CN113971240B - Internet of Things fault early warning method, device and readable storage medium - Google Patents
Internet of Things fault early warning method, device and readable storage medium Download PDFInfo
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Abstract
本发明提供了一种物联网故障的预警方法、装置及可读存储介质，涉及物联网技术领域，其中，上述物联网故障的预警方法，包括：获取物联网中各用户的用户数据；基于所述用户数据，确定各用户对应的用户画像，其中，所述用户画像包括多个标签类型和所述多个标签类型的权重值；根据各用户对应的用户画像，对所述物联网中的各用户进行故障预警。由于本申请可以基于模型中的用户画像提前发现物联网用户数据中的异常情况，并对异常情况进行预警分析，将预警情况制动推送给物联网管理人员，达到提前故障预警、及时跟进处理的效果，同时也能有效降低物联网用户的投诉率，提高客户满意度。
The present invention provides an early warning method, device and readable storage medium for Internet of Things faults, and relates to the technical field of Internet of Things, wherein the above-mentioned early warning method for Internet of Things faults includes: obtaining user data of each user in the Internet of Things; determining the user portrait corresponding to each user based on the user data, wherein the user portrait includes multiple tag types and weight values of the multiple tag types; and performing fault early warning for each user in the Internet of Things according to the user portrait corresponding to each user. Since the present application can detect abnormal situations in the Internet of Things user data in advance based on the user portrait in the model, and perform early warning analysis on the abnormal situations, and push the early warning situation to the Internet of Things management personnel, the effect of early fault warning and timely follow-up processing can be achieved, and at the same time, the complaint rate of Internet of Things users can be effectively reduced, and customer satisfaction can be improved.
Description
Technical Field
The invention relates to the technical field of the internet of things, in particular to an early warning method and device for faults of the internet of things and a readable storage medium.
Background
With the development of the internet of things technology, the internet of things has been widely applied to various industries, and clients of the internet of things are also becoming huge. In the process of using the internet of things card, the internet of things client often cannot be used normally due to various faults such as self faults of the internet of things card, terminal faults, base station faults and the like, so that client complaints are easily caused. Because the existing Internet of things fault has no corresponding early warning mechanism, the fault can be discovered and solved only after the fault appears, and therefore, the situation that the customer complaint rate is high and the customer satisfaction is low is caused.
Disclosure of Invention
The embodiment of the invention provides an early warning method and device for an Internet of things fault, which are used for solving the problems that the existing Internet of things fault has no corresponding early warning mechanism, and the fault is discovered and solved only after the occurrence of the fault, so that the complaint rate of a user is high and the customer satisfaction is low.
In order to solve the technical problems, the invention is realized as follows:
the embodiment of the invention provides an early warning method for faults of the Internet of things, which comprises the following steps:
Acquiring user data of each user in the Internet of things;
determining a user portrait corresponding to each user based on the user data, wherein the user portrait comprises a plurality of tag types and weight values of the tag types;
and carrying out fault early warning on each user in the Internet of things according to the user portraits corresponding to each user.
The embodiment of the invention also provides an early warning device for the faults of the Internet of things, which comprises:
The acquisition module is used for acquiring user data of each user in the Internet of things;
The determining module is used for determining user portraits corresponding to all users based on the user data, wherein the user portraits comprise a plurality of tag types and weight values of the tag types;
and the early warning module is used for carrying out fault early warning on each user in the Internet of things according to the user portrait corresponding to each user.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the early warning method of the Internet of things faults when being executed by a processor.
According to the method and the device for detecting the faults, user data of all users in the Internet of things are obtained, user portraits corresponding to all the users are determined based on the user data, the user portraits comprise a plurality of tag types and weight values of the tag types, and fault early warning is conducted on all the users in the Internet of things according to the user portraits corresponding to all the users. The method and the system can find the abnormal situation in the user data of the Internet of things in advance based on the user portrait in the model, perform early warning analysis on the abnormal situation, push early warning braking to the manager of the Internet of things, achieve the effects of early fault early warning and timely follow-up processing, and simultaneously can effectively reduce the complaint rate of the user of the Internet of things and improve the customer satisfaction.
