CN116070844A

CN116070844A - Consumable material measuring and calculating method and device, electronic equipment and storage medium

Info

Publication number: CN116070844A
Application number: CN202211733313.9A
Authority: CN
Inventors: 袁苑; 李忠谕; 迟景升; 陈灏; 胡春英; 卢斌; 李锦明
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2022-12-30
Filing date: 2022-12-30
Publication date: 2023-05-05

Abstract

The disclosure relates to a consumable measurement method, a device, an electronic device and a storage medium, comprising: acquiring and analyzing user work order information, and determining service information and scene information; determining a corresponding judgment model according to the service information and the scene information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption quantity of consumables of different types corresponding to each business information and each scene information; based on the decision model, the predicted consumable type and the predicted consumable quantity are determined. Therefore, based on different business information and scene information, a corresponding judgment model can be formulated, and the type of consumable materials and the number of consumable materials used are determined.

Description

Consumable material measuring and calculating method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of data processing, and in particular relates to a consumable measurement method, a device, electronic equipment and a storage medium.

Background

The installation and maintenance of the broadband need to consume various maintenance consumables, wherein passive materials such as optical cables, tail fibers, network cables and the like cannot be used for quantity tracking and statistics audit by a network management system and the like an active terminal, and in actual use, the broadband needs to be flexibly cut and used according to a scene, and does not have standard metering and using units, so that the statistics and control difficulty is high.

At present, the consumption condition of dress dimension consumptive material can only rely on dress dimension personnel scene manual measurement and record, and consuming time and effort can't carry out automatic accurate measurement and audit management for the accuracy of material management is constantly reduced along with dress dimension consumptive material application scene is extensive and the further increase of kind model, quantity.

Disclosure of Invention

The disclosure provides a consumable measurement and calculation system, a method, a device, electronic equipment and a storage medium, which are used for at least solving the problems that the consumption condition of maintenance consumables in the related technology can only be measured and recorded manually on site by maintenance personnel, the consumed time and the consumed force are consumed, automatic accurate measurement and audit management cannot be performed, and the accuracy of material management and control is continuously reduced. The technical scheme of the present disclosure is as follows:

according to a first aspect of embodiments of the present disclosure, there is provided a consumable part measuring and calculating method, including:

acquiring and analyzing user work order information, and determining service information and scene information;

determining a corresponding judgment model according to the service information and the scene information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption amounts of consumables of different types corresponding to each business information and each scene information;

and determining the type and the number of the predicted consumable materials based on the judgment model.

Optionally, before determining the decision model corresponding to the current scene according to the service information and the scene information, the method includes:

acquiring historical work order information; the historical work order information comprises consumption quantity of various consumable materials in corresponding business information and scene information;

analyzing the historical work order information, and determining consumption average values of consumable materials of different types corresponding to each business information and each scene information, wherein the consumption average values are used as initial clustering centers of consumable materials of different types corresponding to each business information and each scene information;

and carrying out iterative adjustment on the initial clustering center, and generating a judgment model based on an adjustment result.

Optionally, the performing iterative adjustment on the initial cluster center, generating a decision model based on an adjustment result, includes:

acquiring new historical work order information;

determining the distance between the consumption value of each type of consumable in the new historical work order information and an initial clustering center corresponding to the target business information and the target scene information aiming at the target business information and the target scene information corresponding to the new historical work order information;

and carrying out iterative adjustment on the corresponding initial clustering center according to the distance until the iterative times reach a preset threshold value, and taking the updated corresponding initial clustering center as the consumption quantity of consumable materials of each type in the target business information and the target scene information to obtain a judgment model.

Optionally, in the case that the decision model corresponds to a non-terminated butterfly drop cable, the determining, based on the decision model, a predicted consumable type and a predicted consumable number includes:

acquiring a first length between a passive optical network port and an optical network unit and a second length between the passive optical network port and an optical splitter;

and determining the predicted consumable length of the non-terminating butterfly-shaped lead-in optical cable according to the difference between the first length and the second length.

Optionally, the acquiring a first length between a passive optical network port and an optical network unit, and a second length between the passive optical network port and an optical splitter includes:

triggering an automatic ranging process when an optical network unit registers, and acquiring a first length between a passive optical network port and the optical network unit;

and adding a ranging process in the first single-hanging process of the optical splitter to obtain a second length between the passive optical network port and the optical splitter.

