CN113570261A - Government affair management system based on cloud computing and smart city - Google Patents
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Abstract
The invention relates to a government affair management system based on cloud computing and smart cities, which comprises: the system comprises a government affair management terminal and a government affair management platform, wherein the government affair management platform is in communication connection with the government affair management terminal; the government affair management platform comprises a data analysis module, a parameter separation module, a service prediction module and a database, wherein communication connection is formed among the modules. And the government affair management terminal sends a government affair management request to the government affair management platform. The government affair management platform carries out data analysis on historical business handling data of the target business to obtain historical business demand data, an initial business prediction function is established according to the historical business demand data, then de-perturbation and discretization are carried out on the initial business prediction function to obtain a target business prediction function, and finally business demand quantity of each day in a target prediction period is predicted according to the target business prediction function to generate business demand data which are sent to a government affair management terminal.
Description
Technical Field
The invention relates to the field of cloud computing and smart cities, in particular to a government affair management system based on the cloud computing and the smart cities.
Background
The intelligent government affairs are advanced stages of electronic government affair development, internal relations of governments, relations of governments and citizens are reformed by means of modern intelligent information communication technology, government management, supervision and public service providing modes are innovated, and the purposes of improving administrative efficiency and providing better public services are finally achieved. The intelligent government affairs are used as a new, intelligent and modern government management mode, and are beneficial to promoting the modernization transformation of management and service; the method is beneficial to improving the scientific decision-making capability of government departments; is beneficial to the construction of people-oriented service type governments. Wisdom government affairs is a new governance mode in big data era, and the requirement changes the traditional governance mode, and government management and public service supply are not centered on governments, and aim at coordinating with social environment and developing, improve efficiency and efficiency of governments, satisfy citizen's demand, and management mode and service mode develop towards wisdom.
In the existing government affair service mode, certain problems and space are still existed for handling related business. For example, the number of windows and the number of staff members for handling the government affairs are fixed and not adaptively arranged according to the actual business requirements. Therefore, when the number of people transacting the business is large, the number of workers is insufficient, so that the waiting time of a client transacting the business is too long, and the experience of the client is reduced. When the number of business handling personnel is small, most of the working time of the business handling personnel is in an idle state, and human resources are wasted.
Disclosure of Invention
In order to solve the above problems, the present invention provides a cloud computing and smart city based government affairs management system, comprising: the system comprises a government affair management terminal and a government affair management platform, wherein the government affair management platform is in communication connection with the government affair management terminal; the government affair management platform comprises a data analysis module, a parameter separation module, a service prediction module and a database, and communication connection is formed among the modules;
the government affair management terminal sends a government affair management request to the government affair management platform; the government affair management request comprises a business code and a target prediction period;
the data analysis module acquires historical service handling data of a target service from a database according to the service code and performs data analysis on the historical service handling data to obtain historical service requirement data;
the data analysis module extracts the service requirements of the historical service requirement data to obtain a first service requirement sequence; the first service demand sequence comprises the daily service demand of the target service in a historical statistical period;
the data analysis module acquires all traffic interference factors of the target service, acquires the correlation coefficient of each traffic interference factor and the target service, and updates the first service demand sequence according to the correlation coefficient of each traffic interference factor and the target service to obtain a service disturbance sequence;
the parameter separation module performs first parameter separation on the service disturbance sequence to obtain a first separation component and a first separation residual quantity, performs second parameter separation on the first separation residual quantity to obtain a second separation component and a second separation residual quantity, and performs third parameter separation on the second separation residual quantity to obtain a third separation component and a third separation residual quantity; performing iterative operations on the step until the separation residue cannot be separated continuously;
the parameter separation module linearly sums the separation component of each parameter separation and the separation residual quantity of the last parameter separation to obtain a parameter separation sequence;
the service prediction module generates a second service demand sequence according to the parameter separation sequence, establishes an initial service prediction function according to the second service demand sequence and the traffic interference factor, and then performs de-perturbation and discretization on the initial service prediction function to obtain a target service prediction function;
and the service prediction module predicts the daily service demand of the target service in the target prediction period according to the target service prediction function to obtain predicted service demand data and sends the predicted service demand data to the government affair management terminal.
