CN113487148B - Method and device for obtaining network evaluation result - Google Patents
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Abstract
The embodiment of the application provides a method and a device for acquiring a network evaluation result, which relate to the field of communication and are used for carrying out real and objective evaluation on user experience in co-construction sharing implementation. The method comprises the following steps: acquiring a historical evaluation record of each user, wherein the historical evaluation record comprises evaluation data of one or more service parameters of a first network; acquiring the current evaluation weight of each user; the current evaluation weight of a user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the same comments made by the user to the total number of the comments of the user; respectively acquiring an evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; the evaluation result of a service parameter is the weighted sum of the evaluation data of the service parameter by each user; and taking the weighted sum of the evaluation results of each service parameter as the evaluation result of the first network. The method and the device are used for acquiring experience evaluation of the user on the network.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a method and an apparatus for obtaining a network evaluation result.
Background
With the rapid development of mobile communication networks, the available operating frequency band, technical difficulty and power consumption of mobile communication networks are also improved. This means that the coverage radius of the fifth generation mobile communication technology (5th generation mobile networks,5G), the sixth generation mobile communication technology (6th generation mobile networks,6G) and future mobile communication network cells will be reduced, resulting in an increase in mobile communication network construction costs. And the multi-operator co-builds a shared mobile communication network (hereinafter referred to as a shared network) so as to reduce the network building cost and improve the utilization efficiency of spectrum resources. Therefore, the shared network will become the main stream direction for building the mobile communication network in the future.
In the scenario of a shared network, the sharing party uses the network of the contractor and pays the contractor, and how to reconcile and charge for specific payment also needs to be based on the user experience of the shared network.
The objective user evaluation experience (such as false evaluation, malicious evaluation, false evaluation and the like) is not achieved, so that account checking and charging are inaccurate when co-building sharing is implemented, and loss of a contractor or a sharing party is caused. Therefore, how to make a true objective assessment of the user experience is particularly important in co-building sharing implementations.
Disclosure of Invention
The embodiment of the application provides a method and a device for acquiring a network evaluation result, which solve the technical problem of how to evaluate user experience truly and objectively in co-building sharing implementation.
In a first aspect, a method for obtaining a network evaluation result is provided, including: acquiring a historical evaluation record of each user evaluating the first network, wherein the historical evaluation record comprises evaluation data of one or more service parameters of the first network; acquiring the current evaluation weight of each user; the current evaluation weight of one user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the total number of user comments occupied by the same comments of the user; respectively acquiring an evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; wherein, the evaluation result of a business parameter is the weighted sum of the evaluation data of each user to the business parameter; and taking the weighted sum of the evaluation results of each service parameter as the evaluation result of the first network.
By the scheme of the application, a low evaluation weight is configured for the user, the proportion of the evaluation of the user when the final evaluation is acquired is reduced, and the proportion of the inaccurate evaluation in the final evaluation is reduced. Thus, the influence of the evaluation record of the user on the network evaluation result can be reduced. In practical application, the influence of non-objective comments on the final result can be reduced, and the accuracy of the network evaluation result is ensured.
In one possible design, for a first user, the first user is any user evaluating the first network, and the method includes the steps of: acquiring a characteristic value of a first user; determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user; the current level is inversely proportional to the eigenvalue; determining the current evaluation weight of the first user according to the current level of the first user; the current evaluation weight is proportional to the current level. The method comprises the steps of determining the current level of the user in inverse proportion to the characteristic value according to the characteristic value of the user, and determining the evaluation weight of the user in direct proportion to the level according to the current level of the user, so that the inverse proportion of the characteristic value and the current evaluation weight is realized.
In one possible design, the user's characteristic values include: the number of user reviews is proportional to the total number of user reviews. The bad evaluation proportion is used as the characteristic value, so that the influence of malicious bad evaluation on the evaluation result can be reduced.
In one possible design, determining the current level of the first user based on the previous level of the first user and the characteristic value of the first user includes: the current level L of the first user, the previous level L of the first user and the characteristic value Rpi of the first user satisfy the following relationship:
Wherein Ls is a level step length, L1 is a first level threshold, and R is a feature threshold.
In one possible design, determining the current rating weight of the first user based on the current level of the first user includes: the current evaluation weight W of the first user and the current level L of the first user meet the following relation:
wherein M is a weight step length, ls is a level step length, L2 is a second level threshold, M is a positive integer greater than or equal to 0 and less than or equal to mmax, mmax is a weight upper limit minus b, and a and b are constants.