Optionally, after determining the predicted consumable type and the predicted consumable number based on the decision model, the method further includes:

determining consumable charge according to the predicted consumable type and the predicted consumable quantity;

and carrying out early warning based on the consumable charge.

According to a second aspect of embodiments of the present disclosure, there is provided a consumable part measuring and calculating device, including:

the acquisition module is used for acquiring and analyzing the user work order information and determining service information and scene information;

the determining module is used for determining a corresponding judgment model according to the service information and the scene information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption amounts of consumables of different types corresponding to each business information and each scene information;

and the calculation module is used for determining the type of the predicted consumable and the number of the predicted consumable based on the judgment model.

According to a third aspect of embodiments of the present disclosure, there is provided a consumable measurement electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the consumable part measurement method of any one of the above.

According to a fourth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, which when executed by a processor of a consumable measurement electronic device, causes the consumable measurement electronic device to perform any one of the consumable measurement methods.

According to a fifth aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer programs/instructions which when executed by a processor implement the consumable evaluation method of any one of the above.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

acquiring and analyzing user work order information, and determining service information and scene information; determining a corresponding judgment model according to the service information and the scene information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption quantity of consumables of different types corresponding to each business information and each scene information; based on the decision model, the predicted consumable type and the predicted consumable quantity are determined.

Therefore, based on different business information and scene information, a corresponding judgment model can be formulated, and the type of consumable materials and the number of consumable materials used are determined.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.

FIG. 1 is a flow chart illustrating a consumable part measurement method according to an example embodiment.

FIG. 2 is a logical schematic diagram illustrating a consumable part measurement method according to an example embodiment.

FIG. 3 is a flowchart illustrating a K-means clustering algorithm, according to an exemplary embodiment.

Fig. 4 is a schematic diagram of a butterfly drop cable length calculation logic, shown in accordance with an example embodiment.

Fig. 5 is a block diagram illustrating a consumable part measuring device according to an example embodiment.

FIG. 6 is a block diagram illustrating an electronic device for consumable part measurement, according to an example embodiment.

FIG. 7 is a block diagram illustrating an apparatus for consumable part measurement, according to an example embodiment.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

FIG. 1 is a flow chart illustrating a consumable part measurement method, as shown in FIG. 1, according to an exemplary embodiment, comprising:

in step S11, user work order information is acquired and parsed, and service information and scene information are determined.

The installation and maintenance of the broadband consumes various maintenance consumables, including passive materials such as optical cables, tail fibers, network cables and the like, and active terminals such as optical cats and the like.

However, the passive materials cannot be subjected to quantity tracking and statistics auditing by a network management system and other systems like an active terminal, and are flexibly cut and used according to a scene in actual use, and do not have standard metering use units, so that the statistics management and control difficulty is further increased.

In the step, when a work order required by a user is received, key labels corresponding to broadband service information, current scene information, user house type information and the like can be acquired and converted through voice recognition and an artificial intelligent text recognition algorithm.

For example, the key tag content can be extracted by carrying out semantic analysis after converting the real-time call record of the user and the customer service into the text, or identifying key information in the work order content by the artificial intelligent text.

The business information and the scene information can be used for a subsequent matching judgment model, and the consumable type and the consumable quantity corresponding to the user work order information are further obtained according to the user work order information.

For example, the service information may be service types, and the service types include six categories of broadband/fixed phone/ITV mobile, full house WIFI, intelligent housekeeping (indoor/outdoor);

the coverage scene comprises two main types of full coverage and thin coverage, and consumable type judgment models formulated under different service types and coverage scenes are correspondingly different.

In step S12, a corresponding decision model is determined according to the service information and the scene information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption quantity of consumable materials of different types corresponding to each business information and each scene information.

In this step, the decision model is matched according to the service information and the scene information. Specifically, an artificial intelligent K-means clustering analysis algorithm can be adopted for different consumable types, and an initial clustering center is established by combining key data such as house type information, installation distance, historical mean value and the like; and acquiring a field measurement sample for model training, and selecting an optimal clustering center, namely the optimal consumable quantity in the scene through continuous iterative optimization.

In one implementation, before determining a decision model corresponding to a current scene according to service information and scene information, the method includes:

acquiring historical work order information; the historical work order information comprises consumption quantity of various consumable materials in the corresponding business information and scene information; analyzing the historical work order information, and determining consumption average values of consumable materials of different types corresponding to each business information and each scene information, wherein the consumption average values are used as initial clustering centers of consumable materials of different types corresponding to each business information and each scene information; and carrying out iterative adjustment on the initial clustering center, and generating a judgment model based on an adjustment result.