According to a preferred embodiment, the data analysis module performing service requirement extraction on the historical service requirement data to obtain a first service requirement sequence includes:
the data analysis module acquires the daily service demand of the target service in a historical statistical period according to the historical service demand data; the service demand is the number of people who have the target service to handle the demand;
the data analysis module carries out ascending arrangement on the business demand of each day in the historical statistical period according to the time sequence to obtain a first business demand sequence; the first traffic demand sequence includes a traffic demand of the target traffic per day over a historical statistics period.
According to a preferred embodiment, the data analysis module performing data analysis according to the historical service transaction data to obtain historical service demand data includes:
the data analysis module acquires expected traffic, actual traffic and traffic influence classification data of the target service in a historical statistical period each day according to historical service transaction data, and calculates total traffic difference of the target service in the historical statistical period each day according to the expected traffic and the actual traffic;
the data analysis module acquires a first traffic difference and a second traffic difference according to the total traffic difference and the traffic influence classification data of each day in the historical statistical period, and acquires the traffic demand of each day in the historical statistical period according to the total traffic difference, the first traffic difference and the second traffic difference.
According to a preferred embodiment, the data analysis module obtaining the first traffic difference amount and the second traffic difference amount according to the total traffic difference amount and the traffic influence classification data comprises:
the data analysis module acquires the business influence classification coefficient of each client stopping transacting the target business according to the business influence classification data; the business influence classification data comprises business influence classification coefficients of all clients stopping transacting the target business;
the data analysis module divides all the clients suspending transacting the target service into a first client and a second client according to the service influence classification coefficient of each client suspending transacting the target service;
the data analysis module respectively counts the number of the first customers and the number of the second customers to obtain the number of the first customers and the number of the second customers;
and the data analysis module performs data verification on the first client quantity and the second client quantity according to the total traffic difference quantity, and takes the first client quantity as the first traffic difference quantity and the second client quantity as the second traffic difference quantity when the first client quantity and the second client quantity pass the verification.
According to a preferred embodiment, the second service requirement sequence is:
D＝[d(0),d(1)…d(N-1)]
and D is the second service demand sequence, N is the number of days of the historical statistical period, and D (N-1) is the service demand of the (N-1) th day in the historical statistical period.
According to a preferred embodiment, the step of establishing, by the traffic prediction module, the initial traffic prediction function according to the second traffic demand sequence and the traffic interference factor includes:
wherein p (D | δ) is an initial service prediction function, δ is a traffic interference factor, D is a second service demand sequence, N is the number of days of the historical statistical period, t is a time period index, and D (t) is the traffic demand of the target service on the t-th day in the historical statistical period.
According to a preferred embodiment, the service prediction module performing de-perturbation and discretization on the initial service prediction function to obtain the target service prediction function includes:
wherein R is a target traffic prediction function, dsAs an actual value of the traffic demand, dpThe predicted value of the service demand is obtained according to the initial service prediction function, N is the number of days of the historical statistical period, xi adjustment coefficient, a is a weight parameter, and b is a bias parameter.
According to a preferred embodiment, the government administration terminal is a device having a calculation function, a storage function and a communication function for use by government administration staff, and includes: smart mobile phones, desktop computers, notebook computers, smart watches, and smart wearable devices.
According to a preferred embodiment, the service code is used for uniquely identifying a service; the business comprises house passing, marriage registration, company registration and operation permission; the traffic interference factor is a factor influencing traffic demand, and comprises time, regulation and news; the historical service transaction data comprises expected service volume, actual service volume and service influence classification data of the target service in a historical statistic period every day.
