CN114037340A - Service attraction model-based cross-boundary service efficiency evaluation method - Google Patents
Service attraction model-based cross-boundary service efficiency evaluation method Download PDFInfo
- Publication number
- CN114037340A CN114037340A CN202111407793.5A CN202111407793A CN114037340A CN 114037340 A CN114037340 A CN 114037340A CN 202111407793 A CN202111407793 A CN 202111407793A CN 114037340 A CN114037340 A CN 114037340A
- Authority
- CN
- China
- Prior art keywords
- service
- cross
- user
- boundary
- attraction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 claims abstract description 34
- 239000002131 composite material Substances 0.000 claims abstract description 13
- 238000010606 normalization Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 6
- 239000004576 sand Substances 0.000 claims description 4
- 238000011160 research Methods 0.000 abstract description 7
- 230000004927 fusion Effects 0.000 description 11
- 238000004364 calculation method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 235000013311 vegetables Nutrition 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 230000009193 crawling Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000004091 panning Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000003796 beauty Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000005465 channeling Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000013209 evaluation strategy Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012856 packing Methods 0.000 description 1
- 238000013077 scoring method Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Game Theory and Decision Science (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Tourism & Hospitality (AREA)
- Pure & Applied Mathematics (AREA)
- Algebra (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a service efficiency evaluation method based on a service attraction model, which comprises the following steps: step S1, collecting service data and user data; step S2, judging a single service mode; step S3, calculating a QoS value of the service according to an analytic hierarchy process; step S4, calculating the user quantity of the service, if the service is the traditional service, the variable is the user quantity of the service itself; if the service is a cross-border service, the variable is the user amount plus the pilot amount; step S5, calculating service cost according to the operation cost and the diversion cost of the service; step S6, data normalization processing; step S7, calculating an attraction value of the single cross-boundary service by using a service attraction model based on the influence factors of supply and demand matching; step S8, calculating a composite cross-boundary service attraction value to evaluate service efficiency; the invention is extremely important for researching next-generation service, provides a new research idea and tool for evaluating cross-boundary service, and helps service providers to realize mutual benefits and win-win.
Description
Technical Field
The invention belongs to the technical field of cross-boundary service and computers, and particularly relates to a cross-boundary service efficiency evaluation method based on a service attraction model.
Background
With the rapid development of cloud computing and software definition technologies, eventing-as-a-service (xaas) has become a supporting technology for the dynamic combination and cross-border collaboration of heterogeneous resources. The online resource package mainly includes Software applications, Service platforms, infrastructures, and the like, and typically includes Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). In addition, offline resource packing is closely related to specific application fields such as Design as a Service (DaaS) and Simulation as a Service (SimaaS). With the appearance and development of XaaS, the underlying infrastructure breaks the technical barriers among various service providers, so that service collaboration is more efficient and service sharing is more convenient. Service convergence has been deep into our daily lives, for example, GPS is formed by convergence of map and positioning services. Further, the new retail service is formed by integrating logistics, e-commerce, supermarket and other services. Further, taking intelligent traffic as an example, the cross-border service acquires real-time traffic information across different fields: the system comprises a traffic monitoring camera, a weather condition, a social network and the like, and a service solution taking a user as a guide is constructed. Under this background, service fusion receives attention from academic and industrial circles, and is classified into data fusion, capability fusion, and domain fusion according to the degree of service fusion: (ii) a typical representation of data fusion is "service composition"; the method can combine data and capabilities of multiple services to meet business requirements of users in complex scenes. ② typical examples of capability fusion are "big services"; in the big data era, service reuse is more and more important, and the extended big service concept is a collection of heterogeneous virtualized services and physical services (e.g., Web services, cloud services, mashups, internet of things, etc.) centered on big data. Third, a typical example of domain fusion is "cross-border service"; the so-called cross-border service is to combine services from different domains to complete more complex transactions. For example, the convergence of online retail services, offline supermarket services, payment services and logistics creates new retail. The domain fusion can be the highest level of service fusion, and the domain fusion can promote the industry to form a new service ecosystem.
