CN113128809A - Computer room evaluation method and device and electronic equipment - Google Patents
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Abstract
The application discloses a machine room evaluation method and device and electronic equipment. The method comprises the following steps: collecting multiple items of basic data of a machine room, and determining reference values of the current various items of basic data of the machine room according to the collected multiple items of basic data; obtaining the current reference value of each value influence factor of the machine room according to the reference value of each item of basic data and the weight of each value influence factor of each item of basic data, wherein the value influence factors comprise: revenue contribution factors, cost occupancy factors and machine room attribute factors; obtaining the type of the machine room, and obtaining the current reference value of the machine room according to the reference value of each value influence factor and the weight of each value influence factor to the type of the machine room, wherein the type of the machine room comprises: a convergence machine room, an access network machine room, or a resource point; and obtaining an evaluation value of the machine room according to the current reference value of the machine room and the weight of the machine room.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for evaluating a computer room, and an electronic device.
Background
With the rapid development of markets and services, the number of machine rooms is increased sharply to support related work, and the number and quality of machine rooms directly or indirectly affect the cost and quality of service. In order to focus on client perception and network value, network cost reduction and efficiency improvement work is effectively supported, high-value areas are accurately identified and sequenced, and carelessness, optimization and cost control are purposefully achieved. And the value machine room is rapidly identified, the value of the machine room is comprehensively analyzed according to the network element and the customer importance, and the analysis result can provide efficiency fee data support for electricity renting decision, point selection evaluation, base station disassembly and movement compensation optimization evaluation and other works.
At present, the method for evaluating the value of a machine room is as follows: 1. dividing a machine room to be evaluated into two major categories, namely a core machine room and an access network machine room according to the hierarchy of the governed important network element equipment in a network; 2. for the core machine room: experts of related departments and branch companies in each city collaboratively roughly grade the value of the machine room through machine room environment management, comprehensive level of power supply matching health and the like (the grades are sequentially divided into five grades of five stars, four stars, three stars, two stars and one star from high to low); 3. for the access network machine room: experts of related departments and each city branch company manually evaluate and score according to scenes, services, network elements and the like of a machine room, and rank the relative departments and each city branch company according to total scores of various dimensions.
Therefore, at present, the evaluation of the machine room value is mainly carried out by the experience of experts and simple scoring ranking through some management methods, the value evaluation is carried out through less data volume and dimensionality, and the evaluation accuracy is low.
Disclosure of Invention
The embodiment of the application provides a method and a device for evaluating a machine room and electronic equipment, and aims to solve the problem that the accuracy of machine room evaluation is low in the prior art.
In order to solve the technical problem, the invention is realized as follows:
in a first aspect, a machine room evaluation method is provided, including: collecting multiple items of basic data of a machine room, and determining the current reference value of each item of basic data of the machine room according to the collected multiple items of basic data; obtaining the current reference value of each value influence factor of the machine room according to the reference value of each item of the basic data and the weight of each value influence factor of each item of the basic data, wherein the value influence factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors; obtaining the type of the machine room, and obtaining the current reference value of the machine room according to the reference value of each value influence factor and the weight of each value influence factor to the type of the machine room, wherein the type of the machine room comprises: a convergence machine room, an access network machine room, or a resource point; and obtaining the evaluation value of the machine room according to the current reference value of the machine room and the weight of the machine room.
In a second aspect, there is provided a machine room evaluation apparatus comprising: the acquisition module is used for acquiring multiple items of basic data of the machine room; the determining module is used for determining the reference value of each item of current basic data of the machine room according to the acquired multiple items of basic data; a first obtaining module, configured to obtain a reference value of each current value influencing factor of the machine room according to a reference value of each item of the basic data and a weight of each value influencing factor of each item of the basic data, where the value influencing factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors; a second obtaining module, configured to obtain a type to which the machine room belongs, and obtain a current reference value of the machine room according to the reference value of each value influencing factor and a weight of each value influencing factor on the type to which the machine room belongs, where the type to which the machine room belongs includes: a convergence machine room, an access network machine room, or a resource point; and the third acquisition module is used for obtaining the evaluation value of the machine room according to the current reference value of the machine room and the weight of the machine room.