According to a preferred embodiment, the expected traffic is the number of queuing people of the target service; the actual business volume is the actual number of people handling the target business; the business influence classification data comprises business influence classification coefficients of all clients stopping transacting the target business; the business influence classification coefficient is used for representing the reason why the client suspends the handling of the target business, when the business influence classification coefficient is zero, the corresponding client suspends the handling of the target business due to the self reason, and when the business influence classification coefficient is one time, the corresponding client suspends the handling of the target business due to the reason of the business handling center.
According to a preferred embodiment, the total traffic difference is the total number of customers who discontinued handling the target traffic; the first business difference is the number of clients who stop transacting the target business due to the clients; the second traffic difference is the number of clients who stop transacting the target traffic due to the traffic transaction center.
The invention has the following beneficial effects: the method and the system predict the service demand of the target service in the appointed time period according to the collected historical service handling data of the target service, thereby helping government affair managers to make a reasonable personnel scheduling plan, fully utilizing human resources and avoiding the situation of human idle under the condition of improving user experience.
Drawings
Fig. 1 is a block diagram illustrating a cloud computing and smart city based government administration system according to the present invention;
fig. 2 is a schematic diagram of a server for implementing the government administration platform of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection.
According to the business prediction demand of the government affair managers, the business demand in the future appointed time period is automatically predicted, and corresponding business demand data are generated, so that the government affair managers can make reasonable personnel scheduling plans to properly adjust the number of the business handlers and the number of the business handling windows in the target prediction time period, the business handlers are adaptive to the corresponding business demand, and the human resources are fully utilized. Therefore, when the service demand is less, the number of corresponding service handling personnel is reduced, the idle rate of the personnel can be reduced, and the waste of human resources is avoided. The number of service handling personnel is increased when the service demand is more, the service handling efficiency can be improved, the waiting time for a client to handle the service is reduced, and the client experience degree is improved. Namely, the method and the system can help government affair managers to make reasonable personnel scheduling plans by predicting the business demands in the appointed time period, fully utilize human resources, and avoid the situation that human power is idle under the condition of improving user experience.
Referring to fig. 1, in one embodiment, a cloud computing and smart city based government administration system may include: the system comprises a government affair management terminal and a government affair management platform, wherein the government affair management platform is in communication connection with the government affair management terminal. The government affair management platform comprises a data analysis module, a parameter separation module, a service prediction module and a database, wherein communication connection is formed among the modules.
The government affair management terminal sends a government affair management request to the government affair management platform; the government affair management terminal is a device with a calculation function, a storage function and a communication function used by government affair management personnel, and comprises: smart mobile phones, desktop computers, notebook computers, smart watches, and smart wearable devices.
The data analysis module acquires historical service handling data of a target service from a database according to the service code and performs data analysis on the historical service handling data to obtain historical service requirement data;
the data analysis module extracts the service requirements of the historical service requirement data to obtain a first service requirement sequence; the first service demand sequence comprises the daily service demand of the target service in a historical statistical period;
the data analysis module acquires all traffic interference factors of the target service, acquires the correlation coefficient of each traffic interference factor and the target service, and updates the first service demand sequence according to the correlation coefficient of each traffic interference factor and the target service to obtain a service disturbance sequence;
the parameter separation module performs first parameter separation on the service disturbance sequence to obtain a first separation component and a first separation residual quantity, performs second parameter separation on the first separation residual quantity to obtain a second separation component and a second separation residual quantity, and performs third parameter separation on the second separation residual quantity to obtain a third separation component and a third separation residual quantity; performing iterative operations on the step until the separation residue cannot be separated continuously;
the parameter separation module linearly sums the separation component of each parameter separation and the separation residual quantity of the last parameter separation to obtain a parameter separation sequence;
the service prediction module generates a second service demand sequence according to the parameter separation sequence, establishes an initial service prediction function according to the second service demand sequence and the traffic interference factor, and then performs de-perturbation and discretization on the initial service prediction function to obtain a target service prediction function;
and the service prediction module predicts the daily service demand of the target service in the target prediction period according to the target service prediction function to obtain predicted service demand data and sends the predicted service demand data to the government affair management terminal.