By integrating industries, organizations and value chains, cross-border services promote sharing and collaboration among industries, creating new values. Meanwhile, because of the complex characteristics of the cross-boundary service, how to effectively evaluate the performance of the cross-boundary service is also the focus of research in academia and industry at present. Further, we may be more concerned about the performance of the cross-border service, which refers to the effectiveness of the commercial value, i.e., performance is a valuable output/time. Therefore, with cross-border services, the performance is first evaluated. For the performance evaluation problem of cross-border services, researchers have done a lot of related work:
the service efficiency evaluation is generated after world war II and based on military operational research and probability theory, and the evaluation goes through three development stages of simple quantitative calculation, research range expansion and research subject deepening. Service performance is a measure of service value and may directly affect the user's choice of service. Evaluation methods are generally classified into analytic methods, simulation methods, statistical methods, and multi-index comprehensive evaluation methods. In the prior art, the common method for evaluating the performance of cross-border services is the Multiple Criterion Decision Making (MCDM) setting, which includes fuzzy Analytical Hierarchy Process (AHP) and technical for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques. It uses weights to describe each attribute and normalizes the results. In MCDM, single-target optimization aggregates QoS parameter values into a performance function, and then compares the results to determine the optimal combination; multi-objective optimization associates each QoS parameter with a performance function and compares the services of each parameter to determine the best combination.
The performance is calculated according to the aggregated QoS value, and Kouicem, Yachiret, and the like convert the service combination problem into a single-target optimization problem to find a service with the best performance. Aoudia et al propose a Service combination method present an adaptive QoS-Aware Service Composition Approach (P-MPGA) based on a multi-population genetic algorithm in a self-adaptive QoS-Aware Internet of things medical environment, and 12 QoS dimensions are evaluated, but the method is too complex and time-consuming. The document provides an innovative method for evaluating QoS of a service provider in a service-oriented environment, provides a tensor-based QoS model, and constructs a multidimensional relation between QoS evaluation factors and QoS values of the service provider, but the method is not comprehensive in consideration. At the same time, the performance assessment also takes into account credits. The document uses a profile created based on social trust relationships in an internet of things environment to evaluate trust levels of services and methods of evaluating QoS for services. But there may be errors because trust is assessed through feedback provided by the social network as well as subjective trust. Wang et al summarize user trust and service reputation for services for accurate confidence assessment. For complex services, the literature contributes to how to measure the effectiveness of individuals and complex services. The efficiency of individual services is measured by integrating consumer preferences, objective factors and expert subjective judgments, and then the efficiency of the services is measured based on an expert scoring method. However, the method results in subjective colors, and only one service is calculated by using the methods.
The existing cross-boundary service efficiency evaluation method has the following defects:
(1) the existing performance evaluation of cross-boundary service only faces to the demand side but does not consider the supplier, and the cross-boundary service needs offline resource support, so the information of the supplier influences the evaluation effect all the time. Therefore, conventional performance evaluation strategies lack any consideration of provider real-time status information in cross-border services.
(2) The existing performance evaluation method of the cross-border service is not comprehensive, complex and time-consuming in evaluating elements.
(3) The existing performance evaluation method for cross-boundary service does not classify the modes from the flow guide perspective and performs performance analysis.
Therefore, it is desirable to provide a more reasonable and simple method for evaluating the performance of a cross-border service in a comprehensive manner.