Possible designsAccording to the historical evaluation record and the current evaluation weight of each user, respectively obtaining the evaluation result of each service parameter of the first network, wherein the evaluation result comprises the following steps: the evaluation result Sij of the ith user on the jth service parameter and the evaluation result Sj of the jth service parameter satisfy the following relationship:
wherein N is the total number of users evaluating the first network, wi' is the current evaluation weight of the ith user; alternatively, wi 'is the normalized evaluation weight of the ith user, wi' satisfies the following relationship:
in one possible design, taking a weighted sum of the evaluation results of each service parameter as the evaluation result of the first network includes: the evaluation result S of the first network satisfies the following relationship: 
Wherein X is the total number of service parameters of the first network, sj is the evaluation result of the j-th service parameter, and Wj' is the preset weight of the j-th service parameter; or, wj 'is the normalized evaluation weight of the j-th service parameter, and the Wj' satisfies the following relationship: />
In one possible design, the method described in the embodiments of the present application is performed on a blockchain in the form of a smart contract, where the contents of a blockvolume record of the blockchain includes: user history evaluation record, user level and evaluation result of network.
In a second aspect, an apparatus for obtaining a network evaluation result is provided, including: the device comprises a first acquisition unit, a second acquisition unit, a third acquisition unit and a calculation unit. Wherein:
a first acquisition unit configured to acquire a history evaluation record of each user, the history evaluation record including evaluation data of one or more service parameters of the first network.
The second acquisition unit is used for acquiring the current evaluation weight of each user; the current evaluation weight of one user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the total number of user comments occupied by the same comments made by the user.
The third acquisition unit is used for respectively acquiring the evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; the evaluation result of a service parameter is a weighted sum of the evaluation data of the service parameter for each user.
And the calculation unit is used for taking the weighted sum of the evaluation results of each service parameter as the evaluation result of the first network.
In the embodiment of the application, the comment of the user with a high characteristic value is biased to a certain aspect, so that the comment of the user is not worry or falsification or malicious evaluation, which is equivalent to inaccurate evaluation of the user. Thus, the influence of the evaluation record of the user on the network most evaluation result can be reduced. In practical application, the influence of non-objective comments on the final result can be reduced, and the accuracy of the network evaluation result is ensured.
It should be noted that, the apparatus for obtaining a network evaluation result provided in the second aspect is configured to implement a method for obtaining a network evaluation result described in the first aspect or any one of the possible designs in the first aspect, and the specific implementation of the method for obtaining a network evaluation result may refer to the specific implementation of the method for obtaining a network evaluation result described in the first aspect or any one of the possible designs in the first aspect.
In a third aspect, an apparatus for obtaining a network evaluation result is provided, including: comprising the following steps: one or more processors, and memory; the memory is coupled with the one or more processors; the memory is for storing computer program code comprising instructions which, when executed by the one or more processors, cause the apparatus to perform any of the methods as provided in the first aspect above.
In a fourth aspect, there is provided a computer readable storage medium comprising computer instructions which, when run on a computer, cause the computer to perform any of the methods provided in the first aspect.
In a fifth aspect, there is provided a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform any of the methods provided in the first aspect.
Drawings
Fig. 1 is a schematic diagram of a device architecture for obtaining a network evaluation result according to an embodiment of the present application;
fig. 2 is a flowchart of a method for obtaining a network evaluation result according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for determining an evaluation weight of a user according to an embodiment of the present application;
fig. 4 is a schematic diagram of a device for obtaining a network evaluation result according to an embodiment of the present application;
fig. 5 is a schematic diagram of another device for obtaining a network evaluation result according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings are used for distinguishing between different objects or between different processes of the same object and not for describing a particular order of objects.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present invention are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that in the description of embodiments of the present invention, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in an embodiment should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" means two or more.
First, some terms or basic concepts of the technology related to the embodiments of the present invention will be briefly described and explained.
Blockchain: blockchains are a distributed database that originates from bitcoin and is the underlying technology of bitcoin. The blockchain is a series of data blocks which are generated by the association of a cryptography method, and each data block contains information of a bit coin network transaction and is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. In a narrow sense, a blockchain is a distributed ledger that combines blocks of data in a sequential manner in time order into a chain data structure, and that is cryptographically secured against tampering and counterfeiting. In broad terms, blockchain technology is a completely new distributed infrastructure and computing paradigm that utilizes blockchain data structures to validate and store data, distributed node consensus algorithms to generate and update data, cryptography to secure data transfer and access, and intelligent contracts composed of automated script code to program and manipulate data. Blockchains are mainly used to solve trust and security problems of transactions.
Intelligent contract: intelligent contracts, also known as intelligent contracts, are event driven, stateful, multi-party acknowledged programs that run on blockchains and are capable of automatically processing assets according to preset conditions, with the biggest advantage of using program algorithms instead of human arbitration and execution of contracts.
The method provided in the embodiments of the present application may be used in any standard communication system, which may be a third generation partnership project (3rd generation partnership project,3GPP) communication system, for example, a long term evolution (long term evolution, LTE) system, or a 5G mobile communication system or a New Radio (NR) system, or a non-3 GPP communication system, without limitation.