In a third aspect, an electronic device is provided, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the method according to the first aspect.
In the embodiment of the application, multiple items of basic data of the machine room are collected, the multiple items of basic data of the machine room are converted into income contribution factors, cost occupation factors and machine room attribute factors which affect the value of the machine room, and the three kinds of data are comprehensively calculated, so that the evaluation value of the machine room is obtained, the value evaluation value of the machine room is obtained according to the actual use condition of the machine room, and the accuracy of the value evaluation of the machine room is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic flow chart of a machine room evaluation method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a value distribution of a computer room according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a machine room evaluation apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the application provides a machine room evaluation method and device and electronic equipment, aiming at the problem that the machine room evaluation accuracy is low in the prior art. According to the method, multiple items of basic data of the machine room are converted into income contribution degree, cost occupation condition and machine room attribute which affect the value of the machine room, and the three data are comprehensively calculated, so that the value evaluation value of the machine room is obtained.
Fig. 1 is a schematic flow chart of a machine room evaluation method in an embodiment of the present application, and as shown in fig. 1, the method mainly includes the following steps 101 to 104.
In step 101, a plurality of items of basic data of a machine room are collected, and a reference value of each item of current basic data of the machine room is determined according to the collected plurality of items of basic data.
In an optional implementation manner of this embodiment, the three-fee audit data may be used as reference data, and the resource management data, the contract data, and the financial reimbursement bill data are respectively added, and the resource management data, the contract data, and the financial reimbursement bill data are used as basic data.
In step 101, the values of the current basic data items of the machine room can be evaluated according to the specific values of the basic data items, so as to obtain the reference values of the basic data items.
In step 102, obtaining a reference value of each current value influencing factor of the machine room according to a reference value of each item of the basic data and a weight of each value influencing factor of each item of the basic data, where the value influencing factors include: revenue contribution factors, cost occupancy factors, and machine room attribute factors.
In an alternative implementation of this embodiment, the reference value X for the ith value influencing factori：
In step 103, obtaining a type to which the machine room belongs, and obtaining a current reference value of the machine room according to the reference value of each value influence factor and the weight of each value influence factor on the type to which the machine room belongs, wherein the type to which the machine room belongs includes: a convergence room, an access network room, or a resource point.
In an optional implementation manner of this embodiment, the current reference value X of the machine room:
In step 104, an evaluation value of the machine room is obtained according to the current reference value of the machine room and the weight of the machine room.
In an alternative implementation of this embodiment, the machine room value X Rx。
Wherein X represents the current reference value of the machine room; rxRepresenting the current value weight of the room.
In the embodiment of the application, multiple items of basic data of the machine room are collected, the multiple items of basic data of the machine room are converted into income contribution factors, cost occupation factors and machine room attribute factors which affect the value of the machine room, and the three kinds of data are comprehensively calculated, so that the evaluation value of the machine room is obtained, the value evaluation value of the machine room is obtained according to the actual use condition of the machine room, and the accuracy of the value evaluation of the machine room is improved.
In an optional implementation manner, the weight of each item of basic data to each value influence factor and the weight of each value influence factor to the type of the computer room may be implemented by constructing a determination matrix, and therefore, in the optional implementation manner, before step 102, the method may further include: for each value influence factor, constructing a first judgment matrix of the plurality of items of basic data on the value influence factor according to a preset judgment matrix construction rule, and obtaining the weight of each item of basic data on the value influence factor according to the first judgment matrix; prior to step 103, the method further comprises: and according to the judgment matrix construction rule, constructing a second judgment matrix of each value influence factor on the type of the machine room, and according to the second judgment matrix, obtaining the weight of each basic data on the value influence factor.
In the foregoing optional implementation manner, optionally, the determining the matrix construction rule may include: for decision matrix A: a ═ aij)n×n，aij>0，aij×ajiWhen i is j, a is 1ij＝aji1, wherein aijIndicating the relative importance of the ith element and the jth element to the upper layer factor, and n is the number of elements participating in the reference value of the upper layer factor.