Disclosure of Invention
The invention aims to provide a cross-boundary service performance evaluation method based on a service attraction model,
the invention is implemented by adopting the following technical scheme:
a service attractive model-based cross-boundary service efficiency evaluation method is characterized in that the cross-boundary service efficiency is judged based on the service attractive model; the service attraction model comprises two aspects of a supply side and a demand side; evaluating the cross-boundary service efficiency at the supply side and the demand side; the method comprises the following steps:
step S1, acquiring service data related to the supply side and user data related to the demand side;
step S2, judging the single cross-boundary service mode type provided by the supply side by the diversion mode, wherein the single cross-boundary service mode type comprises: platform type, module type and relational type;
step S3: calculating the QoS value of the single cross-boundary service mode through an analytic hierarchy process;
step S4: generating a pilot flow U according to the following formula according to the user quantity of the service provided by the supply side in the single cross-boundary service modest；
Wherein, BstThe user quantity of the service s is transgressed at the moment t; b isjstIs the user traffic that service j leads to service s at time t;
step S5: calculating the service cost d for a single cross-boundary service model by the following formulas，
Wherein, OsIs the operating cost of service s; during the diversion, r is the cost of the user to successfully register for service s; ρ is the successful registered user volume of service s, α is the transaction volume of service s; μ is the transaction price for service s, b is the percentage of transaction cost to transaction price; t is a set of traditional service nodes, and H is a set of cross-border service nodes;
step S6: normalizing data such as traffic guidance quantity, service cost and the like in a cross-border service mode to generate a service attraction model; namely:
wherein, FusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; qossIs the quality of service; esThe number of services in the service pattern for service s; to be supplied sideQuality is defined as the product of QoS and number of services; a. theuIs the user purchasing power; u shapestIs the user volume of service s at time t; further, the quality on the demand side is defined as the product of the user purchasing power and the user amount. dsIs the cost of service; duIs the physical distance of user u from the service; the distance is defined as dsAnd duThe product of (a);
step S7: calculating an attraction value F of a single cross-boundary service mode from positive and negative influence factors of supply and demand matching according to a service attraction modelusg；
Step S8: calculating the service efficiency F of the attraction value of the composite cross-boundary service according to the attraction value of the single cross-boundary service mode by the following formulaus：
FusThe method is the attraction of a composite cross-boundary service mode s to a user u; fusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; g is a single set of cross-border service patterns.
Further, the normalizing the data comprises:
according to
Carrying out normalization processing on the data if xiThe higher the value is, the better xiIs a positive factor; if the representation is lower, the better, xiIs a negative factor.
The invention has the following beneficial effects:
according to the method, through the stability and competitiveness of the service attraction model cross-boundary service, the efficiency of the service provided by the supply side on the attraction of the user in the demand side is evaluated, and the modeling is carried out from the perspective of the supply side and the demand side. The invention makes important contribution to the research of next-generation service, provides new research thought and tool for the evaluation of cross-boundary service, and helps service providers to realize mutual-profit and win-win results.
Drawings
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings;
fig. 1 is a flowchart illustrating a cross-boundary service performance evaluation method based on a service attraction model according to an embodiment of the present invention.
FIG. 2 shows a schematic diagram of a service affinity model.
Fig. 3 shows a schematic diagram of different cross-border services.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described below in conjunction with the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments and drawings obtained by a person skilled in the art without any inventive work belong to the scope of protection of the present invention. It is to be understood by persons skilled in the art that the following detailed description is illustrative and not restrictive, and is not to be taken as limiting the scope of the invention.
The invention provides a service performance evaluation method based on a service attraction model, which is used for judging the service performance of a cross boundary based on the service attraction model; the service attraction model comprises two aspects of a supply side and a demand side; evaluating the cross-boundary service efficiency at the supply side and the demand side; in some optional implementation manners of this embodiment, the obtaining of the relevant service data and user data further includes the following steps:
step S1, acquiring service data related to the supply side and user data related to the demand side; reliability of the service, price and time spent by the user to obtain the service, number of services in the mode in which the service is located, user purchasing power, operating cost of the service, and user volume of the service. For example, the reliability of the service is obtained by crawling user scores of a mobile phone APP store, the purchasing power of the user, the price and the time spent by the user for obtaining the service are obtained through reports of related services, and the service operation cost and the user amount are obtained through annual reports;
step S2, judging the single cross-boundary service mode type provided by the supply side by the diversion mode, wherein the single cross-boundary service mode type comprises: platform type, module type and relational type;
step S3: calculating the QoS value of the single cross-boundary service mode through an analytic hierarchy process;
calculating a QoS value of a service in a single cross-boundary service type by using an analytic hierarchy process, and calculating according to the reliability of the service, the price and time spent by a user for obtaining the service and other factors; calculating a service QoS value of a service in a single cross-border service type, calculating the QoS value of the service using an Analytic Hierarchy Process (AHP) according to factors such as reliability of the service and price and time taken by a user to obtain the service, further comprising:
analytic Hierarchy Process (AHP) aims to quantify the relative priority of a given set of alternatives on a scale of ratios, at the discretion of the decision-maker, and to emphasize the importance of the intuitive discretion of the decision-maker in the decision-making process and the consistency of the comparison of the alternatives. The advantage of this approach is that it organizes tangible and intangible factors in a systematic way and provides a structured but relatively simple solution to the decision problem.