Fig. 1 shows a simplified schematic diagram of a communication system architecture to which embodiments of the invention may be applied. As shown in fig. 1, the communication system may include: a (radio) access network (R) AN device, a terminal, a core network, a Data Network (DN), etc.
In the communication system shown in fig. 1, the (R) AN access network device is configured to implement radio access related functions to provide AN access network to its coverage area. The terminal can access the core network through AN access network provided by (R) AN access network equipment, thereby accessing DN and completing interaction of service data.
As illustrated in fig. 1, the first, second and third areas are coverage areas of the (R) AN device, respectively, to provide access services for users in the areas thereof. The core network may include access and mobility management functions (access and mobility management function, AMF), session management functions (session management function, SMF), UPF. The device according to the present application will be described with reference to fig. 1.
For example, the (R) AN device may be a base station, a broadband network traffic gateway (broadband network gateway, BNG), a convergence switch, a non-3 GPP access device, or the like. The base station may include various forms of base stations, such as: macro base stations, micro base stations (also referred to as small stations), relay stations, access points, and the like. The embodiment of the present application is not particularly limited thereto. For example, the access network device may be an evolved universal terrestrial radio access network (evolved universal terrestrial radio access network, E-UTRAN) device in a fourth generation mobile communication technology (4th generation,4G) network, a next generation radio access network (next generation radio access network, NG-RAN) device in a 5G network, an evolved Node B (eNodeB), a WIFI access Node (AP), a worldwide interoperability for microwave access (world interoperability for microwave access, WIMAX) Base Station (BS), and so forth.
A terminal (terminal), which may be referred to as a User Equipment (UE) or a terminal device (terminal equipment). It is apparent that the terminal of each area shown in fig. 1 may include, but is not limited to, a vehicle-mounted terminal, a mobile phone (mobile phone), a tablet computer or a computer with a wireless transceiving function, an intelligent gas station, an intelligent signal lamp, etc.
Each operator may individually set up a dedicated (R) AN device for providing AN access network of the operator to a coverage area of the (R) AN device, and further providing network services of the operator to users in the coverage area.
In the co-building sharing scenario, one area may be established by one operator for the (R) AN device, and AN access network is provided to a coverage area of the (R) AN device, where the access network is shared with other operators, so that multiple operators in one area may share the access network.
For example, a certain (R) AN device in fig. 1 is set up by AN operator a, and the operator a may share AN access network provided by the (R) AN device with a sharing party such as AN operator B, AN operator C, and the like, and charge the sharing party for a certain fee, where a specific fee needs to refer to AN evaluation result of the shared access network by users of the operator B and the operator C.
The embodiment of the invention provides a method for acquiring a network evaluation result, and the method provided by the application is described in detail through a specific embodiment.
On one hand, the embodiment of the invention provides a method for obtaining the network evaluation result, which can be used for evaluating the user experience of the network truly and objectively, and the specific application scene is not limited. For example, the scheme provided by the application can be used for obtaining real and objective evaluation on the user experience of the network in the co-building sharing implementation.
It should be noted that, for any system network in any area, the process of acquiring the evaluation result is the same, and the following embodiments of the present application take the process of acquiring the evaluation result of the first network (any network) as an example for explanation, and the other steps are not repeated. For example, the first network may be any co-established shared access network.
As shown in fig. 2, the method for obtaining a network evaluation result provided in the present application may include the following steps:
s201, the network equipment acquires a history evaluation record of each user.
The user described in the application may refer to a user evaluating the first network, which will not be described in detail later.
Wherein the historical evaluation record includes evaluation data for one or more traffic parameters of the first network,
optionally, the service parameters may include: voice quality, web browsing quality, wireless signal strength, video service quality, etc. The present application does not limit the type of specific service parameters.
In one possible implementation, the rating data may be a rating of the business parameter by the user. For example, the score is fully divided into 10 points. For example, the voice quality is evaluated as 5 points, and the web browsing quality is evaluated as 8 points.
In another possible implementation, the evaluation data may also be an evaluation level of the service parameter. For example, the rating may be five ratings of poor, general, good, and very good. For example, the user has a good speech quality rating and a general rating of web browsing quality.
Alternatively, the historical evaluation record of the user may be a user evaluation record within a preset time period. The preset time period may be in units of years, months, weeks, days, hours, etc. The present application is not limited in this regard.
For example, if the preset time period is 6 months and the current time is 1 month of 2020, the historical evaluation record of the user refers to the user evaluation record from 1 month of 2020 to 1 month of 2020.
Alternatively, the historical evaluation record of the user may be a preset number of user evaluation records.
For example, if the preset number of times is 10, the user's historical evaluation record refers to the last 10 evaluation records of the user.
It should be noted that, the historical evaluation record of the user may be obtained from the network or may be obtained from the operator database. When the historical evaluation record of the user is obtained from the network, the crawler technology can be adopted to gradually crawl the historical evaluation record of the user, and the application is not limited to the method.