That is, the important material use quantity average value under various business, various coverage scenes, various house types and various installation distance sections is collected according to the historical worksheet classification, and an initial mapping table is formed by network cables, tail fibers, end-forming lead-in optical cables, hot melt sleeves, crystal heads and the like; and then, according to the initial mapping table, acquiring the numerical value under each associated scene as an initial clustering center of K classes.

In one implementation, performing iterative adjustment on an initial cluster center, generating a decision model based on an adjustment result, including:

acquiring new historical work order information; aiming at target business information and target scene information corresponding to the new historical work order information, determining the distance between the consumption value of each type of consumable in the new historical work order information and an initial clustering center corresponding to the target business information and the target scene information; and carrying out iterative adjustment on the corresponding initial clustering center according to the distance until the iterative times reach a preset threshold value, and taking the updated corresponding initial clustering center as the consumption quantity of consumable materials of each type in the target business information and the target scene information to obtain a judgment model.

That is, the distances from the numerical value under the same scene to the initial clustering centers of the corresponding categories in the historical worksheet are calculated respectively, and the clustering centers of K categories are calculated again; then, sampling to obtain a field actual measurement sample, and performing model training to perform continuous iterative optimization; and calculating a standard measure function until the maximum iteration number is reached, and determining an optimal clustering center, namely the optimal consumable quantity in the scene.

In step S13, the predicted consumable type and the predicted consumable number are determined based on the decision model.

In one implementation, determining the predicted consumable type and the predicted consumable number based on the decision model in a case where the decision model corresponds to a non-terminated butterfly drop cable includes:

acquiring a first length between a passive optical network port and an optical network unit and a second length between the passive optical network port and an optical splitter; and determining the predicted consumable length of the non-terminated butterfly-shaped lead-in optical cable according to the difference between the first length and the second length.

In one implementation, obtaining a first length between a passive fiber network port and an optical network unit and a second length between the passive fiber network port and an optical splitter includes:

triggering an automatic ranging process when an optical network unit registers, and acquiring a first length between a passive optical network port and the optical network unit; and adding a ranging process in the first single-hanging process of the optical splitter to obtain a second length between the passive optical network port and the optical splitter.

Specifically, firstly, in an OBD (Optical Branching Device ) first single-hanging measurement process, a ranging process is added in an ONU (Optical Network Unit ) registration process of the first single-hanging measurement process to obtain all the last-stage OBD hanging measurement distances of the current network which are opened, and the last-stage OBD hanging measurement distances are correspondingly stored in a last-stage OBD optical distance table according to OBD codes;

then, when ONU registration is carried out in the process of opening the work order installation, the ONU is synchronously and automatically triggered to an OLT (optical line terminal ) network manager to trigger an automatic ranging process, and the OLT network manager returns a corresponding optical distance value to the system for recording;

furthermore, the ONU serial code of the work order can be automatically identified, tracing is carried out according to the topological relation of the resource tree, and the final-stage OBD code of the ONU is automatically associated. And automatically searching and returning the corresponding optical distance value of the OBD from the final-stage OBD optical distance table according to the OBD code, and automatically calculating the optical distance difference value of the ONU and the final-stage OBD optical distance to obtain the length of the lead-in optical cable.

In one implementation, after determining the predicted consumable type and the predicted consumable number based on the decision model, the method further includes: determining consumable charge according to the predicted consumable type and the predicted consumable quantity; early warning is carried out based on consumable material cost.

That is, the bill of the using amount of various consumable materials can be output according to the work order, the unit price of the matched materials can calculate the charge amount, the charge amount can be compared with the actual input cost of the ground city in the transverse direction and checked in the longitudinal direction, and the automatic early warning and pressure drop of the charge amount can be realized.

From the above, it can be seen that the technical scheme that the embodiment of the present disclosure provided, can be based on different business information and scene information, formulate corresponding judgement model, and then confirm consumable type and the consumptive material quantity that use, compare in the mode of relying on dress dimension personnel scene to manually measure and record in the correlation technique, this application calculation accuracy is high, the data error is little, can realize artifical zero intervention, the data can backtrack, the accuracy is high, can significantly reduce the work load of a line dress dimension personnel, realize the automatic audit management of material.

