CN107239853B - Intelligent housekeeper system based on cloud computing and working method thereof - Google Patents
Intelligent housekeeper system based on cloud computing and working method thereof Download PDFInfo
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Abstract
The invention discloses a cloud computing-based intelligent housekeeper system and a working method thereof, wherein the system comprises an enterprise-level user side and a common personal user side; the enterprise-level user side comprises a product configuration module, a product registration module, a product detail management module, a product association configuration module, a portal management module, a sales financial management module and an express manager management module. The common personal user side comprises a settlement module, a user information module, a housekeeper service module, a data report query module and a data navigation module. The method adopts the basic cloud self-adaptive genetic algorithm crossover operator, and realizes the minimization of the operation cost of the housekeeper management system and the timely and accurate processing of uncertain information. The cloud computing-based method provided by the invention improves the speed of optimal solution convergence, increases the search range of the global optimal solution, reduces the operation time and cost of the intelligent housekeeper system, improves the operation efficiency of the system and shortens the response time.
Description
Technical Field
The invention relates to a smart housekeeper system platform and a method, in particular to a smart housekeeper system platform and a method based on the combination of a cloud computing technology and an intelligent quantum algorithm, and belongs to the technical field of cloud computing technology and internet application management.
Background
With the development of cloud computing and internet technologies, more and more O2O platforms and APP systems based on cloud platforms and the internet appear, which brings convenience to the life of people. For example, the system includes commodity purchase management based on APP, social security payment management, and property and water and electricity charge management. In these application systems, since various different types of data are scattered and unordered, how to effectively collect and reasonably classify the data of the user, and to push data information required to be managed in time and track, navigate and update the data to the user according to the classification result is a major problem that needs to be solved urgently.
The design of a common housekeeper management system platform in the prior art is mostly designed based on a general heuristic algorithm, the data searching speed is low, the clustering analysis result is incomplete, the optimal solution is difficult to obtain, and the data is difficult to effectively predict and provide reference for future behaviors through extracting regular information from the data in the distributed management. This reduces the efficiency of data management, weakening the predictive function of data management.
In the application of an information management platform based on a cloud computing environment, some data are redundant and invalid, and must be processed by adopting appropriate means, such as data cleaning, data denoising, data clustering analysis and the like, and the data information is designed in a traceability manner according to the information of the type, scale, acquisition channel and the like of the data. Common data processing algorithms in the prior art are: dijkstra algorithm, genetic algorithm, ant colony algorithm, simulated annealing algorithm, tabu search algorithm and the like, and the existing housekeeper system has the following problems:
1. the prompting function algorithm for intelligently calculating the appointment time and the expense adopted in the existing system has complicated steps;
2. excessive running time and running cost are required, and the result is not accurate enough;
3. the future behavior is difficult to effectively predict and provide reference through data extraction regularity information, and scientific prediction capability is lacked.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to design a smart housekeeper system and a smart housekeeper method which have high convergence speed, can minimize the running time and running cost, can process and predict data in real time, and can provide high accuracy and high efficiency for users based on cloud computing.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a cloud computing based intelligent housekeeper system comprises an enterprise-level user side and a common personal user side;
the enterprise-level user side comprises:
a product configuration module: the system is used for product management and purchase protocol management and is responsible for configuration management of different modules;
a product registration module: the product registration code prefix is 3-bit major pinyin initial and the following is free coding;
the product detail management module: the system is used for displaying the configured product detail management and inquiring according to conditions;
a product association configuration module: for setting a batch of other products associated with this product;
a portal management module: the system is used for announcement management, information encyclopedia, focus map management, patent product relationship management and enterprise dynamic management;
a sales finance management module: the customer service system is used for checking the order progress condition of the customer service system; the system is used for the national financial managers to check all orders and financial data, and the data are divided according to order areas; the system is used for the sales payment manager to check three types of logs of WeChat payment, Payment and refund;
express manager management module: the system is used for nationwide express managers to check all orders and express data; the regional express manager is used for checking the data of the local region, and the data are divided according to the order region; for nationwide order distributors to distribute all orders; the order distribution method is used for distributing local orders by regional order distributors, wherein the initial region of the order is taken from a user region and is changed by an administrator;
the common personal user terminal comprises:
a settlement module: the system is used for reminding the user in time and providing a self-settlement function;
a user information module: the system is used for perfecting user information and perfecting addressee information;
a housekeeper service module: used for checking the purchased products, inquiring the data report, uploading data and downloading data;
the data report query module: the system is used for summarizing and querying data of income, purchase and expenditure of a user;
the data navigation module: the navigation path guidance function is used for rapidly acquiring relevant data.
A working method of a smart housekeeper system based on cloud computing comprises the following steps:
A. establishing a unified mathematical model of efficiency and stability of the intelligent housekeeper system;
the design of the unified mathematical model for stability efficiency and stability is to introduce a double-chain cloud quantum method to generate an event chain population and a time chain population, the smaller the numerical value of the event chain population is, the higher the rescheduling efficiency and stability is, and the unified mathematical model for stability efficiency and stability is designed as follows:
in the formula: fnRepresents the management completion time of the event n to be managed; bnRepresents the time of responding to the event n to be managed; DLnA completion time window representing an event n to be managed; t'nmAnd tnmRespectively representing the time when the module m of the intelligent housekeeper system starts to manage the event n to be managed in rescheduling and initial scheduling; PF () represents a deviation penalty value function; RT represents the current management time; n isiRepresenting the total number of events that the user needs to complete at the current moment; m represents the serial number of the module, namely, the natural serial numbers are used for respectively representing each module in the system in sequence;
B. the method comprises the following steps of updating data stored in a smart housekeeper system in real time, carrying out data cleaning and cluster analysis processing according to the scale and the type of the data, and in a sales finance module, a settlement module and a data report query module, in order to obtain an optimal solution of the data cleaning and the data cluster analysis and improve the convergence rate of the obtained solution and optimize cost variables, wherein the cross operator obtaining step based on a cloud self-adaptive genetic algorithm comprises the following steps:
b1, respectively representing two individuals participating in the crossover operation as father IFAnd parent body IM；
B2 from [1, 10]Selecting two random integers q within the range1And q is2Generating two sub-individuals on the basis of the two sub-individuals;
b3, production IDFor IDProduct list lambda ofDBefore q thereof1The position is formed by a mother individual IMFront q of1One position is determined by the lambda of the parentFDetermining;
b4 for IDService resource allocation list NDBefore q thereof2The position is formed by a mother individual IMFront q of2One position is determined, and other positions are determined by N of fatherFDetermining; in the same wayInteraction of father and mother to generate another child IT；
B5, exchange and plug-in for the list of products: the exchange type is different products of the exchange product list, if the relation constraint condition is not satisfied after the exchange, the original position is changed back, and the next exchange is carried out; insert means that the last position lc in the list of all pre-products of the mutation operator is calculated first1And the foremost position lc of all subsequent products in the list2Secondly at lc1And lc2Randomly selecting a position lc, and inserting the operator into the position of lc; the plug-in variation is a variation mode with more use, and the individual variation step is to firstly perform variation on each product j in the product list lambda in the individual IiCarrying out mutation according to the probability, and carrying out position transformation of an operator according to an insertion type mutation mode; each administrator assignment to the list of service resources in individual I
Randomly selecting N for the product corresponding to the positioniDistributing services meeting the requirements; generating cloud cross probability and mutation probability through a Y-condition cloud generator and a forward cloud generator, and generating a cross operator and a mutation operator through an X-condition cloud generator; lambda is an event list satisfying the time sequence constraint and arranging all management tasks; n is a management module distribution list which represents a vector formed by distribution modes corresponding to each event of the event list, i represents the serial number of the managed event, and lcRepresenting the position of operator insertion;
C. the processing result is fed back and stored through analysis and is provided to the client with predictable reference information: the predictable reference information comprises expense flow, purchasing and consumption interest point prediction and payment category tendency, the selection of searching for an optimal solution is realized, a settlement module and an express delivery manager response module obtain a non-dominated optimal solution through cloud computing, and the optimal solution obtained by the two modules adopts an AHP hierarchical method, namely, a total target layer, a sub-target layer and a scheme layer are included from top to bottom; the total target layer is a total target for solving the real-time management problem, the sub-target layers comprise sub-targets of minimizing total cost, minimizing operation time and maximizing customer satisfaction, and the scheme layer is designed based on a set of non-dominated solutions taken by a cloud computing method; the method comprises the following steps of solving an optimal solution of the intelligent housekeeper system by a cloud self-adaptive genetic algorithm based on an AHP strategy, wherein each individual corresponds to a group of solutions of the settlement module and the express delivery manager response module:
c1, randomly generating x individuals to form an initial population, and initializing pcrAnd pct；pcrRepresenting cloud crossover operators, pctRepresenting a cloud mutation operator;
c2, solving the fitness of each individual, and adaptively adjusting pcrAnd pct；
C3, selecting operation is carried out by adopting an optimal retention strategy;
c4, searching fitness value, judging whether there is individual update, if there is no update in 30 continuous generations, doubling the population number;
c5, judging whether an optimal solution is obtained or not, if so, outputting an optimal individual, otherwise, executing the step C2;
D. after data management and tendency judgment of a user tend to be stable, data management and prediction functions are completed, in a settlement module and an express manager response module, in order to optimize real-time prediction response time of a smart housekeeper system, an optimal solution is obtained by adopting a cloud computing-based method, and the method specifically comprises the following steps:
d1, initializing a population, and carrying out chromosome coding of a cloud genetic algorithm to represent events to be responded in the queue; the population is a to-be-managed event set of a user;
d2, designing a fitness evaluation function with an expression of fit (x) ═ 1/z (x), z (x) representing an individual objective function value, the smaller the function value the more excellent the individual is;
d3, selecting individuals for the population by using the optimal individual retention strategy and fitness proportion selection to determine the range of the individuals entering the next generation of the population;
d4 generation of crossover operator p by X condition generator of cloud modelcrAnd performing cross operation on the parent individuals; by double crossingIn the fork mode, the gene region between the cross points is placed at the head of the filial generation individual, the same codes in the father generation individual are removed, and other codes are copied into the filial generation in sequence; if the child individual exceeds the constraint condition, moving the position 0 for adjustment;
d5 generation of mutation operator p by X condition generator of cloud modelctAnd carrying out interchange mutation operation on individuals; random selection of r from gene codes of variant individuals1And r2Two codes, r1And r2The selected codes are exchanged for non-0 natural numbers, so that new individuals are generated;
d6, reconstructing a new population by selecting k excellent individuals of the parent population and k individuals of the offspring population to be combined, thereby enlarging the population size and increasing the search space; k next generation populations are obtained again through related operations of a genetic algorithm; the operation of the step can lead the excellent genes of the population to be better preserved and obtain better feasible solution and optimal solution;
d7, updating the quantum gate;
d8, judging whether the stopping condition is met, if not, jumping to the step D3, otherwise, stopping the operation of the algorithm.
Compared with the prior art, the invention has the following beneficial effects:
1. the method adopts the basic cloud self-adaptive genetic algorithm crossover operator, so that the minimization of the operation cost of the housekeeper management system and the timely and accurate processing of uncertain information are realized.
2. According to the invention, the optimal solution strategy of the intelligent housekeeper system is solved by adopting the cloud self-adaptive genetic algorithm based on the AHP strategy, so that the optimal decision scheme is obtained from a group of obtained non-dominated solutions.
3. Due to the data cleaning, denoising and clustering analysis algorithm designed by the invention, the prediction of the future behavior of the user is realized and the optimal housekeeping system management reference scheme is provided.
4. The cloud computing-based method provided by the invention improves the speed of optimal solution convergence, increases the search range of the global optimal solution, reduces the operation time and cost of the intelligent housekeeper system, improves the operation efficiency of the system and shortens the response time.
5. In summary, in order to solve the problems that the steps of the reservation and expense settlement function algorithm in the existing system are complicated, too much time and operation cost are needed, the result is inaccurate, and the prediction function is lacked, the cloud computing-based intelligent housekeeper system and the cloud computing-based intelligent housekeeper method are designed, so that the convergence speed of obtaining the optimal solution of the reasonable scheduling scheme in the housekeeper system is high; minimizing algorithm run time and reducing run cost; the cloud computing method is adopted to realize the cleaning and cluster analysis of the data and realize the real-time prediction processing function; the management scheduling scheme of the housekeeper system with high accuracy and high efficiency can be provided for the user.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. A cloud computing based intelligent housekeeper system comprises an enterprise-level user side and a common personal user side; the specific composition is shown in figure 1. A working method of an intelligent housekeeper system based on cloud computing comprises the following specific steps as shown in FIG. 2, wherein a compiling example of a module serial number m in the step A is as follows: the product configuration module is represented by 1, and the product registration module is represented by 2; until all modules are represented by all natural numbers.
The method of the present invention may be implemented in an embedded chip, a software module executed by a processor, or a combination of both. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (1)
1. A cloud computing based intelligent housekeeper system comprises an enterprise-level user side and a common personal user side;
the enterprise-level user side comprises:
a product configuration module: the system is used for product management and purchase protocol management and is responsible for configuration management of different modules;
a product registration module: the product registration code prefix is 3-bit major pinyin initial and the following is free coding;
the product detail management module: the system is used for displaying the configured product detail management and inquiring according to conditions;
a product association configuration module: for setting a batch of other products associated with this product;
a portal management module: the system is used for announcement management, information encyclopedia, focus map management, patent product relationship management and enterprise dynamic management;
a sales finance management module: the customer service system is used for checking the order progress condition of the customer service system; the system is used for the national financial managers to check all orders and financial data, and the data are divided according to order areas; the system is used for the sales payment manager to check three types of logs of WeChat payment, Payment and refund;
express manager management module: the system is used for nationwide express managers to check all orders and express data; the regional express manager is used for checking the data of the local region, and the data are divided according to the order region; for nationwide order distributors to distribute all orders; the order distribution method is used for distributing local orders by regional order distributors, wherein the initial region of the order is taken from a user region and is changed by an administrator;
the common personal user terminal comprises:
a settlement module: the system is used for reminding the user in time and providing a self-settlement function;
a user information module: the system is used for perfecting user information and perfecting addressee information;
a housekeeper service module: used for checking the purchased products, inquiring the data report, uploading data and downloading data;
the data report query module: the system is used for summarizing and querying data of income, purchase and expenditure of a user;
the data navigation module: the navigation path guiding function is used for rapidly acquiring related data;
the method is characterized in that: the working method of the intelligent housekeeper system based on the cloud computing comprises the following steps:
A. establishing a unified mathematical model of efficiency and stability of the intelligent housekeeper system;
the unified mathematical model for the efficiency and the stability is designed by introducing a double-chain cloud quantum method-based generation event chain and time chain population, the smaller the numerical value is, the higher the rescheduling efficiency and the stability are, and the unified mathematical model for the efficiency and the stability is designed as follows:
in the formula: fnRepresents the management completion time of the event n to be managed; bnRepresents the time of responding to the event n to be managed; DLnA completion time window representing an event n to be managed; t'nmAnd tnmRespectively representing the time when the module m of the intelligent housekeeper system starts to manage the event n to be managed in rescheduling and initial scheduling; PF () represents a deviation penalty value function; RT represents the current management time; n isiRepresenting the total number of events that the user needs to complete at the current moment; m represents the serial number of the module, namely, the natural serial numbers are used for respectively representing each module in the system in sequence;
B. the method comprises the following steps of updating data stored in a smart housekeeper system in real time, carrying out data cleaning and cluster analysis processing according to the scale and type of the data, and in a sales finance module, a settlement module and a data report query module, in order to obtain an optimal solution of data cleaning and data cluster analysis and improve the convergence speed and the optimal cost variable of the optimal solution, wherein the cross operator obtaining step based on a cloud self-adaptive genetic algorithm comprises the following steps:
b1, respectively representing two individuals participating in the crossover operation as father IFAnd parent body IM；
B2 from [1, 10]Selecting two random integers q within the range1And q is2Generating two sub-individuals on the basis of the two sub-individuals;
b3, production IDFor IDProduct list lambda ofDBefore q thereof1The position is formed by a mother individual IMFront q of1One position is determined by the lambda of the parentFDetermining;
b4 for IDService resource allocation list NDBefore q thereof2The position is formed by a mother individual IMFront q of2One position is determined, and other positions are determined by N of fatherFDetermining; similarly, the father and the mother interact to generate another child IT；
B5, exchange and plug-in for the list of products: the exchange type is different products of the exchange product list, if the relation constraint condition is not satisfied after the exchange, the original position is changed back, and the next exchange is carried out; insert means that the last position lc in the list of all pre-products of the mutation operator is calculated first1And the foremost position lc of all subsequent products in the list2Secondly at lc1And lc2Randomly selecting a position lc, and inserting the operator into the position of lc; the individual variation step is to firstly perform variation on each product j of the product list lambda in the individual IiCarrying out mutation according to the probability, and carrying out position transformation of an operator according to an insertion type mutation mode; each administrator assignment to the list of service resources in individual I
Randomly selecting N for the product corresponding to the positioniDistributing services meeting the requirements; the cloud cross probability and the mutation probability are generated by the Y-condition cloud generator and the forward cloud generator,generating a crossover operator and a mutation operator by an X conditional cloud generator; lambda is an event list satisfying the time sequence constraint and arranging all management tasks; n is a management module distribution list which represents a vector formed by distribution modes corresponding to each event of the event list, i represents the serial number of the managed event, and lcRepresenting the position of operator insertion;
C. the processing result is fed back and stored through analysis and is provided to the client with predictable reference information: the predictable reference information comprises expense flow, purchasing and consumption interest point prediction and payment category tendency, the selection of searching for an optimal solution is realized, a settlement module and an express delivery manager response module obtain a non-dominated optimal solution through cloud computing, and the optimal solution obtained by the two modules adopts an AHP hierarchical method, namely, a total target layer, a sub-target layer and a scheme layer are included from top to bottom; the total target layer is a total target for solving the real-time management problem, the sub-target layers comprise sub-targets of minimizing total cost, minimizing operation time and maximizing customer satisfaction, and the scheme layer is designed based on a set of non-dominated solutions taken by a cloud computing method; the method comprises the following steps of solving an optimal solution of the intelligent housekeeper system by a cloud self-adaptive genetic algorithm based on an AHP strategy, wherein each individual corresponds to a group of solutions of the settlement module and the express delivery manager response module:
c1, randomly generating x individuals to form an initial population, and initializing pcrAnd pct；pcrRepresenting cloud crossover operators, pctRepresenting a cloud mutation operator;
c2, solving the fitness of each individual, and adaptively adjusting pcrAnd pct；
C3, selecting operation is carried out by adopting an optimal retention strategy;
c4, searching fitness value, judging whether there is individual update, if there is no update in 30 continuous generations, doubling the population number;
c5, judging whether the optimal solution is obtained, if so, outputting the optimal individual, otherwise, executing
Step C2;
D. after data management and tendency judgment of a user tend to be stable, data management and prediction functions are completed, in a settlement module and an express manager response module, in order to optimize real-time prediction response time of a smart housekeeper system, an optimal solution is obtained by adopting a cloud computing-based method, and the method specifically comprises the following steps:
d1, initializing a population, and carrying out chromosome coding of a cloud genetic algorithm to represent events to be responded in the queue; the population is a to-be-managed event set of a user;
d2, designing a fitness evaluation function with an expression of fit (x) ═ 1/z (x), z (x) representing an individual objective function value, the smaller the function value the more excellent the individual is;
d3, selecting individuals for the population by using the optimal individual retention strategy and fitness proportion selection to determine the range of the individuals entering the next generation of the population;
d4 generation of crossover operator p by X condition generator of cloud modelcrAnd performing cross operation on the parent individuals; adopting a double-crossing mode, placing the gene region between the crossing points at the head of the offspring individuals, simultaneously removing the same codes in the parent individuals, and copying other codes to the offspring in sequence; if the child individual exceeds the constraint condition, moving the position 0 for adjustment;
d5 generation of mutation operator p by X condition generator of cloud modelctAnd carrying out interchange mutation operation on individuals; random selection of r from gene codes of variant individuals1And r2Two codes, r1And r2The selected codes are exchanged for non-0 natural numbers, so that new individuals are generated;
d6, reconstructing a new population by selecting k excellent individuals of the parent population and k individuals of the offspring population to be combined, thereby enlarging the population size and increasing the search space; k next generation populations are obtained again through related operations of a genetic algorithm; the operation of the step can lead the excellent genes of the population to be better preserved and obtain better feasible solution and optimal solution;
d7, updating the quantum gate;
d8, judging whether the stopping condition is met, if not, jumping to the step D3, otherwise, stopping the operation of the algorithm.
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| JP2003316790A (en) * | 2002-04-26 | 2003-11-07 | Bellsystem 24 Inc | Life/leisure information retrieval system based on weather information |
| CN102360483A (en) * | 2011-10-06 | 2012-02-22 | 蒙自聚纶网络有限公司 | Navigation type B2C (business to customer) network shopping platform for gathering electronic business official websites with self establishment of brands |
| CN102819789A (en) * | 2012-07-26 | 2012-12-12 | 深圳市互联通科技有限公司 | Electronic commerce supply chain management system |
| CN104851001A (en) * | 2015-05-08 | 2015-08-19 | 苏州众里数码科技有限公司 | Method for product tracing and counterfeiting prevention |
| CN106254543A (en) * | 2016-09-27 | 2016-12-21 | 盐城工学院 | Distributed interconnection Network and Finance Network based on cloud computing framework borrows method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003316790A (en) * | 2002-04-26 | 2003-11-07 | Bellsystem 24 Inc | Life/leisure information retrieval system based on weather information |
| CN102360483A (en) * | 2011-10-06 | 2012-02-22 | 蒙自聚纶网络有限公司 | Navigation type B2C (business to customer) network shopping platform for gathering electronic business official websites with self establishment of brands |
| CN102819789A (en) * | 2012-07-26 | 2012-12-12 | 深圳市互联通科技有限公司 | Electronic commerce supply chain management system |
| CN104851001A (en) * | 2015-05-08 | 2015-08-19 | 苏州众里数码科技有限公司 | Method for product tracing and counterfeiting prevention |
| CN106254543A (en) * | 2016-09-27 | 2016-12-21 | 盐城工学院 | Distributed interconnection Network and Finance Network based on cloud computing framework borrows method and system |
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