S202, the network equipment acquires the current evaluation weight of each user.
The current evaluation weight of a user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the same comments made by the user to the total number of the comments of the user.
The evaluation weight of the user refers to the importance degree of the evaluation of the service parameter of the first network with respect to the evaluation result of the service parameter, which is different from the general specific gravity, and is represented by not only the percentage of the evaluation of the service parameter of the first network by a certain user, but also the relative importance degree of the evaluation of the service parameter of the first network by the user.
In one possible implementation manner, when the characteristic value of the user is obtained, the evaluation record of the user on other networks except the first network can be referred to, so that inaccurate evaluation weight configuration to the user without the history evaluation record as a basis when the user accesses the first network for the first time can be avoided.
For example, the user's characteristic value may be the same comment made by the user on the first network, as a proportion of the total number of comments made by the user on the first network.
For example, the feature value of the user may be the same comment made by the user in the past, and may be a proportion of the total number of the past comments of the user. The previous comments may include comments made by the user to other networks than the first network.
In the embodiment of the application, the higher the characteristic value of the user is, the worse the evaluation referential of the user is, and the real experience of the user on the network cannot be reflected. The lower the characteristic value of the user is, the better the evaluation referential of the user is, and the real experience of the user on the network can be reflected.
Alternatively, the characteristic value of the user may be a proportion of the number of critique times of the user to the total number of critique times of the user. For example, in order to reduce the fee for paying the shared network to the contractor, when evaluating the access network provided by the contractor, the user scheduled by the shared party a performs bad evaluation on the network, and the bad evaluation ratio of the scheduled users is higher, so that the feature values of the users are higher.
Alternatively, the characteristic value of the user may be a proportion of the number of times the user likes to the total number of comments of the user. For example, in order to increase the use fee of the shared network charged to the sharing party, when evaluating the access network provided by the party, the user scheduled by the party a gives a good score to the network, and the good score of the scheduled user is high, so that the feature value of these users is high.
In one possible implementation manner, the network device in S202 first obtains the feature value of the user, and then determines the current evaluation weight of the user according to the feature value of the user.
Specifically, the obtaining of the current evaluation weight of a user may be implemented as: firstly, acquiring a characteristic value of a user; then determining the current level of the user according to the previous level of the user and the characteristic value of the user; and finally, determining the current evaluation weight of the user according to the current level of the user.
It should be noted that, the specific implementation of obtaining the current evaluation weight of the user may be detailed in the schemes described in the following steps S301 to S303, which are not described herein again.
And S203, the network equipment respectively acquires the evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight.
Wherein, the evaluation result of one service parameter is the weighted sum of the evaluation data of each user on the service parameter.
Optionally, the evaluation result Sij of the ith user on the jth service parameter and the evaluation result Sj of the jth service parameter may satisfy the following relationship:
wherein N is the total number of users evaluating the first network, wi' may be the current evaluation weight of the i-th user; alternatively, wi 'may be the normalized evaluation weight of the ith user, wi' may satisfy the following relationship:
in a possible implementation manner, the evaluation data of the service parameters by the user is in a numerical form, and in S203, the evaluation result of each service parameter of the first network may be obtained through calculation.
For example, if there are three users' evaluation data of voice quality service parameters, the score of user a is 10 points, and the normalized evaluation weight is 0.5; the score of the user B is 5, and the normalized evaluation weight is 0.2; the score of the user C is 8, the normalized evaluation weight is 0.3, and the evaluation result of the voice quality service parameter is: sj=10×0.5+5×0.2+8×0.3=8.4 minutes.
In another possible implementation manner, the evaluation data of the service parameters by the user is in a non-numeric form (such as the evaluation level in step 201), and the evaluation result of each service parameter of the first network is obtained through calculation after the non-numeric form of the evaluation data is converted into the numeric form. The non-numerical evaluation data are converted into numerical values, different numerical values can be corresponding to different evaluation data, and then conversion is carried out according to the corresponding relation.
For example, the correspondence of the non-numerical form of the evaluation data to the numerical value may be as shown in table 1.
TABLE 1
| Evaluation grade | Scoring of |
| Difference of difference | 2 |
| Poor quality | 4 |
| In general | 6 |
| Good quality | 8 |
| Very good | 10 |
It should be noted that, table 1 is only referred to, and the grade corresponding score may be configured according to the actual situation.
S204, the network equipment takes the weighted sum of the evaluation results of each service parameter as the evaluation result of the first network.