As shown in fig. 2, the method in the embodiment of the present invention will be described in the following with a specific embodiment, which specifically includes:

step 1: based on different service information and scene information, associating material inventory information, making a corresponding judgment model, and determining the type of consumable used.

The service information comprises six categories, namely broadband/fixed telephone/ITV (integrated wireless television) mobile installing and transferring machine, full-house WIFI and intelligent housekeeping (indoor/outdoor); the scene information includes two major categories, full coverage and thin coverage. Consumable type models formulated under different service information and scene information correspond to different types.

Step 2: except for the non-end butterfly-shaped lead-in optical cable, the AI intelligent K-means clustering analysis algorithm is adopted for quantity measurement and calculation aiming at different consumable types, and the specific steps are as shown in fig. 3:

2.1: acquiring the average value of the number of important material usage under various services, various scene information, various house types and various installation distance sections according to the historical worksheet classification, wherein the average value comprises network cables, tail fibers, end-forming lead-in optical cables, hot melt sleeves, crystal heads and the like, so as to form an initial mapping table;

2.2: acquiring numerical values under each associated scene according to the initial mapping table, and taking the numerical values as initial clustering centers of K classes;

2.3: respectively calculating the distances from the numerical value under the same scene in the historical work order to the initial clustering centers of the corresponding categories, and calculating the clustering centers of K categories again;

2.4: sampling to obtain a field actual measurement sample, and performing model training to perform continuous iterative optimization;

2.5: and calculating a standard measure function until the maximum iteration number is reached, and determining an optimal clustering center, namely the optimal consumable quantity in the scene.

Step 3: aiming at the 'non-end butterfly-shaped lead-in optical cable', the length of the lead-in optical cable is automatically calculated by a calculation method of the difference between the light distance of the lead-in ONU and the light distance of the final-stage OBD.

The specific steps are shown in fig. 4:

3.1: in the OBD first single-hanging measurement process, a ranging process is added in an ONU registration process of the first single-hanging measurement to obtain all the open final-stage OBD hanging measurement distances of the current network, and the final-stage OBD light distance list is correspondingly stored according to OBD codes;

3.2: when ONU registration is carried out in the process of opening the work order installation, the ONU is synchronously and automatically triggered to the OLT network management to automatically measure the distance, and the OLT network management returns the corresponding optical distance value to the system for recording;

3.3: the system automatically recognizes the ONU serial code of the work order, backtracks the source according to the topological relation of the resource tree, and automatically associates the final-stage OBD code to which the ONU belongs.

And automatically searching and returning the corresponding optical distance value of the OBD from the final-stage OBD optical distance table according to the OBD code, and automatically calculating the optical distance difference value of the ONU and the final-stage OBD optical distance to obtain the length of the lead-in optical cable.

Step 4: aiming at a work order required by a user, acquiring and converting through voice recognition and AI text recognition algorithms, analyzing and acquiring key labels corresponding to broadband service information, current scene information (full coverage/thin coverage), user house type information and the like, and matching a consumable judgment model in the step 1, so that the optimal consumable use quantity obtained in the step 2 through calculation is further matched.

The user demands and the worksheet can be set up through public numbers, customer service telephones of operators, portal websites of operators and other channels, and the worksheet is formed.

Further, the key label content is extracted by carrying out semantic analysis after converting the voice recording of the real-time conversation between the user and the customer service or identifying key information in the work order content by the AI text.

Step 5: according to the list of the using quantity of various consumables output by the work order, matching the unit price of the materials to calculate the charge amount, and carrying out transverse comparison and longitudinal audit on the charge amount as a measuring and calculating basis and the cost of the ground and the market to realize automatic early warning of the charge amount and pressure drop.

In this embodiment, based on different service types and coverage scenarios, a corresponding decision model is formulated, and the type of consumable used is determined. The service types comprise six categories, namely broadband/fixed telephone/ITV (integrated wireless television) mobile installation, full-house WIFI and intelligent housekeeping (indoor/outdoor); the overlay scene includes two broad categories of full overlay and thin overlay. The consumable type judgment models formulated under different service types and coverage scenes are correspondingly different.