In the above alternative implementation, aijThe value of (a) can be determined by means of expert scoring, for example, a can be definedijThe value range is as follows:
wherein, when the importance of the element i relative to the upper level factor is equal to the element j, aij＝1；
When the importance of the element i to the upper level factor is slightly more important than the element J, aij＝3；
When element i is more important than element j relative to the upper level factor, aij＝5；
When element i is more important than element j relative to the upper level factor, aij＝7；
When the importance of element i to the upper level factor is more important than that of element j, aij＝9；
When the importance of the element i relative to the factor of the previous level is between a and j relative to the factor of the previous levelij2m-l and aijWhen the angle is 2m +1, aij＝2m，m＝1,2,3,4；
If and only if aijM, m 1, 2, …, 9, then aij＝1/m。
For example, the decision matrix for 6 underlying data versus one of the value contributors may be:
when the weights are obtained from the decision matrix, the weights can be obtained by calculating n × 1 type zero matrices of the decision matrix, for example, for the decision matrix of 6 items of basic data on one of the value influencing factors, calculating Q ═ zeros (6,1) to obtain a 6-item one-dimensional matrix:
the values of the elements in the matrix are the weights of the basic data to the value influencing factors.
In practical application, a value evaluation model can be constructed, and comprises three stages: the first level (target level) factors comprise three types of convergence machine rooms, access network machine rooms and resource points, the second level (criterion level) factors comprise income contribution degree, cost occupation condition and machine room attributes, and the third level (scheme level) factors comprise basic data such as resource management data, contract data, financial reimbursement bill data, network element work parameters, telephone traffic, traffic data, solidified contract data, ERP contract data and white list data.
Then, a judgment matrix (namely, each factor of the second level and the first level) of each specific factor is constructed:
for the ith element and the jth element, when comparing the importance of the specific factors of the previous level, the relative weight a of the number quantization is adoptedijTo indicate. Assuming that a total of n elements are involved in the comparison, then let A ═ a (a)ij)n×nIs a pair-wise comparison matrix (decision matrix). The characteristics of the judgment matrix are as described above, and are not described again.
And obtaining each judgment matrix according to the construction mode of the judgment matrix.
In practical applications, when the pair comparison matrix a is obtained by comparing each other for each factor of a complex system, it is difficult to satisfy many consistency conditions, and therefore, the pair comparison matrix is generally required to have a certain consistency or approximate consistency, i.e., to allow an error to exist. By comparing the identity of matrix a in pairs, the degree to which its error or inconsistency exists can be measured. Therefore, in an optional implementation manner, before obtaining the weight of each item of the basic data on the price influence factor according to the first determination matrix, the method further includes: according to the maximum characteristic root of the first judgment matrix, obtaining a consistency index of the first judgment matrix, and if the consistency index of the first judgment matrix is larger than a preset correction value, adjusting the value of an element in the first judgment matrix until the consistency index of the first judgment matrix is larger than the preset correction value; before obtaining the weight of each item of the basic data on the price influence factor according to the second judgment matrix, the method further includes: and acquiring a consistency index of the second judgment matrix according to the maximum characteristic root of the second judgment matrix, and if the consistency index of the second judgment matrix is larger than a preset correction value, adjusting the values of elements in the second judgment matrix until the consistency index of the second judgment matrix is larger than the preset correction value.
In an alternative implementation, the mean value of the largest feature root of the judgment matrix may be used as an index CI for checking the consistency of the matrix a:
the larger the CI value, the worse the perfect agreement of the pair-wise comparison matrix a, i.e., the larger the deviation obtained by using the eigenvector as the weight vector.
When lambda ismaxWhen n, CI is 0, a is considered to be an uniform array:
when lambda ismaxIs slightly larger than n and CI<When the value is 0.1, the consistency of the paired comparison matrixes A is in an acceptable range, and the characteristic vector of A can be used as a weight vector;
when lambda ismax>n and CI>And O.1, indicating that the consistency of the paired comparison matrix A exceeds an acceptable range, and needing to perform mutual comparison two by two to obtain the paired comparison matrix.
