CN114048020A - Guest room management system based on big data - Google Patents
Guest room management system based on big data Download PDFInfo
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- CN114048020A CN114048020A CN202111104444.6A CN202111104444A CN114048020A CN 114048020 A CN114048020 A CN 114048020A CN 202111104444 A CN202111104444 A CN 202111104444A CN 114048020 A CN114048020 A CN 114048020A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1448—Management of the data involved in backup or backup restore
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1458—Management of the backup or restore process
- G06F11/1464—Management of the backup or restore process for networked environments
Abstract
The invention provides a guest room management system based on big data, which comprises: the system comprises a plurality of hardware terminals, a plurality of clients, an application server, a data storage server, a data backup server and a web server; the application server receives data collected by the hardware terminals, transmits the data to the data storage server, and simultaneously transmits monitoring and control data information returned by the Web server to the hardware terminals in real time; the data storage server stores the data uploaded by the application server in real time and uploads the data to the data backup server for data backup; the data backup server is used for realizing the backup function of big data in the management system and supporting the query of historical data; and the web server is used for making a data query request to the data backup server through the web server when the client accesses the management system, and displaying the fed-back data on the client.
Description
Technical Field
The invention relates to the technical field of guest room management, in particular to a guest room management system based on big data.
Background
With the emergence and rapid development of the internet of things, devices and terminals in various industries and fields can be rapidly converged together through an access network, which indicates that a big data era has come. Big data is a product in the continuous development process of the information age, under the high-speed development of social science and technology, the big data research of the current society is deeply covered in all the fields of the whole world and is closely related to everyone, and the flood of the big data is rolled into the whole world. Big data is continuously generated from complex and diversified sensing equipment and application systems of the internet of things, and will continue to grow rapidly in a faster, more diversified and more complex manner in the future.
The Internet of things big data industry for hotel room management has a good foundation, faces a difficult development opportunity, does not have a universal open service platform at present, and is a very difficult and serious problem in the prior art. Large data resources are difficult to circulate, the management capability is not strong, and the value of the data is difficult to realize to the maximum extent; meanwhile, the management system is not open enough, the access compatibility of the guest room terminal equipment is poor, and data service accidents frequently occur.
For example, in the prior art, patent document CN107886611A discloses a room security management system for a self-service hotel, which includes a mobile terminal, a smart lock, and a server: the intelligent lock comprises two-dimension code recognition equipment and a communication module; the server comprises a database a and a database b; the database a stores guest room booking information; the database b stores the position and price information of the guest room; and the server extracts the vacant guest room information stored in the database b according to the guest room screening performed by the mobile terminal and feeds the vacant guest room information back to the mobile terminal. However, with the continuous improvement of the requirements of the people living in, the technical scheme has the defects of insufficient support capability of the management system and low working efficiency.
Patent document CN212433841U discloses a wisdom hotel service system based on cloud ware, including the terminal of independently living in who has room card transceiver, a plurality of customer terminals, management terminal and cloud ware, a plurality of customer terminals and management terminal all with cloud ware communication connection, customer terminal includes control processing module, and all with control processing module connected activation module, network communication module, be used for showing hotel service item's hotel service module and be used for showing the merchant service module of cooperation merchant's service item, cloud ware includes control module, authentication module, charging module and storage module, control module respectively with a plurality of customer terminals and management terminal communication connection, authentication module, charging module and storage module all are connected with control module. However, the technical scheme has the defects of difficult circulation of large data resources, weak management capability and difficult realization of the value of data to the maximum extent.
Disclosure of Invention
In order to solve the technical problem, the invention provides a guest room management system based on big data, which comprises: the system comprises a plurality of hardware terminals, a plurality of clients, an application server, a data storage server, a data backup server and a web server;
the application server receives data collected by the hardware terminals, transmits the data to the data storage server, and simultaneously transmits monitoring and control data information returned by the Web server to the hardware terminals in real time;
the data storage server stores the data uploaded by the application server in real time and uploads the data to the data backup server for data backup;
the data backup server is used for realizing the backup function of big data in the management system and supporting the query of historical data;
and the web server is used for providing a data query request to the data backup server through the web server when the client accesses the management system, and displaying the feedback data on the client.
Further, the web server comprises a load balancing server and a plurality of sub servers; and the web server adopts a weighted polling algorithm, and the load balancing server distributes the requests sent by the plurality of clients to each sub-server.
Further, the weighted polling algorithm specifically includes the following steps:
step 1: collecting values of different sub-server load factors, the load factors including: the utilization rate L (ci) of the CPU of the sub-server, the utilization rate L (mi) of the memory of the sub-server and the utilization rate L (ni) of the bandwidth of the sub-server;
step 2: comparing each utilization rate of each sub-server with a corresponding utilization rate threshold value, wherein the utilization rate threshold value of a CPU of the sub-server is Y (ci), the utilization rate threshold value of a memory of the sub-server is Y (mi), and the utilization rate threshold value of the bandwidth of the sub-server is Y (ni);
if L (ci) > Y (ci), L (mi) > Y (mi) or L (ni) > Y (ni), setting the weight of the sub-server to 0, and no longer allocating tasks to the sub-server within the time period T, otherwise, if L (ci) < Y (ci), L (mi) < Y (mi) and L (ni) < Y (ni), calculating the real-time load value M (i) of the sub-server according to the weight vector T (i) of each load factor of each sub-server;
and step 3: calculating a sub-server processing capacity extreme value N (i) and a sub-server real-time load rate R (i);
and 4, step 4: comparing the value of each sub-server real-time load rate R (i) with the threshold value Y (i) of the sub-server,
if R (i) > Y (i), setting the weight of the sub-server to be 0, otherwise, carrying out the next step;
and 5: calculating a weight C (i) of the sub-server, and calculating a weight CW (i) really distributed by the sub-server according to the weight W (i) set for the sub-server;
step 6: calculating the ratio of the real distribution weight CW (i) among the sub-servers, and polling the distribution request task according to the ratio of the real distribution weight CW (i).
Further, the value of the sub-server real-time load m (i) = l (i) ×
；
Wherein, t (i) = [ t (ci), t (mi), t (ni) ]; l (i) = [ l (ci), l (mi), l (ni) ]; t (ci), T (mi) and T (ni) respectively represent weights of the CPU, the memory and the bandwidth of the sub-server; l (ci), L (mi), L (ni) respectively represent the utilization rates of the CPU, the memory and the bandwidth of the sub-server;
sub-server processing capacity extremum N(i)=P(i)*
Wherein, p (i) = [ p (ci), p (mi), p (ni) ], p (ci), p (mi), p (ni) respectively represent the CPU processing speed, the memory size, and the bandwidth throughput of the sub-server;
the sub-server real-time load rate R (i) = M (i)/N (i);
the calculation weight C (i) = (R-R (i))/R of the sub-server; wherein R =
N represents the number of sub-servers;
weight cw (i) = c (i) actually allocated by sub-server
。
Furthermore, the management system adopts a data distribution storage backup mechanism, integrates three processes of data processing of the application server, data storage of the data storage server and data backup of the data backup server, the application server is used as a data acquisition node, the data storage server is used as a data merging node, and the data backup server is used as a data backup node.
Furthermore, the application server comprises a data acquisition module and a data sending module, the data acquisition module acquires the latest data generated by the plurality of hardware terminals, the data format comprises hardware terminal numbers and sending dates, and the data sending module sends the latest data to the data storage server.
Further, the data storage server comprises a memory management module and a plurality of buffer areas; the memory management module distributes the received data to a plurality of buffer areas according to the size of the received data, periodically executes data merging operation, sends the merged data to a data backup server, and releases the space of the corresponding plurality of buffer areas; and the data backup server merges the received merged data with the data in the current historical database to finally generate a new historical database.
Further, the data backup server stores data in a mode of combining a hierarchical table and a sequence table, the type information of the data is stored in the first hierarchical structure, all hardware terminal device information under each type information is stored in the second hierarchical structure, and each type of hardware terminal device information is sorted according to the time of the hardware terminal device for transmitting the data through the sequence table structure.
Further, the format of the data set stored in the data backup server is { type, device, time }; when the data is queried, the data backup server filters invalid data in the query request by using a data filtering mechanism.
Further, when the storage capacity of the data backup server is insufficient, the management system elastically increases the data backup server to expand the capacity, and a plurality of data backup servers are arranged to form a distributed backup system to backup the historical database together and serve the query requests of the plurality of clients for the historical data.
The big data-based guest room management system not only adopts a weighted polling algorithm to distribute the load service of the sub-servers based on the weight proportion of each sub-server to relieve the pressure on WEB server transmission, but also maximally averages the load balanced distribution, so that the situation that the connected requests are simultaneously distributed to the same sub-server can not occur, and further adopts a data distribution storage backup mechanism and a data filtering mechanism to support the requirements of the guest room management system on transaction processing and data storage, thereby greatly increasing the throughput of data and ensuring data transmission.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of the overall structure of a big data-based guest room management system according to the present invention;
FIG. 2 is a schematic diagram of a weighted round robin algorithm performed on a WEB server according to the present invention;
FIG. 3 is a flow chart of the weighted round robin algorithm of the present invention;
FIG. 4 is a schematic diagram of a data distributed storage backup mechanism according to the present invention;
FIG. 5 is a diagram illustrating a data backup server storing data using a hierarchical table and a sequence table according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the drawings of the embodiments of the present invention, in order to better and more clearly describe the working principle of each element in the intelligent hotel system, the connection relationship of each part in the device is shown, only the relative position relationship between each element is clearly distinguished, and the limitation on the signal transmission direction, the connection sequence, and the size, the dimension and the shape of each part structure in the element or structure cannot be formed.
As shown in fig. 1, a schematic diagram of an overall structure of a big data-based guest room management system according to the present invention includes: a plurality of hardware terminals 1, 2 … … n; a plurality of clients 1, 2 … … n; an application server; a data storage server; a data backup server and a web server.
The hardware terminals 1 and 2 … … n are intelligent terminals such as sensors and controllers provided in guest rooms. For example, an air conditioner sensing and control terminal, a temperature and humidity monitoring terminal, an intelligent door lock terminal, a television, a telephone communication terminal and the like.
The plurality of clients 1 and 2 … … n comprise browser software and APP installed on the mobile communication device and the PC, users of the clients access the Web server through the browser software or the APP to obtain data display of a plurality of hardware terminals in the management system, and monitor and control the state of the intelligent terminal through the browser software and the APP.
The application server maintains the persistence of data transmission with the hardware terminals 1 and 2 … … n, and is responsible for receiving data transmitted to the data storage server by the hardware terminals 1 and 2 … … n, and also needs to be responsible for transmitting monitoring and control data information returned by the Web server to the hardware terminals in real time.
And the data storage server provides a large data real-time storage function in the management system, and the data of the hardware terminals 1 and 2 … … n uploaded by the application server are stored in the data storage server.
The data backup server is used for realizing the backup function of the big data in the management system, keeping the data consistent with the data storage server, performing system backup on the big data in real time, ensuring the integrity and the safety of the data, and supporting the query of the data by the data backup server relative to the data operation stored in the data storage server, thereby effectively reducing the operation pressure of the data storage server to a certain extent. And the results filtered by the web server according to the request made by the client are also stored in the data backup server in real time.
And the web server is used for providing a portal website and an APP software portal for the plurality of clients 1 and 2 … … n, and when the clients access the management system, a user requests the data from the data backup server through the web server and then displays the fed data on the clients. The web server provides a direct browsing interface for managing data presentation of the plurality of hardware terminals 1, 2 … … n in the system, and also provides an interface for facilitating the client to reversely control the plurality of hardware terminals 1, 2 … … n.
The invention performs a weighted round robin algorithm on the web server to relieve the pressure on the web server transmission. Fig. 2 is a schematic diagram of the weighted polling algorithm performed on the web server according to the present invention. Because the pressure load of a single server is too heavy in the data transmission process, the web server is specifically set to comprise a load balancing server and a plurality of sub servers 1, 2 … … n. The requests of multiple clients 1, 2 … … n are processed through a weighted round robin algorithm in the web server and then distributed by the load balancing server to the various sub-servers in turn. The load balancing server is used for receiving and sharing the requests of the multiple clients 1 and 2 … … n, the load balancing server is connected with the multiple sub servers 1 and 2 … … n and decides to forward and distribute the requests of the multiple client terminals 1 and 2 … … n to different sub servers 1 and 2 … … n according to the result of the weighted polling algorithm, the data transmission pressure of the web server is effectively relieved, the data transmission efficiency is ensured, the mode is low in cost, simple in configuration and flexible to use, the data throughput is greatly increased, and the data transmission is ensured.
Specifically, the real-time load factor of the sub-server is dynamic data, which is obtained by multiplying the utilization rate of the load factor in the sub-server by the corresponding weight. Within a certain time period T, preferably T =10s, the load balancing server collects the values of the load factors of the sub-servers at a time, the values of the load factors are dynamically changed, according to the collected values of the load factors, the values of the load factors are firstly compared with the correspondingly set threshold, and in the case that the collected values of the load factors are smaller than the correspondingly set threshold, the processing capacity extreme value of each sub-server and the real-time load value of each sub-server are calculated, and the two values are compared to obtain a value, which is called a load rate. And if the load rate is smaller than the threshold value of the server, calculating the weight value of the current sub-server, and performing polling processing request tasks according to the weight value.
And calculating the weight ratio of each sub-server according to the calculated weight of the sub-server, wherein the weight ratio can well represent the performance of the current sub-server. The weight ratio does not contain the sub-servers with the load ratios larger than or equal to the threshold of the server, and the weight of the sub-servers is determined according to the ratio, so that the normal operation of the sub-servers can be ensured.
When the plurality of clients 1, 2 … … n send requests to the server, the requests are sent to the load balancing server first, the load balancing server feeds back load states, namely information of load factors, to the sub-servers in real time, then the load balancing server distributes the requests to different sub-servers 1, 2 … … n according to a weight polling algorithm, and after the different sub-servers 1, 2 … … n process the requests, the processing results are fed back to the plurality of clients 1, 2 … … n through the load balancing server.
As shown in fig. 3, the flow of the weighted round robin algorithm is as follows:
step 1: collecting the values of the load factors of the different sub servers 1 and 2 … … n, the values of the load factors involved in this embodiment are: utilization rate L (ci) of CPU of sub-server, utilization rate L (mi) of memory of sub-server, and utilization rate L (ni) of bandwidth of sub-server.
Step 2: comparing the value of each load factor of each sub-server with the corresponding threshold value, namely, the utilization rate threshold value Y (ci) of the CPU of the sub-server, the utilization rate threshold value Y (mi) of the memory of the sub-server and the utilization rate threshold value Y (ni) of the bandwidth of the sub-server;
if L (ci) > Y (ci), L (mi) > Y (mi) or L (ni) > Y (ni) exists, setting the weight of the sub-server to 0, and no longer allocating tasks to the sub-server within the time period T, otherwise, when L (ci) < Y (ci), L (mi) < Y (mi) and L (ni) < Y (ni), calculating the real-time load factor value M (i) of the sub-server according to the weight vector T (i) of the load factors;
M(i)=L(i)*
；
T(i)=[T(ci),T(mi),T(ni)]；
L(i)=[L(ci),L(mi),L(ni)]；
wherein, T (ci), T (mi), T (ni) respectively represent the weight of CPU, memory and bandwidth of each sub-server, and the sum of the weight and the weight is 1; l (ci), L (mi), L (ni) respectively represent the utilization rates of the CPU, the memory and the bandwidth of the sub-servers.
And step 3: calculating a sub-server processing capacity extreme value N (i) and a sub-server real-time load rate R (i);
N(i)=P(i)*
，R(i)=M(i)/N(i)；
wherein, p (i) = [ p (ci), p (mi), p (ni) ], p (ci), p (mi), and p (ni) respectively represent the CPU processing speed, the memory size, and the bandwidth throughput of the sub-server.
And 4, step 4: comparing the value of each sub-server real-time load rate r (i) with a sub-server threshold value y (i), wherein y (i) = [ y (ci), y (mi), y (ni) ];
if R (i) > Y (i), setting the weight of the sub-server to be 0, otherwise, carrying out the next step;
and 5: calculating the weight value C (i) of the sub-server,
C(i)=（R-R(i)）/R；
wherein R =
And n represents the number of child servers.
Calculating the weight CW (i) really distributed by the sub server according to the weight W (i) set for the sub server:
CW(i)=C(i)*
；
step 6: calculating the ratio of the real distribution weight CW (i) among the sub-servers, and polling the distribution request task according to the ratio of the real distribution weight CW (i).
The implementation of the weighted round robin algorithm is all that is done. The addition of the weighted polling algorithm in the web server not only can distribute the request services of a plurality of clients based on the weight ratio of each sub-server, but also can maximally average the load balance distribution of each sub-server, so that the situation that the connected requests are simultaneously distributed to the same sub-server can be avoided.
In order to support the requirements of the guest room management system in terms of transaction processing and data storage, in a preferred embodiment, as shown in fig. 4, the present invention employs a data distribution storage backup mechanism, which integrates three processes of data processing of an application server, data storage of a data storage server, and data backup query of a data backup server, wherein the application server serves as a data acquisition node, the data storage server serves as a data merge node, and the data backup server serves as a data backup node.
Specifically, the application server includes a data acquisition module and a data sending module, the data acquisition module acquires the latest data generated by the plurality of hardware terminals 1 and 2 … … n, and the data format is { 'ZD 001': '2018.12.01' }, 'ZD 001' represents the number of the hardware terminal, and '2018.12.01' represents the date of sending data, after the data communication module receives the data of the hardware terminal, the last communication time of the intelligent terminal is refreshed at regular time, and if no new data or connection request sent from the hardware terminal is obtained within a specified certain time period, the hardware terminal is considered to be in an offline state. Therefore, in order to ensure that the hardware terminals can keep online, the hardware terminals always send connection requests to the application server at a fixed frequency.
The data sending module of the application server sends the acquired data to the data storage server, the data storage server comprises a memory management module and a plurality of buffer areas, and the memory management module distributes the data to the plurality of buffer areas according to the size of the received data. In order to cope with the limitation of the memory capacity of the data storage server, the memory management module periodically executes the data merging operation. When more and more data are accumulated in the memories of the buffer areas, the data merging operation merges all the data on the buffer areas with the historical data of the current database stored on the data backup server, and finally a new historical database is generated on the data backup server. After merging, the memory management module deletes all the merged data and releases the space of the corresponding plurality of buffer areas. Therefore, the data stored in the buffer of the data storage server is a small part relative to the whole database, and for the query requests of different clients, data search needs to be performed in the data backup server.
The data backup server stores data by combining a hierarchy table and a sequence table, and the storage structure is schematically shown in fig. 5. The type information of the data stored in the first hierarchical structure, for example, the type information shown in fig. 5, is divided into a sensor and a controller, and the second hierarchical structure stores all hardware terminal devices under each type information, for example, as shown in fig. 5, the sensor may be divided into a temperature and humidity sensor and an RFID sensor; the controller can be divided into an air conditioner controller and an intelligent door lock terminal; each value of each hardware terminal device is stored with a sequence list structure, and the sequence list structure is sorted according to the time information of data transmission of the hardware device. Thus, the format of the data set stored in the data backup server is { type, device, time }. when the web server requests to perform data query in the data backup server, the data backup server filters invalid data access in the query request process by using a data filtering mechanism, so that the data retrieval efficiency is further improved. For example, three objects obj1, obj2 and obj3 are stored in the data backup server, and their data formats are { 'a', 'ZD 001', '2018.12.01', { 'B', 'ZD 002', '2017.12.01', { 'C', 'ZD 003', '2016.12.01' }. When a web server sends a data query request, for example, when a temperature and humidity sensor of a certain guest room is queried for sensing data in 12 months in 2017, the data backup server automatically extracts the type of the query request as sensor B, and the hardware terminal device temperature and humidity sensor number of the certain guest room is ZD002, so that only data with data formats { 'B', 'WD 002', '2017.12' } need to be found, and other data which do not conform to the data format are filtered out, so that the efficiency of executing filtering operation is very high.
In a preferred embodiment, a plurality of data backup servers can be arranged to jointly manage consistent database historical data and simultaneously serve query requests for the historical data, a distributed backup system is formed, and when the storage capacity is insufficient, the management system can flexibly increase the data backup servers to expand the capacity.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, 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 (10)
1. A big data based guest room management system, comprising: the system comprises a plurality of hardware terminals, a plurality of clients, an application server, a data storage server, a data backup server and a web server;
the application server receives data collected by the hardware terminals, transmits the data to the data storage server, and simultaneously transmits monitoring and control data information returned by the Web server to the hardware terminals in real time;
the data storage server stores the data uploaded by the application server in real time and uploads the data to the data backup server for data backup;
the data backup server is used for realizing the backup function of big data in the management system and supporting the query of historical data;
and the web server is used for providing a data query request to the data backup server through the web server when the client accesses the management system, and displaying the feedback data on the client.
2. The big data based room management system of claim 1, wherein the web server comprises a load balancing server and a plurality of sub-servers; and the web server adopts a weighted polling algorithm, and the load balancing server distributes the requests sent by the plurality of clients to each sub-server.
3. The big-data-based guest room management system according to claim 2, wherein the weighted polling algorithm specifically comprises the following steps:
step 1: collecting values of different sub-server load factors, the load factors including: the utilization rate L (ci) of the CPU of the sub-server, the utilization rate L (mi) of the memory of the sub-server and the utilization rate L (ni) of the bandwidth of the sub-server;
step 2: comparing each utilization rate of each sub-server with a corresponding utilization rate threshold value, wherein the utilization rate threshold value of a CPU of the sub-server is Y (ci), the utilization rate threshold value of a memory of the sub-server is Y (mi), and the utilization rate threshold value of the bandwidth of the sub-server is Y (ni);
if L (ci) > Y (ci), L (mi) > Y (mi) or L (ni) > Y (ni), setting the weight of the sub-server to 0, and no longer allocating tasks to the sub-server within the time period T, otherwise, if L (ci) < Y (ci), L (mi) < Y (mi) and L (ni) < Y (ni), calculating the real-time load value M (i) of the sub-server according to the weight vector T (i) of each load factor of each sub-server;
and step 3: calculating a sub-server processing capacity extreme value N (i) and a sub-server real-time load rate R (i);
and 4, step 4: comparing the value of each sub-server real-time load rate R (i) with the threshold value Y (i) of the sub-server,
if R (i) > Y (i), setting the weight of the sub-server to be 0, otherwise, carrying out the next step;
and 5: calculating a weight C (i) of the sub-server, and calculating a weight CW (i) really distributed by the sub-server according to the weight W (i) set for the sub-server;
step 6: calculating the ratio of the real distribution weight CW (i) among the sub-servers, and polling the distribution request task according to the ratio of the real distribution weight CW (i).
4. The big-data-based room management system of claim 3,
value of sub-server real-time load m (i) = l (i) ×)
；
Wherein, t (i) = [ t (ci), t (mi), t (ni) ]; l (i) = [ l (ci), l (mi), l (ni) ]; t (ci), T (mi) and T (ni) respectively represent weights of the CPU, the memory and the bandwidth of the sub-server; l (ci), L (mi), L (ni) respectively represent the utilization rates of the CPU, the memory and the bandwidth of the sub-server;
processing capacity limit n (i) = p (i) ×) of sub-servers
Wherein, p (i) = [ p (ci), p (mi), p (ni) ], p (ci), p (mi), p (ni) respectively represent the CPU processing speed, the memory size, and the bandwidth throughput of the sub-server;
the sub-server real-time load rate R (i) = M (i)/N (i);
the calculation weight C (i) = (R-R (i))/R of the sub-server; wherein R =
N represents the number of sub-servers;
weight cw (i) = c (i) actually allocated by sub-server
。
5. The big-data-based guest room management system according to claim 1, wherein the management system adopts a data distribution storage backup mechanism, and integrates three processes of data processing of an application server, data storage of a data storage server and data backup of a data backup server, the application server is used as a data acquisition node, the data storage server is used as a data merge node, and the data backup server is used as a data backup node.
6. The big data based guest room management system according to claim 5, wherein the application server comprises a data collection module and a data transmission module, the data collection module collects the latest data generated by the plurality of hardware terminals, the data format comprises hardware terminal numbers and transmission dates, and the data transmission module transmits the latest data to the data storage server.
7. The big-data-based guest room management system according to claim 5, wherein the data storage server comprises a memory management module and a plurality of buffers; the memory management module distributes the received data to a plurality of buffer areas according to the size of the received data, periodically executes data merging operation, sends the merged data to a data backup server, and releases the space of the corresponding plurality of buffer areas; and the data backup server merges the received merged data with the data in the current historical database to finally generate a new historical database.
8. The big-data-based guest room management system according to claim 5, wherein the data backup server stores data by combining a hierarchical table and a sequence table, the first hierarchical structure stores type information of the data, the second hierarchical structure stores all hardware terminal device information under each type of information, and each type of hardware terminal device information is sorted according to the time of data transmission of the hardware terminal device through the sequence table structure.
9. The big-data-based guest room management system of claim 8, wherein the format of the dataset stored in the data backup server is { type, device, time }; when the data is queried, the data backup server filters invalid data in the query request by using a data filtering mechanism.
10. The big-data-based guest room management system according to claim 1, wherein when the storage capacity of the data backup server is insufficient, the management system flexibly increases the capacity of the data backup server, and a plurality of data backup servers are arranged to form a distributed backup system to backup a historical database together to serve the query requests of the plurality of clients for the historical data.
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