Moreover, aiming at different consumable types (except for 'non-end butterfly-shaped lead-in optical cable', automatic ranging can be performed), an AI intelligent K-means clustering analysis algorithm is adopted, and key data such as house type information, installation distance, historical average value and the like are combined to establish an initial clustering center; and acquiring a field measurement sample for model training, and selecting an optimal clustering center, namely the optimal consumable quantity in the scene through continuous iterative optimization.

Aiming at the 'non-end butterfly-shaped lead-in optical cable', the length of the lead-in optical cable is automatically calculated by a calculation method of the difference between the light distance of the lead-in ONU and the light distance of the final-stage OBD.

When a work order required by a user is received, acquiring and converting through voice recognition and AI text recognition algorithms, analyzing and acquiring key labels corresponding to broadband service types, current coverage scenes (full coverage/thin coverage), user house type information and the like, and using the key labels to match the judgment model in the step 1, and further acquiring consumable types and consumable quantity corresponding to the work order according to the step 2.

According to the list of the using quantity of various consumables output by the work order, matching the unit price of the materials to calculate the charge amount, and carrying out transverse comparison and longitudinal audit according to the actual input cost of the ground city as a measuring and calculating basis to realize automatic early warning of the charge amount and pressure drop.

FIG. 5 is a block diagram of a consumable metering device, according to one example embodiment, comprising:

an acquisition module 201, configured to acquire and parse user work order information, and determine service information and scene information;

a determining module 202, configured to determine a corresponding decision model according to the service information and the scenario information; the judgment model is obtained by carrying out cluster analysis on historical consumable data and is used for determining consumption amounts of consumables of different types corresponding to each business information and each scene information;

and the calculating module 203 is configured to determine the predicted consumable type and the predicted consumable quantity based on the decision model.

Optionally, the determining module 202 is configured to:

acquiring new historical work order information;

Optionally, in the case that the decision model corresponds to a non-terminated butterfly drop cable, the computing module 203 is configured to:

Optionally, the computing module 203 is configured to:

and carrying out early warning based on the consumable charge.

In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory, comprising instructions executable by a processor of an electronic device to perform the method. Alternatively, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In an exemplary embodiment, a computer program product is also provided which, when run on a computer, causes the computer to implement the method of consumable part measurement.

Fig. 7 is a block diagram illustrating an apparatus 800 for consumable part measurement, according to an example embodiment.

For example, apparatus 800 may be a mobile phone, computer, digital broadcast electronic device, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.

Referring to fig. 7, apparatus 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the apparatus 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the described methods. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Power supply component 807 provides power to the various components of device 800. Power supply component 807 can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for device 800.

The multimedia component 808 includes a screen between the device 800 and the account that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from an account. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the device 800 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, click wheel, button, or the like. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the apparatus 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the apparatus 800, the sensor assembly 814 may also detect a change in position of the apparatus 800 or one component of the apparatus 800, the presence or absence of an account in contact with the apparatus 800, an orientation or acceleration/deceleration of the apparatus 800, and a change in temperature of the apparatus 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communication between the apparatus 800 and other devices, either in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic elements for executing the methods described in the first and second aspects.

In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of apparatus 800 to perform the method. Alternatively, for example, the storage medium may be a non-transitory computer-readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

In an exemplary embodiment, a computer program product containing instructions is also provided, which when run on a computer, causes the computer to perform the consumable part measuring method of any of the embodiments.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A consumable part measuring and calculating method, comprising:

2. The consumable measurement method according to claim 1, wherein before determining the decision model corresponding to the current scene according to the service information and the scene information, the method comprises:

3. The consumable measurement method of claim 2, wherein iteratively adjusting the initial cluster center generates a decision model based on an adjustment result, comprising:

acquiring new historical work order information;

4. The consumable measurement method of claim 1, wherein, in the case where the decision model corresponds to a non-terminated butterfly drop cable, the determining the predicted consumable type and the predicted consumable quantity based on the decision model comprises:

5. The consumable measurement method of claim 4, wherein the obtaining a first length between a passive fiber network port and an optical network unit and a second length between the passive fiber network port and an optical splitter comprises:

6. The consumable measurement method of claim 1, wherein after determining the predicted consumable type and the predicted consumable quantity based on the decision model, further comprising:

and carrying out early warning based on the consumable charge.

7. A consumable measurement device, comprising:

8. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the consumable measurement method of any one of claims 1 to 6.

9. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of a consumable evaluation electronic device, enable the consumable evaluation electronic device to perform the consumable evaluation method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the consumable evaluation method of any one of claims 1 to 6.