing policy configuration information may be used to add, reduce or modify policies based on the original processing policies, generate processing policy configuration information, and send the processing policy configuration information to an execution body (e.g., a server) of the method.

And step 112, updating the processing strategy based on the processing strategy configuration information.

After receiving the processing strategy configuration information, the processing strategy configuration information can be analyzed to obtain parameters needing to be modified, and then the current processing strategy is updated.

As an example, the processing policy configuration information may include a personalized configuration of the user, such as illumination brightness, air-conditioning temperature, operation time of the target device, and processing manner after abnormality of the target device, etc., and may also set a manner of automatically adjusting operation of the target device according to the above-described state analysis result (e.g., automatically adjusting illumination brightness of the illumination device, temperature of the air-conditioning device, etc., according to data of illumination, temperature, etc.).

It should be noted that, steps 111 to 112 of the present embodiment may be performed at any position in the above steps.

According to the embodiment, the processing strategy is updated based on the processing strategy configuration information sent by the target user terminal, so that the targeted management of the operation of the target equipment by the user is realized, the control of the target equipment by the user is more convenient, and the adaptability of the target equipment to different users and different scenes is improved.

In some alternative implementations of the present embodiment, as shown in fig. 6, the state analysis model is trained in advance based on the following steps:

And step 601, acquiring sample monitoring data and a corresponding labeling state analysis result.

The sample monitoring data may include at least one of sample operation data, sample operating state data, and sample environment detection data, among others. The labeling state analysis result represents the actual operation condition of the device corresponding to the sample monitoring data, for example, the actual power consumption, the fault condition, the operation time length and the like of the device.

Step 602, extracting sample operation characteristic data from sample monitoring data.

The method for extracting the sample operation feature data is substantially the same as that of step 102, and will not be described herein.

And 603, inputting the sample operation characteristic data into a preset initial state analysis model to obtain an actual state analysis result.

The initial state analysis model may be an untrained neural network model, or an untrained completed neural network model. The initial state analysis model can carry out convolution, pooling, activation function calculation, classification and other processing on the input operation characteristic data to obtain an actual state analysis result.

Step 604, determining an error between the actual state analysis result and the labeling state analysis result, and adjusting parameters of the initial state analysis model based on the error.

The error between the actual state analysis result and the labeling state analysis result can be represented by a loss value calculated by a preset loss function (such as a cross entropy loss function). Parameters of the initial state analysis model are adjusted by a back propagation method and a gradient descent method with the aim of minimizing loss values.

Step 605, determining the initial state analysis model after parameter adjustment as a state analysis model in response to the initial state analysis model after parameter adjustment conforming to a preset training ending condition.

Wherein the training end condition may include, but is not limited to, at least one of: the training times reach the preset times, the training time reaches the preset time, the loss value converges, and the like.

It should be noted that, the training process described in this embodiment is a process described for a set of sample monitoring data and labeling state analysis results, and the actual training process needs to use multiple sets of samples to train repeatedly until the above training end condition is satisfied.

It should be further noted that, the training process described in this embodiment may be performed by the execution body, or may be performed by another device, and the trained state analysis model may be set on the execution body.

In addition, the training process of the embodiment can be continuously performed, that is, the monitoring data and the corresponding state analysis results corresponding to the plurality of devices can be continuously obtained as training samples, so that the analysis precision of the model is continuously improved.

The embodiment realizes training by using a machine learning method to obtain the state analysis model, and enables the state analysis model to adapt to a control scene of the target equipment, and the trained model can comprehensively analyze data of multiple aspects of the target equipment, so that the accuracy of analyzing the state of the target equipment is improved.

Fig. 7 is a schematic structural diagram of an apparatus for analyzing an operation state of a device according to an embodiment of the present application. The method specifically comprises the following steps: a first obtaining module 701, configured to obtain monitoring data for a target device, where the monitoring data includes at least one of: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; an extracting module 702, configured to extract operation feature data related to the operation of the target device from the monitored data; the analysis module 703 is configured to analyze the operation feature data by using a pre-trained state analysis model, so as to obtain a state analysis result that indicates a current operation condition of the target device; an execution module 704 is configured to determine a processing policy for the target device based on the status analysis result, and execute the processing policy.

In some optional implementations of the present embodiment, the execution module includes: the first execution unit is used for responding to the determined state analysis result to represent that the target equipment is abnormal in operation, generating abnormal prompt information and outputting the abnormal prompt information; and/or the second execution unit is used for controlling the target equipment to stop running and controlling the standby equipment to run according to the control mode of the target equipment in response to the fact that the state analysis result indicates that the target equipment fails and cannot be repaired automatically; and/or the third execution unit is used for starting an automatic repair function for the target equipment based on a preset automatic repair strategy in response to the fact that the state analysis result indicates that the target equipment is faulty and can be repaired automatically.