Drawings
Fig. 1 is one of flowcharts of an early warning method for an internet of things fault provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram showing an effect of a user image according to an embodiment of the present invention;
FIG. 3 is a second flowchart of a method for early warning of an Internet of things fault according to an embodiment of the present invention;
Fig. 4 is a third flowchart of a method for early warning of an internet of things fault according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an early warning device for the fault of the internet of things according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a determining module according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an early warning module according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided merely to facilitate a thorough understanding of embodiments of the invention. It will therefore be apparent to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
As shown in fig. 1, fig. 1 is one of flowcharts of an early warning method for an internet of things fault provided by an embodiment of the present invention, including:
Step S100, user data of each user in the Internet of things are obtained.
The user data includes, but is not limited to, enterprise name of the internet of things card, mobile station integrated service digital network number (Mobile Subscriber International ISDN/PSTN number, MSISDN for short), integrated circuit card identification code (INTEGRATE CIRCUIT CARD IDENTITY, ICCID for short), international mobile subscriber identification code (InternationalMobileSubscriberIdentificationNumber, IMSI for short), accumulated internet usage in the month, accumulated short message usage in the month, package type, on-off state, on-line/off state, on-line state of the terminal, belonging area information, belonging base station information, and the like, which are not limited specifically herein. The manner of obtaining the user data may be directly obtained from each internet of things platform (such as a BOSS system, etc.), each internet of things service gateway, and a network element, or may be obtained from a database established based on the internet of things, which is not specifically limited herein.
Step 200, determining a user portrait corresponding to each user based on the user data, wherein the user portrait comprises a plurality of tag types and weight values of the tag types.
Because each user in the internet of things has a plurality of tag types, the user data of the user can be divided according to different tag types, and the weight value corresponding to each tag type is calculated based on the data in each divided tag type. For other users in the Internet of things, the label types and the weight values corresponding to the label types can be obtained by adopting the mode. The tag types in the present application include, but are not limited to, a user tag type, a service tag type, a group tag type, a base station tag type, a zone tag type, and the like. Assuming that in the present embodiment, a user tag, a base station tag, and an area tag are adopted as tag types, a user portrait effect diagram generated based on the user tag, the base station tag, and the area tag is shown in fig. 2. In fig. 2, each user tag contains attribute data such as an online state of an internet of things card and an offline state of a terminal corresponding to the user, each base station tag contains service interaction data of all users in each base station, and each area tag contains service interaction data of all base stations in each area.
The user tag type can comprise data such as an online state of an Internet of things card, a starting state of a terminal corresponding to the Internet of things card and the like, the service tag type can comprise data such as an Internet of things service interaction information such as interaction time length, accumulated Internet surfing quantity and accumulated short credit quantity, the group tag type can comprise data such as a customer group name, an opening batch of the Internet of things card in the customer group, internet of things service interaction information of the same batch, internet of things card cutting in each batch of opening cards and the like, the base station tag type can comprise information of a base station to which the Internet of things card belongs, such as working state of a base station, interaction information of each Internet of things card in the range of the base station and the like, and the area tag type can comprise position information of an area to which the Internet of things card belongs, working conditions of all base stations in different ranges and the like.
Because the data corresponding to the tag types of different users are different, the weight value calculated by the data corresponding to the tag types of the users is also different, and the weight value is used as the characteristic of the user portrait of the users to obtain the user portrait of the users. The user portrait is a set of the label type of a user and the weight values corresponding to all the label types. For example, the user data of the user a is analyzed to obtain a weight value of 1 corresponding to a user tag type of the user a, a weight value of 0.5 corresponding to a service tag type, a weight value of 0.2 corresponding to a group tag type, a weight value of 0.8 corresponding to a base station tag type and a weight value of 0.6 corresponding to a region tag type, and the user B is analyzed to obtain a user portrait of the user a and the user B, wherein the weight value of 0 corresponding to the user tag type, the weight value of 0.5 corresponding to the service tag type, the weight value of 0.6 corresponding to the group tag type, the weight value of 0.2 corresponding to the base station tag type and the weight value of 0.7 corresponding to the region tag type.
And step S300, performing fault early warning on each user in the Internet of things according to the user portrait corresponding to each user.
According to the weight value corresponding to each label type in the user portrait, determining the fault weight value of each user based on a preset algorithm of the fault weight value, predicting the current fault risk degree of each user according to the fault weight value, and carrying out fault early warning on the user when the risk degree reaches a certain threshold value.
The mode of the fault early warning can be that the abnormal user is directly pushed to the Internet of things manager, or the user portrait information of the abnormal user is pushed to the Internet of things manager, so that the Internet of things manager can conveniently conduct troubleshooting on the abnormal data of the abnormal user, and the fault reason is determined.
According to the method and the device for detecting the faults, user data of all users in the Internet of things are obtained, user portraits corresponding to all the users are determined based on the user data, the user portraits comprise a plurality of tag types and weight values of the tag types, and fault early warning is conducted on all the users in the Internet of things according to the user portraits corresponding to all the users. According to the method and the system, the abnormal situation in the user data of the Internet of things can be predicted in advance based on the user portrait in the model, the abnormal situation is subjected to early warning analysis, the early warning situation is braked and pushed to the Internet of things manager, the effects of early warning of faults and timely follow-up processing are achieved, meanwhile, the complaint rate of the users of the Internet of things can be effectively reduced, and the customer satisfaction degree is improved.
Optionally, as shown in fig. 3, fig. 3 is a second flowchart of an early warning method for an internet of things fault according to an embodiment of the present invention, based on the embodiment shown in fig. 1, step S200 is described above, and based on the user data, determining a user portrait corresponding to each user includes:
step S210, for each user in the Internet of things, acquiring target data of the user, wherein the target data of the user is data corresponding to a preset key field in the user data of the user;
Step S220, dividing the target data into a plurality of label types according to the preset key field, wherein the label types comprise at least one of user label types, service label types, group label types, base station label types and area label types;
Step S230, obtaining a weight value corresponding to the label type;
Step S240, determining a user portrait of the user based on the tag type and the weight value corresponding to the tag type.
When user data is acquired, corresponding data content can be acquired based on the preset key field, and the obtained target data is data corresponding to the preset key field. Because the mapping relation between each key field and the label type is preset in the model, the target data can be divided based on the preset key field, and the data required by each label type can be obtained.
The tag type in this embodiment includes at least one of a user tag type, a service tag type, a group tag type, a base station tag type, and an area tag type. The user tag type can comprise data such as an online state of an Internet of things card, a starting state of a terminal corresponding to the Internet of things card and the like, the service tag type can comprise data such as an Internet of things service interaction information such as interaction time length, accumulated Internet surfing quantity and accumulated short credit quantity, the group tag type can comprise data such as a customer group name, an opening batch of the Internet of things card in the customer group, internet of things service interaction information of the same batch, internet of things card cutting in each batch of opening cards and the like, the base station tag type can comprise information of a base station to which the Internet of things card belongs, such as working state of a base station, interaction information of each Internet of things card in the range of the base station and the like, and the area tag type can comprise position information of an area to which the Internet of things card belongs, working conditions of all base stations in different ranges and the like.
When the weight value corresponding to the tag type is obtained, the weight value corresponding to the tag type can be determined according to the data in each tag type. Specifically, in this embodiment, the user tag type includes two parameters, that is, an online state of the internet of things card (the online state corresponds to 1, and the offline state corresponds to 0) and a power-on state of the terminal corresponding to the internet of things card (the power-on state corresponds to 1, and the power-off state corresponds to 0), and the weight value corresponding to the user tag type is a product of the online state of the internet of things card and the power-on state of the terminal corresponding to the internet of things card, and when the internet of things card of the user a is in the offline state or the power-off state, the weight value corresponding to the user tag type of the user a is 0. The weight value corresponding to the service tag type is whether service interaction exists in a preset period of the internet of things card (interaction corresponds to 1 and no interaction corresponds to 0), and the preset period can be set according to actual conditions, and is not particularly limited herein. When the user A has service interaction in a preset period, the weight value corresponding to the service label type of the user A is 1, otherwise, the corresponding weight value is 0. The weight value corresponding to the group label type is the ratio of the number of the internet of things cards without service interaction in the same group to the total internet of things cards in the same group, wherein the same group represents the same batch of internet of things cards for opening accounts in the same time period or the same batch of internet of things cards based on service classification. If only 50% of cards in the group to which user A belongs have service interactions, then user A's group tag type is 50%. The weight value corresponding to the base station label type is the ratio of the number of the internet of things cards with business interaction under the same base station to the number of the total internet of things cards under the same base station. If only 50% of cards in the base station to which the user a belongs have service interaction, the base station tag type of the user a is 50%. The weight value corresponding to the region label type is the ratio of the number of the Internet of things cards with business interaction in the same region to the number of the total Internet of things cards in the same region. If only 50% of cards in the area to which user a belongs have service interactions, then user a has an area tag type of 50%. In the embodiment, the user image of each user is obtained by obtaining the data corresponding to each tag class from the user data and calculating the weight value of each tag class according to the data corresponding to each tag class, so that the use state of the internet of things of each user can be clearly and intuitively seen, and the follow-up fault early warning through the user image is facilitated.