According to QossThe QoS value of the service is calculated as 0.637 · q +0.258 · p +0.105 · a, where q, p, a are the reliability of the service and the price and time it takes for the user to obtain the service, respectively.
Step S4: generating a pilot flow U according to the following formula according to the user quantity of the service provided by the supply side in the single cross-boundary service modest；
Wherein, BstThe user quantity of the service s is transgressed at the moment t; b isjstIs the user traffic that service j leads to service s at time t;
calculating the user quantity of the service, and calculating the traffic guidance quantity, wherein the traffic guidance quantity is the sum of the traffic of other services to the service, and the user quantity of the cross-boundary service is the user quantity of the cross-boundary service plus the traffic guidance quantity; calculating the user quantity of the service, if the service is a cross-boundary service, calculating the flow guide quantity, wherein the flow guide quantity is the sum of the flow of other services to the service, the user quantity of the cross-boundary service is the user quantity of the cross-boundary service plus the flow guide quantity,
further comprising: and increasing the number of cross-boundary service users according to the user flow among the guide services. "diversion" refers to the act of guiding users between services to consume services in various ways, which has a great influence on the user amount of the services. If the service is the traditional service, the pilot flow does not need to be calculated, and the user quantity of the service is directly substituted into the variable;
according to Ust＝Bst+∑j∈HBjsts ≠ j calculates the user volume of the service, where BstThe user quantity of the service s is transgressed at the moment t; b isjstIs the user traffic that service j leads to service s at time t.
Step S5: calculating the service cost d for a single cross-boundary service model by the following formulas，
Wherein, OsIs the operating cost of service s; during the diversion, r is the cost of the user to successfully register for service s; ρ is the successful registered user volume of service s, α is the transaction volume of service s; μ is the transaction price for service s, b is the percentage of transaction cost to transaction price; t is a set of traditional service nodes, and H is a set of cross-border service nodes;
step S6: normalizing data such as traffic guidance quantity, service cost and the like in a cross-border service mode to generate a service attraction model; namely:
wherein, FusgIs a cross-boundary in a single cross-boundary service model gThe attraction of service s to user u; qossIs the quality of service; esThe number of services in the service pattern for service s; defining quality of a providing side as a product of QoS and the number of services; a. theuIs the user purchasing power; u shapestIs the user volume of service s at time t; further, the quality on the demand side is defined as the product of the user purchasing power and the user amount; dsIs the cost of service; duIs the physical distance of user u from the service; the distance is defined as dsAnd duThe product of (a);
further, the normalizing the data comprises:
according to
Carrying out normalization processing on the data if xiThe higher the value is, the better xiIs a positive factor; if the representation is lower, the better, xiIs a negative factor; step S7: calculating an attraction value F of a single cross-boundary service mode from positive and negative influence factors of supply and demand matching according to a service attraction modelusg；
Step S8: calculating the service efficiency F of the attraction value of the composite cross-boundary service according to the attraction value of the single cross-boundary service mode by the following formulaus：
FusThe method is the attraction of a composite cross-boundary service mode s to a user u; fusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; g is a single set of cross-border service patterns.
In some optional implementation manners of this embodiment, the obtaining related service data and user data further includes:
reliability of the service, price and time spent by the user to obtain the service, number of services in the mode in which the service is located, user purchasing power, operating cost of the service, and user volume of the service. For example, the reliability of the service is obtained by crawling the user score of the mobile phone APP store, the purchasing power of the user, the price and time spent by the user for obtaining the service are obtained through the report of the relevant service, and the service operation cost and the user amount are obtained through annual reports.
In some optional implementations of this embodiment, determining which of the single cross-boundary service modes the service is in, each of the single cross-boundary service modes separately calculating the attraction value, further includes:
the composite cross-boundary service mode is determined according to the diversion manner, which single cross-boundary service mode is formed by combining several single cross-boundary service modes, as shown in fig. 2, the single cross-boundary service mode includes a platform type, a module type and a relationship type. The attraction values are then calculated separately for the single cross-border service mode. The four cross-border service modes are described below:
mode 1: platform type
In the platform-type cross-border service mode, user traffic is channeled to other services through the platform service, and price is a key factor for the channeling. This mode provides strong supply but little synergy. Panning is a good example. It connects many service providers and users. And (5) delivering advertisements of the service provider on the home page of the Taobao, and guiding the advertisements to the service provider. On the other hand, the service provider promotes the product of the user through other channels, and guides the user to visit the treasure. Furthermore, service providers do not direct user traffic to each other unless they have the same partner.