In some optional implementations of this embodiment, the apparatus further includes: the first receiving module is used for receiving the authority verification information sent by the control equipment for controlling the target equipment and verifying the authority verification information; and the sending module is used for sending an instruction for allowing the control device to upload the monitoring data to the control device in response to the fact that the verification result obtained through the determination shows that the verification is passed.

In some optional implementations of the present embodiment, obtaining monitoring data for a target device includes: the second receiving module is used for receiving initial monitoring data aiming at target equipment; and the decryption module is used for decrypting the initial monitoring data based on a preset decryption algorithm to obtain the monitoring data.

In some optional implementations of this embodiment, the apparatus further includes: the second acquisition module is used for acquiring a historical operation characteristic data set corresponding to the target equipment; the prediction module is used for predicting the running state of the running characteristic data and the historical running characteristic data set by using a preset running state prediction model to obtain running state prediction information; the third acquisition module is used for acquiring the running state reference information corresponding to the target equipment; and the adjusting module is used for matching the running state reference information with the running state prediction information and adjusting the running state of the target equipment based on a matching result.

In some optional implementations of this embodiment, the apparatus further includes: the third receiving module is used for receiving the processing strategy configuration information sent by the target user terminal; and the updating module is used for updating the processing strategy based on the processing strategy configuration information.

In some optional implementations of the present embodiment, the state analysis model is trained in advance based on the following steps: acquiring sample monitoring data and a corresponding labeling state analysis result; extracting sample operation characteristic data from sample monitoring data; inputting the sample operation characteristic data into a preset initial state analysis model to obtain an actual state analysis result; determining an error between an actual state analysis result and a labeling state analysis result, and adjusting parameters of an initial state analysis model based on the error; and determining the initial state analysis model after the parameters are adjusted as a state analysis model in response to the initial state analysis model after the parameters are adjusted meeting the preset training ending conditions.

The device operation state analysis device provided in this embodiment may be a device operation state analysis device as shown in fig. 7, and may perform all the steps of the above device operation state analysis method, so as to achieve the technical effects of the above device operation state analysis method, and specific reference is made to the above related description, which is not repeated herein for brevity.

Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and the electronic device 800 shown in fig. 8 includes: at least one processor 801, memory 802, at least one network interface 804, and other user interfaces 803. The various components in the electronic device 800 are coupled together by a bus system 805. It is appreciated that the bus system 805 is used to enable connected communications between these components. The bus system 805 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration, the various buses are labeled as bus system 805 in fig. 8.

The user interface 803 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, etc.).

It will be appreciated that the memory 802 in embodiments of the application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (STATIC RAM, SRAM), dynamic random access memory (DYNAMIC RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate Synchronous dynamic random access memory (Double DATA RATE SDRAM, DDRSDRAM), enhanced Synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCH LINK DRAM, SLDRAM), and Direct memory bus random access memory (DRRAM). The memory 802 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

In some implementations, the memory 802 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system 8021 and application programs 8022.

The operating system 8021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. Application 8022 contains various applications, such as a media player (MEDIA PLAYER), browser (Browser), etc., for implementing various application services. The program for implementing the method of the embodiment of the present application may be contained in the application program 8022.

In this embodiment, by calling a program or an instruction stored in the memory 802, specifically, a program or an instruction stored in the application 8022, the processor 801 is configured to perform the method steps provided by the method embodiments, for example, including:

Acquiring monitoring data for a target device, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; extracting operation characteristic data related to the operation of the target equipment from the monitoring data; analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment; based on the state analysis result, a processing policy for the target device is determined and the processing policy is executed.

The method disclosed in the above embodiment of the present application may be applied to the processor 801 or implemented by the processor 801. The processor 801 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware in the processor 801 or by instructions in software. The processor 801 may be a general purpose processor, digital signal processor (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 802, and the processor 801 reads information in the memory 802 and, in combination with its hardware, performs the steps of the above method.

It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application SPECIFIC INTEGRATED circuits (asics), digital signal processors (DIGITAL SIGNAL processing, dsps), digital signal processing devices (DSPDEVICE, DSPD), programmable logic devices (Programmable Logic Device, plds), field-programmable gate arrays (field-programmable GATE ARRAY, FPGA), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units for performing the above-described functions of the application, or a combination thereof.

For a software implementation, the techniques described herein may be implemented by means of units that perform the functions described herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.

The electronic device provided in this embodiment may be an electronic device as shown in fig. 8, and may perform all the steps of the above-described method for analyzing an operation state of each device, so as to achieve the technical effects of the above-described method for analyzing an operation state of each device, and specific reference is made to the above-described related description, which is omitted herein for brevity.

The embodiment of the application also provides a storage medium (computer readable storage medium). The storage medium here stores one or more programs. Wherein the storage medium may comprise volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid state disk; the memory may also comprise a combination of the above types of memories.

When one or more programs in the storage medium are executable by one or more processors, the above-described device operation state analysis method executed on the electronic device side is implemented.

The above processor is configured to execute a program stored in the memory, so as to implement the following steps of the device operation state analysis method executed on the electronic device side:

Acquiring monitoring data for a target device, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment; extracting operation characteristic data related to the operation of the target equipment from the monitoring data; analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment; based on the state analysis result, a processing policy for the target device is determined and the processing policy is executed.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of function in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different circuitry for each particular application, but such implementation is not to be considered as beyond the scope of the present application.

The steps of a circuit or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "includes," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The circuit steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless an order of performance is explicitly stated. It should also be appreciated that additional or alternative steps may be used.

The foregoing is only a specific embodiment of the application to enable those skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A method of analyzing an operating condition of a device, the method comprising:

Acquiring monitoring data for a target device, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment;

extracting operation characteristic data related to the operation of the target equipment from the monitoring data;

Analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment;

And determining a processing strategy aiming at the target equipment based on the state analysis result, and executing the processing strategy.

2. The method of claim 1, wherein the determining a processing policy for the target device based on the state analysis results and executing the processing policy comprises:

Generating abnormal prompt information and outputting the abnormal prompt information in response to the fact that the state analysis result indicates that the target equipment operates abnormally; and/or the number of the groups of groups,

Responding to the fact that the state analysis result indicates that the target equipment fails and cannot be repaired automatically, controlling the target equipment to stop running and controlling the standby equipment to run according to a control mode of the target equipment; and/or the number of the groups of groups,

And in response to determining that the state analysis result indicates that the target equipment is faulty and can be automatically repaired, starting an automatic repair function for the target equipment based on a preset automatic repair strategy.

3. The method of claim 1, wherein prior to the acquiring the monitoring data for the target device, the method further comprises:

receiving authority verification information sent by control equipment for controlling the target equipment, and verifying the authority verification information;

and in response to determining that the obtained verification result indicates that verification is passed, sending an instruction to the control device, wherein the instruction indicates that the control device is allowed to upload the monitoring data.

4. The method of claim 1, wherein the obtaining monitoring data for the target device comprises:

receiving initial monitoring data for the target device;

And decrypting the initial monitoring data based on a preset decryption algorithm to obtain the monitoring data.

5. The method of claim 1, wherein after the acquiring the monitoring data for the target device, the method further comprises:

acquiring a historical operation characteristic data set corresponding to the target equipment;

Performing running state prediction on the running characteristic data and the historical running characteristic data set by using a preset running state prediction model to obtain running state prediction information;

acquiring running state reference information corresponding to the target equipment;

and matching the running state reference information with the running state prediction information, and adjusting the running state of the target equipment based on a matching result.

6. The method according to claim 1, wherein the method further comprises:

Receiving processing strategy configuration information sent by a target user terminal;

And updating the processing strategy based on the processing strategy configuration information.

7. The method according to any one of claims 1-6, wherein the state analysis model is trained beforehand based on the steps of:

Acquiring sample monitoring data and a corresponding labeling state analysis result;

extracting sample operation characteristic data from the sample monitoring data;

Inputting the sample operation characteristic data into a preset initial state analysis model to obtain an actual state analysis result;

determining an error between the actual state analysis result and the labeling state analysis result, and adjusting parameters of the initial state analysis model based on the error;

And responding to the initial state analysis model after the parameters are adjusted to meet the preset training ending conditions, and determining the initial state analysis model after the parameters are adjusted as the state analysis model.

8. An apparatus for analyzing an operating state of a device, the apparatus comprising:

The first acquisition module is used for acquiring monitoring data aiming at target equipment, wherein the monitoring data comprises at least one of the following: aiming at operation data of target equipment, working state data of the target equipment and environment detection data obtained by detecting the environment of the target equipment;

the extraction module is used for extracting operation characteristic data related to the operation of the target equipment from the monitoring data;

The analysis module is used for analyzing the operation characteristic data by utilizing a pre-trained state analysis model to obtain a state analysis result representing the current operation condition of the target equipment;

And the execution module is used for determining a processing strategy aiming at the target equipment based on the state analysis result and executing the processing strategy.

9. An electronic device, comprising:

a memory for storing a computer program;

A processor for executing a computer program stored in said memory, and said computer program, when executed, implementing the method for analyzing the operation state of a device according to any of the preceding claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the method for analyzing the operation state of a device according to any of the preceding claims 1-7.