Wherein, the evaluation result S of the first network satisfies the following relationship:
wherein X is the total number of service parameters of the first network, sj is the evaluation result of the j-th service parameter, and Wj' is the preset weight of the j-th service parameter; or, wj 'is the normalized evaluation weight of the j-th service parameter, and the Wj' satisfies the following relationship:
for example, if the first network has three service parameters, they are respectively: voice quality, web browsing quality, and wireless signal strength. Wherein, the user score of the voice quality is 8 points, and the normalized evaluation weight is 0.5; the user score of the web page browsing quality is 6 points, and the normalized evaluation weight is 0.3; the user score of the wireless signal strength is 7 points, the normalized evaluation weight is 0.2, and the evaluation result of the user on the first network is as follows: s=8×0.5+6×0.3+7×0.2=7.2 minutes.
Optionally, the scheme provided in the application may be periodically executed to obtain the network evaluation result. Wherein the periodic time interval may be 3 months or 1 month. In practical application, the periodic time interval may be selected according to practical situations, which is not limited in this application.
According to the scheme, the comment of the user with high characteristic value is biased to a certain aspect, so that the comment of the user is not careless, fake or malicious, and the comment is equivalent to inaccurate evaluation of the user. Thus, the influence of the evaluation record of the user on the network most evaluation result can be reduced. In practical application, the influence of non-objective comments on the network evaluation result can be reduced, and the accuracy of the network evaluation result is ensured.
In another aspect, the method of the above aspect may be performed on a blockchain in the form of a smart contract, wherein the content recorded on the blockwork includes a user history rating record, a user level, and a rating result of the network.
Therefore, the decentralization, transparency and difficult tampering of the blockchain technology are utilized, the tampering of information such as user evaluation and the like is avoided, and the trusted storage of user experience evaluation is realized.
In yet another aspect, the embodiment of the invention provides a method for determining an evaluation weight of a user.
It should be noted that, for different users, the process of determining the evaluation weights thereof is the same, and the following embodiments of the present application take the current evaluation weight of the first user (any user evaluating the first network) as an example for description, and the other steps are not repeated.
As shown in fig. 3, the method for determining the evaluation weight of the user provided by the application may include the following steps:
s301, acquiring a characteristic value of a first user.
When the characteristic value of the user is obtained, the historical evaluation record of the user to the first network can be obtained, and the evaluation record of the user to other networks except the first network can be referred to.
S302, determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user.
Wherein the current level of the first user is inversely proportional to the characteristic value of the first user.
Optionally, the current level L of the first user, the previous level L of the first user and the characteristic value Rpi of the first user may satisfy the following relationship:
wherein Ls is a level step length, L1 is a first level threshold, and R is a feature threshold. Wherein, the characteristic threshold R satisfies the following relationship:
wherein np is the number of first comments of the user evaluating the first network, and the first comments are comments pointed by the feature value; p is the number of evaluations of the first network by the user, and alpha is the correction coefficient.
Wherein, R is a preset characteristic value limit for defining whether the comment is objective or not, and a user with a characteristic value larger than R can consider that the comment is not objective; users with characteristic values smaller than R can consider that the comments are objective; users with a feature value equal to R may consider their comments objective or not. Of course, the specific value of R may be configured according to actual requirements, which is not limited in the embodiment of the present application.
Wherein, L1 may be a preset highest level configured to users with non-objective comments, so as to configure low evaluation weights to users with non-objective comments. In practical application, the value of L1 may be configured according to practical requirements, which is not limited in the embodiments of the present application.
It should be noted that Ls and α may be empirical values or theoretical values, and specific values may be configured according to actual requirements, which is not limited in the embodiment of the present application.
For example, assume that L1 is 3. For example, if the previous level L of a certain user is 2, the feature value of the user is greater than the feature threshold, and the previous level of the user is less than L1, the current level of the user is 2. For example, the previous level of another user is level 5, and the feature value is greater than the feature threshold, then the current level of that user is level 3.
Optionally, the initial level of the user is level 0, and the user reports a new rating record to trigger a new rating to the user.
S303, determining the current evaluation weight of the first user according to the current level of the first user.
Wherein the current rating weight of a user is proportional to the current level of the user.
Optionally, the current evaluation weight W of the first user and the current level L of the first user satisfy the following relationship:
wherein M is a weight step length, ls is a level step length, L2 is a second level threshold, M is a positive integer greater than or equal to 0 and less than or equal to mmax, mmax is a weight upper limit minus b, and a and b are constants.
Wherein, L2 can be preset, and whether the highest level of the evaluation data of the user is considered when the comment result is obtained is determined, so that the influence of the user with non-objective comment on the final evaluation result is small or even not. In practical application, the value of L2 may be configured according to practical requirements, which is not limited in the embodiments of the present application.
Alternatively, L2 and L1 may be equal or different, which is not limited in this application.
In addition, mmax depends on the highest level of the user, and a and b are preset values, which are used for increasing the weight of the user, and can be matched with the values according to actual requirements, and the application is not limited herein.
Illustratively, a may be 1 and b may be 2.
In the relation of obtaining the current evaluation weight W of the user, according to the specific size of L, traversing and obtaining M values which enable inequality M multiplied by M multiplied by L2 < L multiplied by L2+ (m+1) multiplied by M multiplied by Ls to be established between 0 and mmax, and if the M values can be obtained, determining the current evaluation weight of the first user as m+a.
If the current level of the user is 5, if M takes 5 such that m×m×ls+l2 < L < l2+ (m+1) ×m×ls holds, it is determined that the current evaluation weight of the user is 5+a.
According to the scheme provided by the application, the level of the user is determined according to the characteristic value of the user, and then the current evaluation weight of the user is determined according to the level of the user. Therefore, the weight of the user is ensured to correspond to the evaluation accuracy of the user, so that the influence of non-objective comments on the final result is reduced, and the accuracy of the network evaluation result is ensured.
The above description has been presented mainly in terms of interaction between the nodes. It is understood that each node, e.g., device, includes corresponding hardware structures and/or software modules for performing each function in order to achieve the functions described above. Those of skill in the art will readily appreciate that the various illustrative algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The present application may divide the functional modules of the apparatus according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that the division of the modules in this application is illustrative, and is merely a logic function division, and other division manners may be implemented in practice.
Fig. 4 shows a schematic diagram of a device for obtaining a network evaluation result according to an embodiment of the present application. As shown in fig. 4, the apparatus 40 includes: a first acquisition unit 401, a second acquisition unit 402, a third acquisition unit 403, and a calculation unit 404.
Wherein the first obtaining unit 401 is configured to obtain a history evaluation record of each user, where the history evaluation record includes evaluation data of one or more service parameters of the first network. For example, the first acquisition unit 401 may be used to perform the process S201 illustrated in fig. 2.
A second obtaining unit 402, configured to obtain a current evaluation weight of each user; the current evaluation weight of one user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the total number of user comments occupied by the same comments made by the user. For example, the second acquisition unit 402 may be used to perform any of the processes S202 illustrated in fig. 2 or the processes S301-S303 illustrated in fig. 3.
A third obtaining unit 403, configured to obtain an evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; the evaluation result of a service parameter is a weighted sum of the evaluation data of the service parameter for each user. For example, the third acquisition unit 403 may be used to perform the process S203 illustrated in fig. 2.
A calculating unit 404, configured to take a weighted sum of the evaluation results of each service parameter as the evaluation result of the first network. For example, the computing unit 404 may be used to perform the process S204 illustrated in fig. 2.
Optionally, for the first user, the second obtaining unit 402 is specifically configured to: and acquiring the characteristic value of the first user. Determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user; the current level is inversely proportional to the eigenvalue. Determining the current evaluation weight of the first user according to the current level of the first user; the current evaluation weight is proportional to the current level.
Optionally, the characteristic values of the user include: the number of user reviews is proportional to the total number of user reviews.
Optionally, the current level L of the first user, the previous level L of the first user and the characteristic value Rpi of the first user satisfy the following relationship:
wherein Ls is a level step length, L1 is a level threshold, and R is a feature threshold.
Optionally, the current evaluation weight W of the first user and the current level L of the first user satisfy the following relationship:
wherein M is a weight step length, ls is a level step length, L2 is a second level threshold, M is a positive integer greater than or equal to 0 and less than or equal to mmax, mmax is a weight upper limit minus b, and a and b are constants. Optionally, the evaluation result Sij of the ith user on the jth service parameter and the evaluation result Sj of the jth service parameter satisfy the following relationship:
Wherein N is the total number of users evaluating the first network, wi' is the current evaluation weight of the ith user; alternatively, wi 'is the normalized evaluation weight of the ith user, wi' satisfies the following relationship:
optionally, the evaluation result S of the first network satisfies the following relationship:
wherein X is the total number of service parameters of the first network, sj is the evaluation result of the j-th service parameter, and Wj' is the preset weight of the j-th service parameter; or, wj 'is the normalized evaluation weight of the j-th service parameter, and the Wj' satisfies the following relationship:
optionally, the apparatus 40 is performed on a blockchain in the form of a smart contract, and the contents of the blockvolume record of the blockchain include: user history evaluation record, user level and evaluation result of network.
The units in fig. 4 may also be referred to as modules, e.g., the processing units may be referred to as processing modules. In addition, in the embodiment shown in fig. 4, the names of the respective units may be other than those shown in the drawing, and for example, the acquisition unit may also be referred to as an input unit.
The embodiment of the application further provides a hardware structure schematic diagram of a device for obtaining a network evaluation result, as shown in fig. 5, the device 50 includes a processor 501, and optionally, the device 50 further includes a memory 502 and a transceiver 503 connected to the processor 501. The processor 501, memory 502 and transceiver 503 are connected by a bus 504.