Mode 2: module type
As user requirements become more complex, the module type cross-border service realizes service integration in a uniform manner. A good example is the beauty group take-out. Under the module type cross-border service mode, the entrance service is the American group take-out. When the user orders take-out, the American group take-out firstly calls the dish selecting service interface, then the payment service interface and finally the take-out service interface. However, the user can pause the service and return to the portal service at any time.
Mode 3: relation type
When user requirements are highly complex, services establish a tight relationship and require high coordination and interdependence to obtain competitive advantages. These relationships can be adjusted through trust between services. Panning and curds are two typical examples. An interface is arranged between the Taobao and the vegetable and bird, and the products of the other side are recommended mutually, so that the user can jump from the Taobao to the vegetable and bird. The user can jump from the vegetable bird to the treasure in the same way.
Mode 4: composite type
In the real world, most of the cross-boundary services are a composite cross-boundary service model. Any combination of platform type, module type, and relational type cross-boundary services is referred to as a "composite cross-boundary service".
In some alternative implementations of the present embodiment, the QoS value of the service is calculated, the QoS value is evaluated using an Analytic Hierarchy Process (AHP) analysis, and the weighting index is passed through a consistency test. The calculation according to the reliability of the service and the price and time spent by the user to obtain the service further comprises:
according to QossThe QoS value of the service is calculated as 0.637 · q +0.258 · p +0.105 · a, where q, p, a are the reliability of the service and the price and time it takes for the user to obtain the service, respectively. The obtained reliability of the service and the price and time spent by the user to obtain the service can be substituted into a formula to calculate the QoS value of the service, and the corresponding weight calculation process is as follows:
constructing corresponding judgment matrix
The maximum eigenvalue of the matrix is calculated to be 3.0385, and further calculation is carried out to obtain
Due to the fact that
The consistency of the judgment matrix is acceptable, and the weights obtained after normalization of the feature vectors are respectively as follows: 0.637,0.258,0.105.
In some alternative implementations of the present embodiment, the user volume of the service is calculated, because the cross-border service is generally integrated in the form of a network across organizations, domains, and processes. The user can enjoy a plurality of services only by interacting with one of the cross-border services. Whereas with conventional services, users need to interact with them separately to enjoy different services. Therefore, the traditional service is essentially single-point competition, cannot meet the complex requirements of users, and has no diversion phenomenon. Further, if the service is a conventional service, the amount of users who directly use the service; if the service is a cross-boundary service, calculating the traffic lead, wherein the user quantity of the cross-boundary service is the sum of the user quantity of the cross-boundary service and the traffic lead of other services, and the method further comprises the following steps:
and increasing the number of cross-boundary service users according to the user flow among the guide services. "diversion" refers to the behavior of guiding users to consume services through various ways among services, and has great influence on the user quantity of the services;
according to Ust＝Bst+∑j∈HBjsts ≠ j calculates the user volume of the service, where BstThe user quantity of the service s is transgressed at the moment t; b isjstIs the user traffic that service j leads to service s at time t. And substituting the user quantity and the diversion quantity of the cross-boundary service into the cross-boundary service flow to calculate the new user quantity of the cross-boundary service.
In some optional implementations of this embodiment, calculating, according to the calculated service cost, the operation cost and the diversion cost of the service further includes:
according to
Calculating the service cost; wherein, OsIs the operating cost of service s. During the diversion, r is the cost of the user successfully registering for service s. Further, ρ is the amount of successfully registered users of the service s, and α is the transaction amount of the service s. And μ is the transaction price for service s and b is the percentage of transaction cost to transaction price. T is a set of legacy service nodes, and H is a set of cross-border service nodes. User registration costs, service transaction prices, and transaction cost as a percentage of the price are obtained through the relevant reports.