As shown in fig. 4, fig. 4 is a third flowchart of an early warning method for the fault of the internet of things according to the embodiment of the present invention, based on the embodiment shown in fig. 1, the step S300 of performing fault early warning on each user in the internet of things according to a user portrait corresponding to each user includes:
Step S310, determining an algorithm for calculating the fault weight value of the user according to the weight value corresponding to the user tag type of the user;
Step S320, determining a fault weight value of the user based on the algorithm;
and step S330, carrying out fault early warning on the user under the condition that the fault weight value is higher than a first preset threshold value.
The same algorithm may be used or different algorithms may be used in calculating the fault weight value of each user. The fault weight value is determined, for each user, based on the weight value corresponding to any one or more of the user tag type, service tag type, group tag type, base station tag type, and zone tag type described above.
As the types of the faults of the internet of things are various, tag type data mainly referenced by each fault type is often different. For example, for the failure types caused by the user, such as untimely payment, power failure and shutdown of the terminal, poor contact between the internet of things card and the card slot, etc., the failure types are mainly determined by referring to the data of the user tag types. For a certain base station or regional network failure, the base station tag type and the regional tag type data are mainly referred to for judgment. Therefore, in another embodiment, when calculating the fault weight value, different algorithms may be determined based on different weight values corresponding to the user tag types, so as to facilitate targeted analysis of different types of faults.
The fault weight value represents the degree of risk of the fault predicted by the model, and if the fault is represented by 1 and the fault is not represented by 0, the fault weight value is a value in the range of [0,1 ]. The first preset threshold may be set according to practical situations, and is not specifically limited herein. Preferably, the first preset threshold is 0.5.
In this embodiment, the algorithm is determined according to the weight value corresponding to the user tag type of the user, and then the fault weight value is calculated according to different algorithms, so that multiple modes exist in the calculation mode of the fault weight value, which is beneficial to the targeted analysis of different fault types.
Optionally, in step S310, an algorithm for calculating the fault weight value of the user is determined according to the weight value corresponding to the user tag type of the user, including:
step S311, determining an algorithm for calculating a fault weight value of the user as a first algorithm when the weight value corresponding to the user tag type of the user is greater than or equal to a second preset threshold, where the first algorithm is positively correlated with at least one of the weight value corresponding to the user tag type, the weight value corresponding to the service tag type, the weight value corresponding to the group tag type, the weight value corresponding to the base station tag type, and the weight value corresponding to the area tag type;
Step S312, determining an algorithm for calculating the fault weight value of the user as a second algorithm when the weight value corresponding to the user tag type of the user is smaller than a second preset threshold, where the second algorithm is positively correlated with at least one of the weight value corresponding to the service tag type, the weight value corresponding to the group tag type, the weight value corresponding to the base station tag type, and the weight value corresponding to the area tag type.
In this embodiment, the user tag type includes two parameters, namely, an online state of the internet of things card (the online state corresponds to 1, and the offline state corresponds to 0) and an on state of the terminal corresponding to the internet of things card (the on state corresponds to 1, and the off state corresponds to 0), and the weight value corresponding to the user tag type is a product of the online state of the internet of things card and the on state of the terminal corresponding to the internet of things card, when the internet of things card of the user a is in the offline state or the terminal is in the off state, the weight value corresponding to the user tag type of the user a is 0, and when the internet of things card of the user a is in the online state and the terminal is in the on state, the weight value corresponding to the user tag type of the user a is 1.
And determining an algorithm for calculating the fault weight value of the user as a first algorithm when the weight value corresponding to the user tag type of the user is greater than or equal to a second preset threshold value, and determining the algorithm for calculating the fault weight value of the user as a second algorithm when the weight value corresponding to the user tag type of the user is less than the second preset threshold value. The second preset threshold may be any value ranging from 0 to 1, such as 0.2,0.3,0.4, and may be specifically set according to the actual situation in practical implementation, which is not specifically limited in this embodiment. Preferably, the second preset threshold is 1, that is, when the second preset threshold is 1, the fault weight value is calculated by using the first algorithm, and when the second preset threshold is 0, the fault weight value is calculated by using the second algorithm.
It should be noted that, the first algorithm may be positively related to any one or more of the weight value corresponding to the service tag type, the weight value corresponding to the group tag type, the weight value corresponding to the base station tag type, and the weight value corresponding to the area tag type. The second algorithm may be positively related to any one or more of the weight value corresponding to the service tag type, the weight value corresponding to the group tag type, the weight value corresponding to the base station tag type, and the weight value corresponding to the area tag type, which is not specifically limited in the present application.
In this embodiment, two different algorithms of the fault weight values are determined according to the weight values corresponding to the user tag types, so that other fault types can be further determined under the condition that faults caused by the user own cause exist, and faults of other operator network ends can be further determined under the condition that the internet of things card is offline or the terminal is powered off.
Optionally, the first algorithm is a product of a weight value corresponding to the user tag type, a weight value corresponding to the service tag type, a weight value corresponding to the group tag type, a weight value corresponding to the base station tag type, and a weight value corresponding to the area tag type;
the second algorithm is the product of the weight value corresponding to the service label type, the weight value corresponding to the group label type, the weight value corresponding to the base station label type and the weight value corresponding to the area label type.
For example, assuming that the weight value corresponding to the user tag type of the user a is 1, the weight value corresponding to the service tag type is 0.5, the weight value corresponding to the group tag type is 0.2, the weight value corresponding to the base station tag type is 0.8, and the weight value corresponding to the area tag type is 0.6, since the weight value corresponding to the user tag type of the user a is greater than or equal to the second preset threshold, the fault weight value of the user a=the weight value corresponding to the user tag type×the weight value corresponding to the service tag type×the weight value corresponding to the group tag type×the weight value corresponding to the base station tag type×the weight value corresponding to the area tag type
=1×0.5×0.2×0.8×0.6=0.048. Assuming that the weight value corresponding to the user tag type of the user B is 0, the weight value corresponding to the service tag type is 0.5, the weight value corresponding to the group tag type is 0.6, the weight value corresponding to the base station tag type is 0.2, and the weight value corresponding to the area tag type is 0.7, since the weight value corresponding to the user tag type of the user B is smaller than the second preset threshold, the fault weight value of the user b=the weight value corresponding to the service tag type×the weight value corresponding to the group tag type×the weight value corresponding to the base station tag type×the weight value corresponding to the area tag type=0.5×0.6×0.2×0.7=0.042.
In this embodiment, the fault weight value is calculated by multiplying the weight values corresponding to the plurality of label categories in the first algorithm and the second algorithm, so that the fault weight value and each label category corresponding to the algorithm have a direct proportion relation, and the weight values of the plurality of label categories can be comprehensively considered, so that the calculation result is more accurate and close to the actual situation.
Optionally, in step S320, before obtaining the fault weight value of the user based on the algorithm, the method includes:
Step S340, sending a fault self-checking prompt to the user when the weight value corresponding to the user tag type of the user is smaller than a second preset threshold.