The processor 501 may be a central processing unit (central processing unit, CPU), a general purpose processor network processor (network processor, NP), a digital signal processor (digital signal processing, DSP), a microprocessor, a microcontroller, a programmable logic device (programmable logic device, PLD), or any combination thereof. The processor may also be any other means for performing a processing function, such as a circuit, device, or software module. The processor 501 may also include multiple CPUs, and the processor 501 may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, or processing cores for processing data (e.g., computer program instructions).
The memory 502 may be a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that may store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, as the embodiments of the present application are not limited in this regard. The memory 502 may be separate or integrated with the processor 501. Wherein the memory 502 may contain computer program code. The processor 501 is configured to execute computer program code stored in the memory 502, thereby implementing the methods provided in the embodiments of the present application.
The transceiver 503 may be used to communicate with other devices or communication networks (e.g., ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.). The communication interface may be a module, a circuit, a transceiver, or any device capable of enabling communication.
The individual units in fig. 4 may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. The storage medium storing the computer software product includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer-executable instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, from one website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Embodiments of the present application also provide a computer-readable storage medium comprising computer-executable instructions that, when run on a computer, cause the computer to perform any of the methods described above.
Embodiments of the present application also provide a computer program product comprising computer-executable instructions which, when run on a computer, cause the computer to perform any of the methods described above.
The embodiment of the application also provides a chip, which comprises: a processor and an interface through which the processor is coupled to the memory, which when executed by the processor executes a computer program or computer-executable instructions in the memory, cause any of the methods provided by the embodiments described above to be performed.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer-executable instructions. When the computer-executable instructions are loaded and executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer-executable instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, from one website, computer, server, or data center by wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Although the present application has been described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed application, from a review of the figures, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
Although the present application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (12)
1. A method for obtaining a network evaluation result, comprising:
acquiring a historical evaluation record of each user, wherein the historical evaluation record comprises evaluation data of one or more service parameters of a first network;
acquiring the current evaluation weight of each user; the current evaluation weight of a user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the same comments made by the user to the total number of the comments of the user;
respectively acquiring an evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; the evaluation result of a service parameter is the weighted sum of the evaluation data of the service parameter by each user;
taking the weighted sum of the evaluation results of each service parameter as the evaluation result of the first network;
aiming at a first user, the first user is any user evaluating the first network, the current evaluation weight of the first user is obtained, and the method comprises the following steps:
acquiring a characteristic value of the first user;
determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user; the current level is inversely proportional to the eigenvalue;
Determining the current evaluation weight of the first user according to the current level of the first user; the current evaluation weight is proportional to the current level;
the determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user comprises the following steps:
the current level L of the first user, the previous level L of the first user and the characteristic value Rpi of the first user satisfy the following relationship:
wherein Ls is a level step length, L1 is a first level threshold, and R is a feature threshold;
the determining the current evaluation weight of the first user according to the current level of the first user comprises the following steps:
the current evaluation weight W of the first user and the current level L of the first user satisfy the following relationship:
the M is a weight step length, the Ls is a level step length, the L2 is a second level threshold, the M is a positive integer which is more than or equal to 0 and less than or equal to mmax, the mmax is a weight upper limit minus the b, and the a and the b are constants.
2. The method of claim 1, wherein the user's characteristic values comprise: and the number of the user comments is proportional to the total number of the user comments.
3. The method according to claim 1, wherein the step of obtaining the evaluation result of each service parameter of the first network according to the historical evaluation record of each user and the respective current evaluation weight includes:
the evaluation result Sij of the ith user on the jth service parameter and the evaluation result Sj of the jth service parameter satisfy the following relationship:
wherein, N is the total number of users evaluating the first network, wi is the current evaluation weight of the ith user; the Wi 'is the normalized evaluation weight of the ith user, and the Wi' satisfies the following relationship:
4. the method according to claim 1, wherein said adding a weighted sum of the evaluation results of each of the service parameters as the evaluation result of the first network comprises:
the evaluation result S of the first network satisfies the following relationship:
wherein, X is the total number of service parameters of the first network, sj is the evaluation result of the j-th service parameter, wj is the preset weight of the j-th service parameter; the Wj 'is normalized evaluation weight of the j-th service parameter, and the Wj' satisfies the following relationship:
5. the method of claim 1, wherein the method is performed on a blockchain in the form of a smart contract, the contents of a blockvolume record of the blockchain including: user history rating record, user level and rating result of network.