In some optional implementations of this embodiment, the normalizing the data includes:
according to
Carrying out normalization processing on the data if xiThe higher the value is, the better xiIs a positive factor; if the representation is lower, the better, xiIs a negative factor. For example, if the user quantity is a positive factor, the user quantities of different services are compared, the maximum value, the minimum value and the self value are substituted into the formula of the positive factor, and finally the normalized value is calculated.
In some optional implementations of this embodiment, the evaluating the service performance by using the service attraction model based on the positive and negative influence factors from the supply and demand matching further includes:
according to
Evaluating the service performance of a single mode, FusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; qossIs the quality of service; esIs the number of services in the service pattern of service s. The quality on the donor side is defined as the product of QoS and the number of services. A. theuIs the user purchasing power; u shapestIs the user volume of service s at time t; further, the quality on the demand side is defined as the product of the user purchasing power and the user amount; dsIs the cost of service; duIs the physical distance of user u from the service; the distance is defined as dsAnd duThe product of (a). And substituting the service quality, the service number, the user purchasing power and the user quantity into a formula to calculate the service efficiency of the single mode.
In some optional implementations of the embodiment, calculating the composite type cross-boundary service attraction value to evaluate the service performance further includes:
according to Fus＝∑g∈GFusgEvaluation of service Performance, FusThe method is the attraction of a composite cross-boundary service mode s to a user u; fusgIs a single cross-border suitAttraction of cross-border services s in the service pattern g to the user u; g is a single set of cross-border service patterns. Substituting the single cross-boundary service attraction value into a formula to be added, and finally obtaining the cross-boundary service efficiency.
Claims (2)
1. A cross-boundary service efficiency evaluation method based on a service attraction model is characterized in that: the method is based on a service attraction model to judge the cross-boundary service efficiency; the service attraction model comprises a supply side and a demand side; the supply side and the demand side evaluate the cross-boundary service efficiency from two angles; the method comprises the following steps:
step S1, acquiring service data related to the supply side and user data related to the demand side;
step S2, judging the single cross-boundary service mode type provided by the supply side by the diversion mode, wherein the single cross-boundary service mode type comprises: platform type, module type and relational type;
step S3: calculating the QoS value of the single cross-boundary service mode through an analytic hierarchy process;
step S4: generating a pilot flow U according to the following formula according to the user quantity of the service provided by the supply side in the single cross-boundary service modest；
Wherein, BstThe user quantity of the service s is transgressed at the moment t; b isjstIs the user traffic that service j leads to service s at time t;
step S5: calculating the service cost d for a single cross-boundary service model by the following formulas；
Wherein, OsIs the operating cost of service s; during the diversion, r is the cost of the user to successfully register for service s; ρ is the successful registered user volume of the service s and α is the services transaction amount; μ is the transaction price for service s, b is the percentage of transaction cost to transaction price; t is a set of traditional service nodes, and H is a set of cross-border service nodes;
step S6: normalizing data such as the diversion user quantity, the service cost and the user purchasing power in a cross-border service mode to generate a service attraction model; namely:
wherein, FusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; qossIs the quality of service; esThe number of services in the service pattern for service s; defining quality of a providing side as a product of QoS and the number of services; a. theuIs the user purchasing power; u shapestIs the user volume of service s at time t; defining the quality of the demand side as the product of the purchasing power of the user and the user quantity; dsIs the cost of service; duIs the physical distance of user u from the service; the distance is defined as dsAnd duThe product of (a);
step S7: calculating the attraction value F of the single cross-boundary service mode from the positive and negative influence factors of supply and demand matching according to the service attraction modelusg；
Step S8: calculating and generating composite cross-boundary service attraction value service efficiency F according to the attraction value of the single cross-boundary service mode by the following formulaus：
FusThe method is the attraction of a composite cross-boundary service mode s to a user u; fusgIs the attraction of the cross-boundary service s in the single cross-boundary service mode g to the user u; g is a single set of cross-border service patterns.