In practical application, it is found that the larger the dimension of the pair-wise comparison matrix is, the worse the consistency of the matrix a is, and therefore, in an alternative implementation, the consistency requirement on the high-dimension judgment matrix can be properly relaxed, and the correction value RI is added to correct the consistency check result. In this alternative implementation, the RI may average a random consistency index, which is related only to the matrix order. For example, the correction values of RI are shown in table 1.
Table 1.
| Order of A | 1 | 2 | 3 | 4 | 5 | 6 |
| |
0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 |
In practical application, a predetermined correction value can be obtained according to the dimension of the current judgment matrix, and the judgment matrix is corrected according to the predetermined correction value.
In an optional implementation manner, different value combinations can be divided according to the value of the machine room in a certain area to obtain a subdivided machine room group. For example, for a machine room in a certain region, the value results of the machine room obtained finally are shown in table 2.
Table 2.
| Number of segments | Number of machine rooms | Ratio of occupation of |
| More than 80 minutes | 39 | 2.92％ |
| 60-80 | 635 | 47.60％ |
| 40-60 | 575 | 43.10％ |
| Less than 40 | 85 | 6.37％ |
For example, the subdivision may be performed by a K-means method, when the number of categories (the value of the parameter K) of the K-means clustering subdivision is selected, if the value of K is too small, that is, the categories are less than the best state of subdivision, if the value of K is too large, that is, the categories are more than the number of categories, it is not convenient to manage the machine room, and on the basis of observing the data distribution shown in fig. 2 below, it may be determined that the point locations are mainly clustered in five regions according to the clustering situation of the point locations, therefore, in an optional implementation manner of this embodiment, five K values (K1, K2, K3, K4, K5) may be selected, and according to the clustering result situation, 5 categories of machine room, a category, B category, C category, D category, and E category, are finally analyzed.
In an optional implementation, optimization may be performed for the machine room with the evaluation value lower than the predetermined value, and therefore, in this optional implementation, after step 104, the method further includes: and if the evaluation value of the machine room is lower than a preset value, obtaining and outputting an optimization scheme of the machine room according to a preset optimization strategy and by combining various basic data of the machine room.
For example, a certain E-type machine room in a certain area is obtained according to the above clustering and subdividing manner, and through itemizing the basic data of the E-type machine room, it is found that the machine room is higher in electricity fee and rent cost, and lower in data traffic, telephone traffic and annual revenue, and according to contract information, the machine room does not belong to an important scene machine room such as colleges and universities, political parties and the like, which indicates that the machine room needs to be optimized in a targeted manner, so that optimization strategies such as moving the machine room can be provided.
The embodiment of the application also provides a machine room evaluation device. Fig. 3 is a schematic structural diagram of a machine room evaluation apparatus according to an embodiment of the present application, and as shown in fig. 3, the machine room evaluation apparatus 300 includes: an acquisition module 310, a determination module 320, a first acquisition module 330, a second acquisition module 340, and a third acquisition module 350.
In this embodiment of the application, the collecting module 310 is configured to collect multiple items of basic data of a machine room; a determining module 320, configured to determine, according to the acquired multiple items of basic data, reference values of current items of basic data of the machine room; a first obtaining module 330, configured to obtain a reference value of each current value influencing factor of the machine room according to a reference value of each item of the basic data and a weight of each value influencing factor of each item of the basic data, where the value influencing factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors; a second obtaining module 340, configured to obtain a type to which the machine room belongs, and obtain a current reference value of the machine room according to the reference value of each value influencing factor and a weight of each value influencing factor on the type to which the machine room belongs, where the type to which the machine room belongs includes: a convergence machine room, an access network machine room, or a resource point; the third obtaining module 350 is configured to obtain an evaluation value of the machine room according to the current reference value of the machine room and the weight of the machine room.
The machine room evaluation apparatus 30 according to the embodiment of the present invention may execute the process corresponding to the method provided in the embodiment of the present application, and each unit/module and the other operations and/or functions in the machine room evaluation apparatus 30 are respectively for implementing the corresponding process in the method, and can achieve the same or equivalent technical effects, and for brevity, no further description is provided herein.
In an optional implementation manner, the method further includes: the first construction module is used for constructing a first judgment matrix of the plurality of items of basic data on the value influence factors according to a preset judgment matrix construction rule for each value influence factor; the first obtaining module 330 is further configured to obtain, according to the first determining matrix, weights of the basic data on the price influencing factors; the second construction module is used for constructing a second judgment matrix of each value influence factor on the type of the machine room according to the judgment matrix construction rule; the second obtaining module 340 is further configured to obtain, according to the second determination matrix, weights of the basic data on the price influencing factors.
In an optional implementation, the plurality of items of basic data include at least one of: the data processing system comprises data management data, contract data, account statement data, network element work parameters, telephone traffic, traffic data, contract data and white list data.
In an optional implementation manner, the determining matrix construction rule includes: for decision matrix A: a ═ aij)n×n，aij>0，aij×aijWhen i is j, a is 1ij＝aji1, wherein aijIndicating the relative importance of the ith element and the jth element to the upper layer factor, and n is the number of elements participating in the reference value of the upper layer factor.
In an optional implementation manner, the method further includes: the first correction module is used for acquiring a consistency index of the first judgment matrix according to the maximum characteristic root of the first judgment matrix, and if the consistency index of the first judgment matrix is larger than a preset correction value, adjusting the value of an element in the first judgment matrix until the consistency index of the first judgment matrix is larger than the preset correction value; and the second correction module is used for acquiring the consistency index of the second judgment matrix according to the maximum characteristic root of the second judgment matrix, and if the consistency index of the second judgment matrix is larger than a preset correction value, adjusting the value of an element in the second judgment matrix until the consistency index of the second judgment matrix is larger than the preset correction value.
In an optional implementation manner, the method further includes: and the optimization module is used for obtaining the evaluation value of the machine room, and if the evaluation value of the machine room is lower than a preset value, combining various basic data of the machine room according to a preset optimization strategy to obtain and output an optimization scheme of the machine room.
Fig. 4 is a schematic diagram of a hardware structure of an electronic device implementing various embodiments of the present invention.
The electronic device 400 includes, but is not limited to: radio frequency unit 401, network module 402, audio output unit 403, input unit 404, sensor 405, display unit 406, user input unit 407, interface unit 408, memory 409, processor 410, and power supply 411. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 4 does not constitute a limitation of the electronic device, and that the electronic device may include more or fewer components than shown, or some components may be combined, or a different arrangement of components. In the embodiment of the present invention, the electronic device includes, but is not limited to, a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted terminal, a wearable device, a pedometer, and the like.
Wherein, the processor 410 may be configured to:
collecting multiple items of basic data of a machine room, and determining the current reference value of each item of basic data of the machine room according to the collected multiple items of basic data;
obtaining the current reference value of each value influence factor of the machine room according to the reference value of each item of the basic data and the weight of each value influence factor of each item of the basic data, wherein the value influence factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors;
obtaining the type of the machine room, and obtaining the current reference value of the machine room according to the reference value of each value influence factor and the weight of each value influence factor to the type of the machine room, wherein the type of the machine room comprises: a convergence machine room, an access network machine room, or a resource point;
and obtaining the evaluation value of the machine room according to the current reference value of the machine room and the current weight of the machine room.
In the embodiment of the invention, the multiple items of basic data of the machine room are collected, the multiple items of basic data of the machine room are converted into the income contribution factor, the cost occupation factor and the machine room attribute factor which influence the value of the machine room, and the three data are comprehensively calculated, so that the evaluation value of the machine room is obtained, the value evaluation value of the machine room is obtained according to the actual use condition of the machine room, and the accuracy of the value evaluation of the machine room is improved.
It should be understood that, in the embodiment of the present invention, the radio frequency unit 401 may be used for receiving and sending signals during a message sending and receiving process or a call process, and specifically, receives downlink data from a base station and then processes the received downlink data to the processor 410; in addition, the uplink data is transmitted to the base station. Typically, radio unit 401 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. Further, the radio unit 401 can also communicate with a network and other devices through a wireless communication system.
The electronic device provides wireless broadband internet access to the user via the network module 402, such as assisting the user in sending and receiving e-mails, browsing web pages, and accessing streaming media.
The audio output unit 403 may convert audio data received by the radio frequency unit 401 or the network module 402 or stored in the memory 409 into an audio signal and output as sound. Also, the audio output unit 403 may also provide audio output related to a specific function performed by the electronic apparatus 400 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 403 includes a speaker, a buzzer, a receiver, and the like.
The input unit 404 is used to receive audio or video signals. The input Unit 404 may include a Graphics Processing Unit (GPU) 4041 and a microphone 4042, and the Graphics processor 4041 processes image data of a still picture or video obtained by an image capturing apparatus (such as a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 406. The image frames processed by the graphic processor 4041 may be stored in the memory 409 (or other storage medium) or transmitted via the radio frequency unit 401 or the network module 402. The microphone 4042 may receive sound, and may be capable of processing such sound into audio data. The processed audio data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 401 in case of the phone call mode.
The electronic device 400 also includes at least one sensor 405, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor includes an ambient light sensor that adjusts the brightness of the display panel 4061 according to the brightness of ambient light, and a proximity sensor that turns off the display panel 4061 and/or the backlight when the electronic apparatus 400 is moved to the ear. As one type of motion sensor, an accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), detect the magnitude and direction of gravity when stationary, and can be used to identify the posture of an electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), and vibration identification related functions (such as pedometer, tapping); the sensors 405 may also include a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, etc., which will not be described in detail herein.
The display unit 406 is used to display information input by the user or information provided to the user. The Display unit 406 may include a Display panel 4061, and the Display panel 4061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 407 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device. Specifically, the user input unit 407 includes a touch panel 4041 and other input devices 4072. Touch panel 4041, also referred to as a touch screen, may collect touch operations by a user on or near it (e.g., operations by a user on or near touch panel 4041 using a finger, a stylus, or any suitable object or attachment). The touch panel 4041 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 410, receives a command from the processor 410, and executes the command. In addition, the touch panel 4041 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 4041, the user input unit 407 may include other input devices 4072. Specifically, the other input devices 4072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a track ball, a mouse, and a joystick, which are not described herein again.
Further, the touch panel 4041 can be overlaid on the display panel 4061, and when the touch panel 4041 detects a touch operation thereon or nearby, the touch operation can be transmitted to the processor 410 to determine the type of the touch event, and then the processor 410 can provide a corresponding visual output on the display panel 4061 according to the type of the touch event. Although in fig. 4, the touch panel 4041 and the display panel 4061 are two independent components to implement the input and output functions of the electronic device, in some embodiments, the touch panel 4041 and the display panel 4061 may be integrated to implement the input and output functions of the electronic device, and the implementation is not limited herein.
The interface unit 408 is an interface for connecting an external device to the electronic apparatus 400. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 408 may be used to receive input (e.g., data information, power, etc.) from an external device and transmit the received input to one or more elements within the electronic apparatus 400 or may be used to transmit data between the electronic apparatus 400 and an external device.
The memory 409 may be used to store software programs as well as various data. The memory 409 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 409 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The processor 410 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, performs various functions of the electronic device and processes data by operating or executing software programs and/or modules stored in the memory 409 and calling data stored in the memory 409, thereby performing overall monitoring of the electronic device. Processor 410 may include one or more processing units; preferably, the processor 410 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 410.
The electronic device 400 may further include a power supply 411 (e.g., a battery) for supplying power to various components, and preferably, the power supply 411 may be logically connected to the processor 410 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system.
In addition, the electronic device 400 includes some functional modules that are not shown, and are not described in detail herein.
Preferably, an embodiment of the present invention further provides an electronic device, which includes a processor 410, a memory 409, and a computer program that is stored in the memory 409 and can be run on the processor 410, and when being executed by the processor 410, the computer program implements each process of the above-mentioned computer room evaluation method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not described here again.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A machine room evaluation method, comprising:
collecting multiple items of basic data of a machine room, and determining the current reference value of each item of basic data of the machine room according to the collected multiple items of basic data;
obtaining the current reference value of each value influence factor of the machine room according to the reference value of each item of the basic data and the weight of each value influence factor of each item of the basic data, wherein the value influence factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors;
obtaining the type of the machine room, and obtaining the current reference value of the machine room according to the reference value of each value influence factor and the weight of each value influence factor to the type of the machine room, wherein the type of the machine room comprises: a convergence machine room, an access network machine room, or a resource point;
and obtaining the evaluation value of the machine room according to the current reference value of the machine room and the current weight of the machine room.