6. An apparatus for obtaining a network evaluation result, comprising:
a first acquisition unit: a historical evaluation record for each user, the historical evaluation record comprising evaluation data for one or more business parameters of the first network;
a second acquisition unit: the method comprises the steps of obtaining current evaluation weights of each user; the current evaluation weight of a user is inversely proportional to the characteristic value of the user, and the characteristic value of the user is the proportion of the same comments made by the user to the total number of the comments of the user;
a third acquisition unit: the evaluation method is used for respectively acquiring the evaluation result of each service parameter of the first network according to the historical evaluation record and the current evaluation weight; the evaluation result of a service parameter is the weighted sum of the evaluation data of the service parameter by each user;
a calculation unit: the method comprises the steps of using a weighted sum of evaluation results of each service parameter as an evaluation result of the first network;
for the first user, the second obtaining unit is specifically configured to:
acquiring a characteristic value of the first user;
determining the current level of the first user according to the previous level of the first user and the characteristic value of the first user; the current level is inversely proportional to the eigenvalue;
Determining the current evaluation weight of the first user according to the current level of the first user; the current evaluation weight is proportional to the current level;
the current level L of the first user, the previous level L of the first user and the characteristic value Rpi of the first user satisfy the following relationship:
wherein Ls is a level step length, L1 is a first level threshold, and R is a feature threshold;
the current evaluation weight W of the first user and the current level L of the first user satisfy the following relationship:
the M is a weight step length, the Ls is a level step length, the L2 is a second level threshold, the M is a positive integer which is more than or equal to 0 and less than or equal to mmax, the mmax is a weight upper limit minus the b, and the a and the b are constants.
7. The apparatus of claim 6, wherein the characteristic values of the user comprise: and the number of the user comments is proportional to the total number of the user comments.
8. The apparatus according to claim 6, comprising:
the evaluation result Sij of the ith user on the jth service parameter and the evaluation result Sj of the jth service parameter satisfy the following relationship:
Wherein, N is the total number of users evaluating the first network, wi is the current evaluation weight of the ith user; the Wi 'is the normalized evaluation weight of the ith user, and the Wi' satisfies the following relationship:
9. the apparatus according to claim 6, comprising:
the evaluation result S of the first network satisfies the following relationship:
wherein, X is the total number of service parameters of the first network, sj is the evaluation result of the j-th service parameter, wj is the preset weight of the j-th service parameter; the Wj 'is normalized evaluation weight of the j-th service parameter, and the Wj' satisfies the following relationship:
10. the apparatus according to claim 6, comprising: the apparatus is configured to perform on a blockchain, the contents of a blockvolume record of the blockchain including: user history rating record, user level and rating result of network.
11. An apparatus for obtaining a network evaluation result, the apparatus comprising: one or more processors, and memory;
the memory is coupled with the one or more processors; the memory is for storing computer program code comprising instructions which, when executed by the one or more processors, cause the apparatus to perform the method of any of claims 1-5.
12. A computer readable storage medium comprising computer instructions which, when run on a computer, cause the computer to perform the method of any of claims 1-5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101650813A (en) * | 2008-08-11 | 2010-02-17 | 阿里巴巴集团控股有限公司 | System and method for assessing network |
| CN103310353A (en) * | 2013-07-05 | 2013-09-18 | 重庆大学 | Anti-attack data filtering optimization system and method |
| CN107392667A (en) * | 2017-07-24 | 2017-11-24 | 中国联合网络通信集团有限公司 | The determination method, apparatus and the network equipment of acceptance of the users |
| CN109829746A (en) * | 2018-12-24 | 2019-05-31 | 北京城市网邻信息技术有限公司 | A kind of integrated evaluating method, device, server and storage medium |
| CN109993448A (en) * | 2019-04-08 | 2019-07-09 | 湖北风口网络科技有限公司 | A kind of appraisal procedure and system of enterprise network public sentiment potential risk |
| JP6856800B1 (en) * | 2020-04-14 | 2021-04-14 | ＶＩＳＩＴＳ Ｔｅｃｈｎｏｌｏｇｉｅｓ株式会社 | Online evaluation method and online server for evaluation |
-
2021
- 2021-06-22 CN CN202110694908.7A patent/CN113487148B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101650813A (en) * | 2008-08-11 | 2010-02-17 | 阿里巴巴集团控股有限公司 | System and method for assessing network |
| CN103310353A (en) * | 2013-07-05 | 2013-09-18 | 重庆大学 | Anti-attack data filtering optimization system and method |
| CN107392667A (en) * | 2017-07-24 | 2017-11-24 | 中国联合网络通信集团有限公司 | The determination method, apparatus and the network equipment of acceptance of the users |
| CN109829746A (en) * | 2018-12-24 | 2019-05-31 | 北京城市网邻信息技术有限公司 | A kind of integrated evaluating method, device, server and storage medium |
| CN109993448A (en) * | 2019-04-08 | 2019-07-09 | 湖北风口网络科技有限公司 | A kind of appraisal procedure and system of enterprise network public sentiment potential risk |
| JP6856800B1 (en) * | 2020-04-14 | 2021-04-14 | ＶＩＳＩＴＳ Ｔｅｃｈｎｏｌｏｇｉｅｓ株式会社 | Online evaluation method and online server for evaluation |
Non-Patent Citations (1)
| Title |
|---|
| 负面评论挖掘的网络口碑危机预警模糊推理;张艳丰 等;《图书情报工作》;第60卷(第09期);第75-82页 * |
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