2. The method as claimed in claim 1, wherein the cross-boundary service performance evaluation method based on the service attraction model comprises: the data normalization processing comprises the following steps:
according to
Carrying out normalization processing on the data if xiThe higher the value is, the better xiIs a positive factor; if the representation is lower, the better, xiIs a negative factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407793.5A CN114037340B (en) | 2021-11-24 | 2021-11-24 | Cross-border service effectiveness evaluation method based on service attraction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407793.5A CN114037340B (en) | 2021-11-24 | 2021-11-24 | Cross-border service effectiveness evaluation method based on service attraction model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114037340A true CN114037340A (en) | 2022-02-11 |
| CN114037340B CN114037340B (en) | 2024-06-04 |
Family
ID=80138810
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111407793.5A Active CN114037340B (en) | 2021-11-24 | 2021-11-24 | Cross-border service effectiveness evaluation method based on service attraction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114037340B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014071776A1 (en) * | 2012-11-06 | 2014-05-15 | 中兴通讯股份有限公司 | Method and system for evaluating quality of experience of communication service users |
| CN110505105A (en) * | 2019-09-26 | 2019-11-26 | 中国联合网络通信集团有限公司 | Control method, device, equipment and the storage medium of network service quality |
| CN110650032A (en) * | 2018-06-27 | 2020-01-03 | 复旦大学 | Method for constructing QoS-based application optimization deployment scheme in multi-cloud environment |
| CN111612330A (en) * | 2020-05-19 | 2020-09-01 | 浙江大学 | Service mode quantitative evaluation method for cross-boundary service |
-
2021
- 2021-11-24 CN CN202111407793.5A patent/CN114037340B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014071776A1 (en) * | 2012-11-06 | 2014-05-15 | 中兴通讯股份有限公司 | Method and system for evaluating quality of experience of communication service users |
| CN110650032A (en) * | 2018-06-27 | 2020-01-03 | 复旦大学 | Method for constructing QoS-based application optimization deployment scheme in multi-cloud environment |
| CN110505105A (en) * | 2019-09-26 | 2019-11-26 | 中国联合网络通信集团有限公司 | Control method, device, equipment and the storage medium of network service quality |
| CN111612330A (en) * | 2020-05-19 | 2020-09-01 | 浙江大学 | Service mode quantitative evaluation method for cross-boundary service |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114037340B (en) | 2024-06-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Liao et al. | Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection | |
| Liu et al. | An overview of ARAS method: Theory development, application extension, and future challenge | |
| Wu et al. | Evaluation the drivers of green supply chain management practices in uncertainty | |
| Amindoust | Supplier selection considering sustainability measures: an application of weight restriction fuzzy-DEA approach | |
| Yao et al. | Optimising the configuration of green supply chains under mass personalisation | |
| Oukil et al. | A hybrid multi‐attribute decision‐making procedure for ranking project proposals: A historical data perspective | |
| Meng et al. | A method to solve cold-start problem in recommendation system based on social network sub-community and ontology decision model | |
| Binh et al. | Firm size, business sector and quality of accounting information systems: Evidence from Vietnam | |
| Liao et al. | GRBMC: An effective crowdsourcing recommendation for workers groups | |
| Zong et al. | Customer stratification theory and value evaluation—analysis based on improved RFM model | |
| Zhang et al. | A pattern-recognition-based algorithm and case study for clustering and selecting business services | |
| Weng et al. | Private‐Sector Partner Selection for Public‐Private Partnership Projects Based on Improved CRITIC‐EMW Weight and GRA‐VIKOR Method | |
| Fan et al. | Optimal selection of design scheme in cloud environment: A novel hybrid approach of multi-criteria decision-making based on F-ANP and F-QFD | |
| Wright | Supply chain strategies of manufacturers in Romania | |
| CN114021873A (en) | Data index quantification method and intelligent park enterprise value evaluation system | |
| Thasni et al. | Cloud service provider selection using fuzzy TOPSIS | |
| Rekik et al. | Quality evaluation of web sites: a comparative study of some multiple criteria decision making methods | |
| WO2017016403A1 (en) | Method and apparatus for determining brand index information about service object | |
| CN114037340A (en) | Service attraction model-based cross-boundary service efficiency evaluation method | |
| CN110837962A (en) | Power customer viscosity calculation method | |
| Sun et al. | E-commerce logistics distribution mode research | |
| Lei et al. | Research on opinion leaders recognition based on TOPSIS in open source design community | |
| Ilieva | Decision analysis for big data platform selection | |
| CN104778636B (en) | Method is recommended in the information service of apparel maintenance support | |
| Huang et al. | Relative Similarity Programming Model for Uncertain Multiple Attribute Decision‐Making Objects and Its Application |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |