CN116567454A - Equipment full life cycle operation monitoring method and system based on Internet of things - Google Patents
Equipment full life cycle operation monitoring method and system based on Internet of things Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention discloses a device full life cycle operation monitoring method and a system based on the Internet of things, which are applied to the technical field of data processing, wherein the method comprises the following steps: and reading the lease request list through the equipment lease platform. And acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing to obtain a first request characteristic. A predetermined request feature sequence is established based on the first request feature. And constructing a device database of the target device, and storing the device database into the lease decision model. And screening to obtain candidate lease requests through a lease decision model. And matching the candidate lease use of the candidate lease request in a predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in a lease decision model to obtain a device lease decision. The method and the device solve the technical problems that in the prior art, the processing efficiency of a device lease streaming process on lease requests is low, and the subjectivity of processing is high.
Description
Technical Field
The invention relates to the field of data processing, in particular to a full life cycle operation monitoring method and system for equipment based on the Internet of things.
Background
The internet of things technology is a technology for acquiring and monitoring various objects or data in real time through various information sensors. In the prior art, during the running process of the leasing circulation of the equipment, the equipment resources are allocated or processed manually, the processing efficiency of the leasing request is low, the subjectivity of manual processing is high, and the equipment resources cannot be allocated accurately and reasonably.
Therefore, in the prior art, the processing efficiency of the equipment lease streaming process on lease requests is low, and the technical problem of strong subjectivity of processing exists.
Disclosure of Invention
The method and the system for monitoring the full life cycle operation of the equipment based on the Internet of things solve the technical problems that in the prior art, the equipment lease circulation process has low processing efficiency on lease requests and the subjectivity of processing is high.
The application provides a device full life cycle operation monitoring method based on the Internet of things, wherein the device full life cycle operation monitoring method is applied to a device full life cycle operation monitoring system, the device full life cycle operation monitoring system is in communication connection with a device leasing platform, the device leasing platform comprises target devices, and the device full life cycle operation monitoring method comprises the following steps: reading a lease request list through the equipment lease platform, wherein the lease request list comprises M lease requests, and M is an integer greater than or equal to 1; acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request characteristic; establishing a predetermined request feature sequence based on the first request feature, wherein the predetermined request feature sequence comprises a predetermined lease time length sequence, a predetermined lease number sequence and a predetermined lease use sequence; constructing a device database of the target device, and storing the device database into a lease decision model, wherein the device database comprises N devices with history operation record identifications, and N is an integer greater than 1; analyzing the predetermined lease time length sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests; matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in the lease decision model to obtain a device lease decision; the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
The application also provides a device full life cycle operation monitoring system based on the Internet of things, wherein the system is in communication connection with a device leasing platform, the device leasing platform comprises target devices, and the system comprises: a lease request acquisition module, configured to read a lease request list through the equipment lease platform, where the lease request list includes M lease requests, and M is an integer greater than or equal to 1; the request feature acquisition module is used for acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request feature; a request feature sequence acquisition module, configured to construct a predetermined request feature sequence based on the first request feature, where the predetermined request feature sequence includes a predetermined lease duration sequence, a predetermined lease number sequence, and a predetermined lease usage sequence; the equipment database construction module is used for constructing an equipment database of the target equipment and storing the equipment database into a lease decision model, wherein the equipment database comprises N pieces of equipment with historical operation record identifiers, and N is an integer greater than 1; the candidate lease request acquisition module is used for analyzing the predetermined lease duration sequence and the predetermined lease quantity sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests; the equipment lease decision acquisition module is used for matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through an equipment decision unit in the lease decision model to obtain an equipment lease decision; the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
The application also provides an electronic device, comprising:
a memory for storing executable instructions;
and the processor is used for realizing the equipment full life cycle operation monitoring method based on the Internet of things when executing the executable instructions stored in the memory.
The application provides a computer readable storage medium storing a computer program which, when executed by a processor, realizes the full life cycle operation monitoring method of the equipment based on the Internet of things.