When the weight value corresponding to the user tag type is smaller than the second preset threshold, the fact that the user causes faults due to the user can be determined, and therefore a fault self-checking prompt can be sent to the corresponding user at the moment. The fault self-checking prompt can be any form of prompt information such as voice, text, pictures and the like, and specific fault reasons can be further determined according to data in the user tag type during prompt, for example, when the online state (corresponding to 1 in online state and 0 in offline state) data of the internet of things card is 0, prompt such as 'please detect whether the internet of things card is arrears or not', 'please detect whether the internet of things card is bad in contact or not' is sent, and when the power-on state (corresponding to 1 in power-on state and 0 in power-off state) data of the internet of things card is 0, prompt such as 'please detect whether the electric quantity of the terminal is sufficient' is sent.
In this embodiment, when the weight value corresponding to the user tag type is smaller than the second preset threshold, a fault self-checking prompt is sent to the corresponding user to prompt the user to detect that a fault exists in the user, so that the user can be effectively prevented from initiating complaints under the condition that the user does not know the cause of the fault, and the complaint rate of the user is reduced.
As shown in fig. 5, fig. 5 is a schematic structural diagram of an early warning device for an internet of things fault, where the early warning device 40 for an internet of things fault includes:
an acquiring module 410, configured to acquire user data of each user in the internet of things;
A determining module 420, configured to determine, based on the user data, a user portrait corresponding to each user, where the user portrait includes a plurality of tag types and weight values of the plurality of tag types;
And the early warning module 430 is used for carrying out fault early warning on each user in the Internet of things according to the user portraits corresponding to each user.
Optionally, as shown in fig. 6, the determining module 420 includes:
A first obtaining unit 421, configured to obtain, for each user in the internet of things, target data of the user, where the target data of the user is data corresponding to a preset key field in the user data of the user;
A dividing unit 422, configured to divide the target data into a plurality of tag types according to the preset key field, where the tag types include at least one of a user tag type, a service tag type, a group tag type, a base station tag type, and a region tag type;
A second obtaining unit 423, configured to obtain a weight value corresponding to the tag type;
A first determining unit 424, configured to determine a user portrait of the user based on the tag type and a weight value corresponding to the tag type.
Optionally, as shown in fig. 7, the early warning module 430 includes:
a second determining unit 431, configured to determine an algorithm for calculating a fault weight value of the user according to a weight value corresponding to a user tag type of the user;
a third determining unit 432, configured to determine a fault weight value of the user based on the algorithm;
and the early warning unit 433 is configured to perform early warning on the fault for the user when the fault weight value is higher than a first preset threshold value.
Optionally, the second determining unit 431 includes:
A first determining subunit, configured to determine, when a weight value corresponding to a user tag type of the user is greater than or equal to a second preset threshold, an algorithm for calculating a failure weight value of the user as a first algorithm, where the first algorithm is positively correlated with at least one of a weight value corresponding to the user tag type, a weight value corresponding to the service tag type, a weight value corresponding to the group tag type, a weight value corresponding to the base station tag type, and a weight value corresponding to the area tag type;
And the second determining subunit is configured to determine, when the weight value corresponding to the user tag type of the user is smaller than a second preset threshold, that an algorithm for calculating the fault weight value of the user is a second algorithm, where at least one of the weight value corresponding to the service tag type, the weight value corresponding to the group tag type, the weight value corresponding to the base station tag type, and the weight value corresponding to the area tag type of the second algorithm is positively correlated.
Optionally, the first algorithm is a product of a weight value corresponding to the user tag type, a weight value corresponding to the service tag type, a weight value corresponding to the group tag type, a weight value corresponding to the base station tag type, and a weight value corresponding to the area tag type;
the second algorithm is the product of the weight value corresponding to the service label type, the weight value corresponding to the group label type, the weight value corresponding to the base station label type and the weight value corresponding to the area label type.
Optionally, the early warning module 430 includes:
And the prompting unit is used for sending a fault self-checking prompt to the user under the condition that the weight value corresponding to the user label type of the user is smaller than a second preset threshold value.
The early warning device 40 for the internet of things fault in the embodiment of the present invention is a device corresponding to the foregoing method for early warning the internet of things fault, and all implementation manners in the foregoing method are applicable to the embodiment of the device, so that the same technical effects can be achieved, which is not described in detail herein.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the early warning method of the Internet of things faults when being executed by a processor. All the implementation manners of the above method are applicable to the embodiment of the computer readable storage medium, and the same technical effects can be achieved, which is not described herein.
The foregoing is a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (11)
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