2. The method of claim 1, wherein the plurality of items of base data comprise at least one of: the data processing system comprises data management data, contract data, account statement data, network element work parameters, telephone traffic, traffic data, contract data and white list data.
3. The method of claim 1,
before obtaining the reference value of each current value influence factor of the machine room, the method further includes: for each value influence factor, constructing a first judgment matrix of the plurality of items of basic data on the value influence factor according to a preset judgment matrix construction rule, and obtaining the weight of each item of basic data on the value influence factor according to the first judgment matrix;
before obtaining the current reference value of the machine room, the method further comprises: and according to the judgment matrix construction rule, constructing a second judgment matrix of each value influence factor on the type of the machine room, and according to the second judgment matrix, obtaining the weight of each basic data on the value influence factor.
4. The method of claim 3, wherein the decision matrix construction rule comprises:
for decision matrix A: a ═ aij)n×n，aij>0，aij×ajiWhen i is j, a is 1ij＝aji1, wherein aijIndicating the relative importance of the ith element and the jth element to the upper layer factor, and n is the number of elements participating in the reference value of the upper layer factor.
5. The method of claim 3,
before obtaining the weight of each item of the basic data on the price influence factor according to the first judgment matrix, the method further includes: according to the maximum characteristic root of the first judgment matrix, obtaining a consistency index of the first judgment matrix, and if the consistency index of the first judgment matrix is larger than a preset correction value, adjusting the value of an element in the first judgment matrix until the consistency index of the first judgment matrix is larger than the preset correction value;
before obtaining the weight of each item of the basic data on the price influence factor according to the second judgment matrix, the method further includes: and acquiring a consistency index of the second judgment matrix according to the maximum characteristic root of the second judgment matrix, and if the consistency index of the second judgment matrix is larger than a preset correction value, adjusting the values of elements in the second judgment matrix until the consistency index of the second judgment matrix is larger than the preset correction value.
6. The method according to any of the claims 1 to 5, characterized in that after obtaining the evaluation value of the machine room, the method further comprises:
and if the evaluation value of the machine room is lower than a preset value, obtaining and outputting an optimization scheme of the machine room according to a preset optimization strategy and by combining various basic data of the machine room.
7. A machine room evaluation apparatus, comprising:
the acquisition module is used for acquiring multiple items of basic data of the machine room;
the determining module is used for determining the reference value of each item of current basic data of the machine room according to the acquired multiple items of basic data;
a first obtaining module, configured to obtain a reference value of each current value influencing factor of the machine room according to a reference value of each item of the basic data and a weight of each value influencing factor of each item of the basic data, where the value influencing factors include: revenue contribution factors, cost occupancy factors and machine room attribute factors;
a second obtaining module, configured to obtain a type to which the machine room belongs, and obtain a current reference value of the machine room according to the reference value of each value influencing factor and a weight of each value influencing factor on the type to which the machine room belongs, where the type to which the machine room belongs includes: a convergence machine room, an access network machine room, or a resource point;
and the third acquisition module is used for obtaining the evaluation value of the machine room according to the current reference value of the machine room and the weight of the machine room.
8. The apparatus of claim 7, further comprising: a first building block and a second building block, wherein,
the first construction module is used for constructing a first judgment matrix of the plurality of items of basic data on each value influence factor according to a preset judgment matrix construction rule;
the first obtaining module is further configured to obtain, according to the first determination matrix, weights of the basic data on the price influencing factors;
the second construction module is used for constructing a second judgment matrix of each value influence factor on the type of the machine room according to the judgment matrix construction rule;
the second obtaining module is further configured to obtain, according to the second determination matrix, weights of the basic data on the price influencing factors.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 6.
10. A computer-readable storage medium, comprising: the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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