The method and the system for monitoring the full life cycle operation of the equipment based on the Internet of things are proposed, and a lease request list is read through a equipment lease platform. And acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing to obtain a first request characteristic. A predetermined request feature sequence is established based on the first request feature. And constructing a device database of the target device, and storing the device database into the lease decision model. And screening to obtain candidate lease requests through a lease decision model. And matching the candidate lease use of the candidate lease request in a predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in a lease decision model to obtain a device lease decision. And monitoring the equipment based on the Internet of things, acquiring the leasing quantity of the equipment and carrying out accurate matching of leasing requests. The quick response to the lease request is realized, the subjectivity of the processing process is further reduced, and the accuracy of equipment matching of the lease request is further improved. The method and the device solve the technical problems that in the prior art, the processing efficiency of a device lease streaming process on lease requests is low, and the subjectivity of processing is high.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly described below. It is apparent that the figures in the following description relate only to some embodiments of the present disclosure and are not limiting of the present disclosure.
Fig. 1 is a flow chart of a method for monitoring operation of a full life cycle of an internet of things-based device according to an embodiment of the present application;
fig. 2 is a schematic flow chart of acquiring a candidate lease request by using the method for monitoring the whole life cycle operation of an internet of things-based device according to the embodiment of the present application;
fig. 3 is a schematic flow chart of acquiring a first request feature by using the method for monitoring the full life cycle operation of the device based on the internet of things according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of a system of a device full life cycle operation monitoring method based on the internet of things according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system electronic device of a device full life cycle operation monitoring method based on the internet of things according to an embodiment of the present invention.
Reference numerals illustrate: rental request acquisition module 11, request feature acquisition module 12, request feature sequence acquisition module 13, equipment database construction module 14, candidate rental request acquisition module 15, equipment rental decision acquisition module 16, processor 31, memory 32, input device 33, and output device 34.
Detailed Description
Example 1
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
In the following description, the terms "first", "second", "third" and the like are merely used to distinguish similar objects and do not represent a particular ordering of the objects, it being understood that the "first", "second", "third" may be interchanged with a particular order or sequence, as permitted, to enable embodiments of the application described herein to be practiced otherwise than as illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only.
While the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, the modules are merely illustrative, and different aspects of the system and method may use different modules.
A flowchart is used in this application to describe the operations performed by a system according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
As shown in fig. 1, an embodiment of the present application provides a device full life cycle operation monitoring method based on the internet of things, where the device full life cycle operation monitoring method is applied to a device full life cycle operation monitoring system, the device full life cycle operation monitoring system is communicatively connected with a device leasing platform, the device leasing platform includes a target device, and the device full life cycle operation monitoring method includes: the method comprises the following steps:
s10: reading a lease request list through the equipment lease platform, wherein the lease request list comprises M lease requests, and M is an integer greater than or equal to 1;
s20: acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request characteristic;
s30: establishing a predetermined request feature sequence based on the first request feature, wherein the predetermined request feature sequence comprises a predetermined lease time length sequence, a predetermined lease number sequence and a predetermined lease use sequence;
specifically, a lease request list is read through a device lease platform, wherein the lease request list comprises M lease requests, and M is an integer greater than or equal to 1. And then, acquiring a preset period, wherein the preset period is a preset time period, the content of the preset period comprises a plurality of lease request data, and extracting a first lease request belonging to the preset period from the M lease requests, wherein the first lease request is any lease request belonging to the preset period. And analyzing the first lease request through a request analysis model to obtain first request features, and circularly acquiring corresponding first request features of all the first lease requests. Wherein the first request feature comprises a specific device use, number of uses, duration of use, etc. request features. The request analysis model is used for carrying out image recognition on the leasing request area in the preset leasing template, and a specific request feature is recognized by utilizing a character recognition unit in the request analysis model. Further, a predetermined request feature sequence is built based on the first request features, namely all the first request features are acquired, and the predetermined request feature sequence is built, wherein the predetermined request feature sequence comprises a predetermined lease time length sequence, a predetermined lease number sequence and a predetermined lease use sequence of a plurality of lease requests.
As shown in fig. 3, the method S20 provided in the embodiment of the present application further includes:
s21: reading a preset leasing template;
s22: analyzing the preset leasing template to obtain preset area positioning of preset characteristics, wherein the preset characteristics comprise duration characteristics, quantity characteristics and application characteristics;
s23: image interception is carried out on the first lease request based on the preset area positioning, so that a first preset area image is obtained;
s24: the first preset area image is identified through a character identification unit in the request analysis model, and a first identification result is obtained;
s25: and obtaining the first request feature according to the first recognition result.
Specifically, when the first request feature of each lease request is obtained, a predetermined lease template is read, wherein the predetermined lease template is a text template containing the lease request feature. Analyzing the preset leasing template to obtain preset area positioning of preset characteristics, wherein the preset characteristics comprise a duration characteristic, a quantity characteristic and a use characteristic. And then, carrying out image interception on the first lease request based on the preset area positioning to obtain a first preset area image. Further, the acquired first preset area image is identified by a character identification unit in the request analysis model, and a first identification result is obtained. The request analysis model is an image processing unit comprising a character recognition unit, the request analysis model recognizes an image of a fixed area in an image of a preset area through the character recognition unit, acquires character request information contained in the image, and further outputs a first recognition result. For example, the numbers and the characters of the leasing feature area of the user are identified, and the identification result, namely a first identification result, is obtained. Finally, the first request feature is obtained according to the first recognition result.
S40: constructing a device database of the target device, and storing the device database into a lease decision model, wherein the device database comprises N devices with history operation record identifications, and N is an integer greater than 1;
s50: analyzing the predetermined lease time length sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests;
s60: matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in the lease decision model to obtain a device lease decision; the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
Specifically, a device database of the target device is built, and the device database is stored in a lease decision model, wherein the device database comprises N devices with historical operation record identifiers, and N is an integer greater than 1. Wherein the lease decision model is composed of a device database and a request screening unit. Analyzing the predetermined lease time length sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests. Further, matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in the lease decision model to obtain a device lease decision. The equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests. Namely, decision-making equipment groups containing a plurality of lease requests in equipment lease decisions, wherein each decision-making equipment group corresponds to one lease request. And monitoring the equipment based on the Internet of things, acquiring the leasing quantity of the equipment and carrying out accurate matching of leasing requests. The quick response to the lease request is realized, the subjectivity of the processing process is further reduced, and the accuracy of equipment matching of the lease request is further improved.
As shown in fig. 2, the method S50 provided in the embodiment of the present application further includes:
s51: sequentially extracting a first lease duration and a first lease number in the first request feature;
s52: performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease duration sequence;
s53: performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease quantity sequence;
s54: extracting a first request duration of a first request in the predetermined lease duration sequence, and matching a first request number of the first request in the predetermined lease number sequence;
s55: weighting calculation is carried out on the first request duration and the first request quantity to obtain a first priority index;
s56: and generating a priority lease sequence according to the first priority index, and analyzing the priority lease sequence to obtain the candidate lease request.
Specifically, when a candidate lease request is acquired, sequentially extracting first lease duration and first lease quantity in all first request features of a predetermined period, and performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease duration sequence. And carrying out descending order arrangement of lease requests based on the first lease duration to obtain the predetermined lease quantity sequence. Extracting a first request duration of a first request in the predetermined lease duration sequence, and matching the first request number of the first request in the predetermined lease number sequence, so as to obtain lease request features ordered according to lease duration. And finally, carrying out weighted calculation on the first request duration and the first request quantity to obtain a first priority index. When the first priority index is obtained, the corresponding relation between the request duration and the duration score and the corresponding relation between the request quantity and the quantity score are set, wherein the specific corresponding relation can be set according to actual requirements. I.e. different request durations contain corresponding duration scores and different request amounts contain corresponding amount scores. And weighting calculation is carried out according to the acquired time length scores and the acquired quantity scores of the request features, wherein the time length scores and the quantity score weights can be set according to actual requirements. And finally, adding the product of the quantitative score and the corresponding weight on the basis of the product of the duration score and the corresponding weight to obtain a first priority index, wherein the higher the first priority index value is, the higher the priority of the corresponding lease request is. And finally, generating a priority lease sequence according to the first priority index, and analyzing the priority lease sequence to obtain the candidate lease request.
The method S50 provided in the embodiment of the present application further includes:
s57: setting equipment lease constraints based on the N pieces of equipment with the historical operation record identifiers;
s58: and the request screening unit sequentially extracts the priority lease sequences based on the equipment lease constraint to obtain the candidate lease request.
Specifically, when the priority lease sequence is analyzed to obtain the candidate lease request, setting equipment lease constraints based on the N pieces of equipment with the history operation record identification, wherein the equipment lease constraints are specific total number constraints of available equipment. And orderly extracting the priority lease sequence based on the equipment lease constraint by a request screening unit, and extracting a plurality of lease requests meeting the equipment lease constraint in the priority lease sequence to obtain the candidate lease requests. The method comprises the steps of obtaining the number of equipment demands ordered in a priority leasing sequence through a request screening unit, and obtaining a plurality of leasing requests meeting equipment leasing constraint according to the number of equipment demands. Therefore, the request with higher priority level is processed, and the condition that the number of devices is insufficient after the request is responded to the lease is avoided.
The method S60 provided in the embodiment of the present application further includes:
s61: extracting a first lease use in a first request feature, and constructing a predetermined lease use sequence according to the first lease use;
s62: randomly extracting a first candidate request in the candidate lease requests;
s63: matching a first candidate use of the first candidate request at the predetermined rental use sequence;
s64: traversing a first pattern of the first candidate usage in a predetermined pattern-usage list;
s65: and the device decision unit analyzes the N devices with the historical operation record identifiers based on the first mode to obtain a first decision device group of the first candidate request.
Specifically, when a decision of a device corresponding to a candidate request is obtained, a first lease use in a first request feature is extracted, and a predetermined lease use sequence is built according to the first lease use. That is, the first rental uses in all the first request features are acquired, and all the acquired first rental uses are assembled into a predetermined rental use sequence. Subsequently, a first candidate request of the candidate lease requests is randomly extracted. And according to the first candidate request in the candidate lease requests, matching the first candidate use of the first candidate request in a preset lease use sequence, namely matching the corresponding first candidate use. Traversing a first mode of the first candidate usage in a predetermined mode-usage list, wherein the predetermined mode-usage list comprises operation modes of a plurality of devices and corresponding usage lists, and matching the corresponding operation modes, namely the first mode, through the first candidate usage. And finally, the equipment decision unit analyzes the N pieces of equipment with the historical operation record identification based on the first mode to obtain a first decision equipment group of the first candidate request, so that accurate acquisition of equipment corresponding to the lease request is realized.
The method S65 provided in the embodiment of the present application further includes:
s651: extracting a first device in the N devices with the history operation record identifiers, wherein the first device corresponds to a first history operation record;
s652: the first historical operating record includes a first historical operating mode timing;
s653: counting the total duration belonging to the first mode in the first historical operation mode time sequence, and recording the total duration as a first mode duration;
s654: performing equipment ascending on the N pieces of equipment with the historical operation record identification based on the first mode duration to obtain a first equipment list;
s655: and determining a first equipment lease decision according to the first equipment list, wherein the first equipment lease decision comprises the first decision equipment group.
Specifically, when the device decision unit analyzes the N devices with the history running record identifiers based on the first mode to obtain the first decision device group of the first candidate request, the first device in the N devices with the history running record identifiers is extracted, and the first device corresponds to the first history running record, wherein the history running record identifiers are identifier data of the devices in a history running process, and the identifier data comprise device identifiers, idle identifiers and the like. The first historical operation record comprises a first historical operation mode time sequence, namely an operation duration sequence of each operation mode in a historical lease process. And then, counting the total duration belonging to the first mode in the first historical operation mode time sequence, and recording as the first mode duration, namely counting the total duration of the first mode, and recording as the first mode duration. Further, the N devices with the history operation record identifiers are subjected to device ascending based on the first mode duration, a first device list is obtained, namely, the N devices with the history operation record identifiers are ordered according to the first mode duration, the longer the operation duration is, the more forward the ordering of the corresponding devices is, the device ascending is obtained, and the first device list is obtained. And finally, determining a first equipment lease decision according to the first equipment list, acquiring a plurality of pieces of equipment which are ranked in front and meet the number of the demand equipment, and determining the first equipment lease decision. The first equipment lease decision comprises a first decision equipment group, wherein the first decision equipment group comprises one or more equipment which accords with a corresponding lease request. And the first equipment lease decision comprises a plurality of first decision equipment groups, wherein each first decision equipment group corresponds to one lease request.
The method S64 provided in the embodiment of the present application further includes:
s641: reading target equipment usage of the target equipment, wherein the target equipment usage comprises K operation modes, and K is an integer greater than 1;
s642: extracting a first operation mode in the K operation modes, and matching a first use group of the first operation mode;
s643: and establishing the preset mode-application list according to a first mapping relation between the first operation mode and the first application group.
Specifically, when the preset mode-purpose list is constructed, the purpose of the target equipment of all the current target equipment is read, wherein the purpose of the target equipment comprises K operation modes, the different operation modes correspond to one or more different purposes, and K is an integer greater than 1. Then, a first operation mode in the K operation modes is extracted and matched with a first application group of the first operation mode, namely, one operation mode of the K operation modes is randomly acquired, and the application group of the operation mode is acquired. Further, the predetermined mode-usage list is established according to a first mapping relationship between the first operation mode and the first usage group. The predetermined mode-use list includes the operation modes of the plurality of devices and the corresponding use list. And the rapid matching of the equipment corresponding to the leasing application is facilitated.
According to the technical scheme provided by the embodiment of the invention, the leasing request list is read through the equipment leasing platform. And acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request characteristic. And constructing a predetermined request feature sequence based on the first request feature, wherein the predetermined request feature sequence comprises a predetermined lease time length sequence, a predetermined lease number sequence and a predetermined lease use sequence. And constructing a device database of the target device, and storing the device database into a lease decision model, wherein the device database comprises N devices with historical operation record identifications, and N is an integer greater than 1. Analyzing the predetermined lease time length sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests. And matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in the lease decision model to obtain a device lease decision. The equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests. And monitoring the equipment based on the Internet of things, acquiring the leasing quantity of the equipment and carrying out accurate matching of leasing requests. The quick response to the lease request is realized, the subjectivity of the processing process is further reduced, and the accuracy of equipment matching of the lease request is further improved. The method and the device solve the technical problems that in the prior art, the processing efficiency of a device lease streaming process on lease requests is low, and the subjectivity of processing is high.
Example two
Based on the same inventive concept as the device full life cycle operation monitoring method based on the internet of things in the foregoing embodiment, the present invention further provides a system of the device full life cycle operation monitoring method based on the internet of things, where the system may be implemented by hardware and/or software, and may generally be integrated in an electronic device, for executing the method provided by any embodiment of the present invention. As shown in fig. 4, the system is communicatively connected to a device rental platform, and the device rental platform includes a target device therein, the system includes:
a lease request acquisition module 11, configured to read a lease request list through the equipment lease platform, where the lease request list includes M lease requests, and M is an integer greater than or equal to 1;
the request feature acquiring module 12 is configured to acquire a predetermined period, extract a first lease request belonging to the predetermined period from the M lease requests, and analyze the first lease request through a request analysis model to obtain a first request feature;
a request feature sequence obtaining module 13, configured to build a predetermined request feature sequence based on the first request feature, where the predetermined request feature sequence includes a predetermined lease duration sequence, a predetermined lease number sequence, and a predetermined lease usage sequence;
the device database construction module 14 is configured to construct a device database of the target device, and store the device database in a lease decision model, where the device database includes N devices with a history operation record identifier, and N is an integer greater than 1;
a candidate lease request acquisition module 15, configured to analyze the predetermined lease duration sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screen to obtain a candidate lease request;
a device lease decision acquisition module 16, configured to match, in the predetermined lease use sequence, a candidate lease use of the candidate lease request, and analyze, by using a device decision unit in the lease decision model, the candidate lease use to obtain a device lease decision; the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
Further, the candidate lease request acquisition module 15 is further configured to:
sequentially extracting a first lease duration and a first lease number in the first request feature;
performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease duration sequence;
performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease quantity sequence;
extracting a first request duration of a first request in the predetermined lease duration sequence, and matching a first request number of the first request in the predetermined lease number sequence;
weighting calculation is carried out on the first request duration and the first request quantity to obtain a first priority index;
and generating a priority lease sequence according to the first priority index, and analyzing the priority lease sequence to obtain the candidate lease request.
Further, the candidate lease request acquisition module 15 is further configured to:
setting equipment lease constraints based on the N pieces of equipment with the historical operation record identifiers;
and the request screening unit sequentially extracts the priority lease sequences based on the equipment lease constraint to obtain the candidate lease request.
Further, the request feature acquisition module 12 is further configured to:
reading a preset leasing template;
analyzing the preset leasing template to obtain preset area positioning of preset characteristics, wherein the preset characteristics comprise duration characteristics, quantity characteristics and application characteristics;
image interception is carried out on the first lease request based on the preset area positioning, so that a first preset area image is obtained;
the first preset area image is identified through a character identification unit in the request analysis model, and a first identification result is obtained;
and obtaining the first request feature according to the first recognition result.
Further, the equipment rental decision acquisition module 16 is further configured to:
extracting a first lease use in a first request feature, and constructing a predetermined lease use sequence according to the first lease use;
randomly extracting a first candidate request in the candidate lease requests;
matching a first candidate use of the first candidate request at the predetermined rental use sequence;
traversing a first pattern of the first candidate usage in a predetermined pattern-usage list;
and the device decision unit analyzes the N devices with the historical operation record identifiers based on the first mode to obtain a first decision device group of the first candidate request.
Further, the equipment rental decision acquisition module 16 is further configured to:
extracting a first device in the N devices with the history operation record identifiers, wherein the first device corresponds to a first history operation record;
the first historical operating record includes a first historical operating mode timing;
counting the total duration belonging to the first mode in the first historical operation mode time sequence, and recording the total duration as a first mode duration;
performing equipment ascending on the N pieces of equipment with the historical operation record identification based on the first mode duration to obtain a first equipment list;
and determining a first equipment lease decision according to the first equipment list, wherein the first equipment lease decision comprises the first decision equipment group.
Further, the equipment rental decision acquisition module 16 is further configured to:
reading target equipment usage of the target equipment, wherein the target equipment usage comprises K operation modes, and K is an integer greater than 1;
extracting a first operation mode in the K operation modes, and matching a first use group of the first operation mode;
and establishing the preset mode-application list according to a first mapping relation between the first operation mode and the first application group.
The included units and modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Example III
Fig. 5 is a schematic structural diagram of an electronic device provided in a third embodiment of the present invention, and shows a block diagram of an exemplary electronic device suitable for implementing an embodiment of the present invention. The electronic device shown in fig. 5 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. As shown in fig. 5, the electronic device includes a processor 31, a memory 32, an input device 33, and an output device 34; the number of processors 31 in the electronic device may be one or more, in fig. 5, one processor 31 is taken as an example, and the processors 31, the memory 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, in fig. 5, by bus connection is taken as an example.
The memory 32 is used as a computer readable storage medium for storing a software program, a computer executable program and modules, such as program instructions/modules corresponding to the method for monitoring the operation of the whole life cycle of the device based on the internet of things in the embodiment of the invention. The processor 31 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 32, i.e. implementing the above-mentioned method for monitoring the full life cycle operation of the device based on the internet of things.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. The equipment full life cycle operation monitoring method based on the Internet of things is characterized by being applied to an equipment full life cycle operation monitoring system, wherein the equipment full life cycle operation monitoring system is in communication connection with an equipment leasing platform, the equipment leasing platform comprises target equipment, and the equipment full life cycle operation monitoring method comprises the following steps:
reading a lease request list through the equipment lease platform, wherein the lease request list comprises M lease requests, and M is an integer greater than or equal to 1;
acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request characteristic;
establishing a predetermined request feature sequence based on the first request feature, wherein the predetermined request feature sequence comprises a predetermined lease time length sequence, a predetermined lease number sequence and a predetermined lease use sequence;
constructing a device database of the target device, and storing the device database into a lease decision model, wherein the device database comprises N devices with history operation record identifications, and N is an integer greater than 1;
analyzing the predetermined lease time length sequence and the predetermined lease number sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests;
matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through a device decision unit in the lease decision model to obtain a device lease decision;
the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
2. The method of claim 1, wherein the screening for candidate rental requests comprises:
sequentially extracting a first lease duration and a first lease number in the first request feature;
performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease duration sequence;
performing lease request descending arrangement based on the first lease duration to obtain the predetermined lease quantity sequence;
extracting a first request duration of a first request in the predetermined lease duration sequence, and matching a first request number of the first request in the predetermined lease number sequence;
weighting calculation is carried out on the first request duration and the first request quantity to obtain a first priority index;
and generating a priority lease sequence according to the first priority index, and analyzing the priority lease sequence to obtain the candidate lease request.
3. The method of claim 2, wherein the analyzing the priority lease sequence to obtain the candidate lease request comprises:
setting equipment lease constraints based on the N pieces of equipment with the historical operation record identifiers;
and the request screening unit sequentially extracts the priority lease sequences based on the equipment lease constraint to obtain the candidate lease request.
4. The method of claim 3, comprising, prior to the sequentially extracting the first lease duration and the first lease number in the first request feature:
reading a preset leasing template;
analyzing the preset leasing template to obtain preset area positioning of preset characteristics, wherein the preset characteristics comprise duration characteristics, quantity characteristics and application characteristics;
image interception is carried out on the first lease request based on the preset area positioning, so that a first preset area image is obtained;
the first preset area image is identified through a character identification unit in the request analysis model, and a first identification result is obtained;
and obtaining the first request feature according to the first recognition result.
5. The method of claim 1, wherein the deriving a device lease decision comprises:
extracting a first lease use in a first request feature, and constructing a predetermined lease use sequence according to the first lease use;
randomly extracting a first candidate request in the candidate lease requests;
matching a first candidate use of the first candidate request at the predetermined rental use sequence;
traversing a first pattern of the first candidate usage in a predetermined pattern-usage list;
and the device decision unit analyzes the N devices with the historical operation record identifiers based on the first mode to obtain a first decision device group of the first candidate request.
6. The method of claim 5, wherein the first decision device group that obtains the first candidate request comprises:
extracting a first device in the N devices with the history operation record identifiers, wherein the first device corresponds to a first history operation record;
the first historical operating record includes a first historical operating mode timing;
counting the total duration belonging to the first mode in the first historical operation mode time sequence, and recording the total duration as a first mode duration;
performing equipment ascending on the N pieces of equipment with the historical operation record identification based on the first mode duration to obtain a first equipment list;
and determining a first equipment lease decision according to the first equipment list, wherein the first equipment lease decision comprises the first decision equipment group.
7. The method of claim 6, further comprising, prior to traversing the first pattern of the first candidate use in the predetermined pattern-use list:
reading target equipment usage of the target equipment, wherein the target equipment usage comprises K operation modes, and K is an integer greater than 1;
extracting a first operation mode in the K operation modes, and matching a first use group of the first operation mode;
and establishing the preset mode-application list according to a first mapping relation between the first operation mode and the first application group.
8. The utility model provides a full life cycle operation monitoring system of equipment based on thing networking, its characterized in that, system and equipment lease platform communication connection, and including target equipment in the equipment lease platform, the system includes:
a lease request acquisition module, configured to read a lease request list through the equipment lease platform, where the lease request list includes M lease requests, and M is an integer greater than or equal to 1;
the request feature acquisition module is used for acquiring a preset period, extracting a first lease request belonging to the preset period from the M lease requests, and analyzing the first lease request through a request analysis model to obtain a first request feature;
a request feature sequence acquisition module, configured to construct a predetermined request feature sequence based on the first request feature, where the predetermined request feature sequence includes a predetermined lease duration sequence, a predetermined lease number sequence, and a predetermined lease usage sequence;
the equipment database construction module is used for constructing an equipment database of the target equipment and storing the equipment database into a lease decision model, wherein the equipment database comprises N pieces of equipment with historical operation record identifiers, and N is an integer greater than 1;
the candidate lease request acquisition module is used for analyzing the predetermined lease duration sequence and the predetermined lease quantity sequence through a request screening unit in the lease decision model, and screening to obtain candidate lease requests;
the equipment lease decision acquisition module is used for matching the candidate lease use of the candidate lease request in the predetermined lease use sequence, and analyzing the candidate lease use through an equipment decision unit in the lease decision model to obtain an equipment lease decision; the equipment lease decisions comprise decision equipment groups of all requests in the candidate lease requests.
9. An electronic device, the electronic device comprising:
a memory for storing executable instructions;
a processor configured to implement the method for monitoring operation of a full lifecycle of an internet of things-based device according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.
10. A computer readable medium having stored thereon a computer program, which when executed by a processor implements the method for monitoring the full life cycle operation of an internet of things based device according to any of claims 1-7.
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