For the purposes of promoting an understanding, the principles and operation of the present invention are described in detail below.
Specifically, in one embodiment, a cloud computing and smart city based government administration system may include:
s1, the government affair management platform receives a government affair management request sent by the government affair management terminal, and the data analysis module acquires historical business handling data of the target business from the database according to the business code and performs data analysis on the historical business handling data to obtain historical business demand data.
In one embodiment, the government administration request includes a traffic code and a target prediction period. The service code is used for uniquely identifying the service, and the service comprises the following steps: house passing, marriage registration, company registration and business approval. The target prediction period is one or more prediction time periods preset by government administration staff. The traffic interference factor is a factor influencing traffic demand, and comprises time, regulation and news; the historical business transaction data comprises expected business volume, actual business volume and business influence classification data of the target business every day in a historical statistic period.
The historical statistical period is the historical time period involved by the historical business handling data. The historical service transaction data is used for analyzing all the historical transaction services of the clients, and analyzing and counting the number of the clients actually completing the service transaction every day in the historical counting period.
In one example, according to the service prediction requirement of the government administration staff, the government administration staff sends a target prediction period containing a service code for identifying a specific target service and an indication of a prediction time period to the government administration platform, so that the government administration platform can predict the number of service handling persons of the target service in a specified future time period according to the service code and the target prediction period.
The government affair management terminal is a device with calculation function, storage function and communication function used by government affair management personnel, and comprises: smart mobile phones, desktop computers, notebook computers, smart watches, and smart wearable devices.
After receiving a government affair management request sent by a government affair management terminal, the government affair management platform automatically predicts the service demand of the target service in the target prediction time period according to the target prediction period and the service code indicated by the government affair management request so as to optimize a personnel scheduling plan of relevant government affair treatment.
In practical application, the existing government affair handling service mode is solidified, the moving range of related workers is small, the mobility of the workers is poor, and human resources are wasted. For example, in the existing government affair handling mode, the number of windows for handling government affair business and the number of workers for handling business are fixed and not adaptively arranged according to actual business requirements.
Therefore, when the number of the business handling personnel is large, the shortage of the number of the business handling personnel handling the business for people is easy to occur, so that the waiting time of a client handling the business is too long, even the waiting time of the client exceeds the remaining working time of the business handling personnel, and the client needs to wait for the next working time of the business handling personnel to handle the related business. When the emergency degree of the business handled by the client is high, the condition delays the best opportunity for handling the business of the client, and brings serious inconvenience to the life of the client. When the number of the people handling the business is small, the number of the business processing personnel handling the business for people is too large, a part of the working time of the business processing personnel is idle, and a large amount of human resources are wasted.
In one embodiment, the data analysis module performing data analysis according to the historical service transaction data to obtain historical service requirement data includes:
the data analysis module acquires expected traffic, actual traffic and traffic influence classification data of the target service in a historical statistical period each day according to historical service transaction data, and calculates total traffic difference of the target service in the historical statistical period each day according to the expected traffic and the actual traffic;
the data analysis module acquires a first traffic difference and a second traffic difference according to the total traffic difference and the traffic influence classification data of each day in the historical statistical period, and acquires the traffic demand of each day in the historical statistical period according to the total traffic difference, the first traffic difference and the second traffic difference;
and the data analysis module generates historical service demand data according to the daily service demand in the historical statistical period.
In one embodiment, the data analysis module obtaining the first traffic difference amount and the second traffic difference amount according to the total traffic difference amount and the traffic impact classification data comprises:
the data analysis module acquires the business influence classification coefficient of each client stopping transacting the target business according to the business influence classification data; the business influence classification data comprises business influence classification coefficients of all clients stopping transacting the target business;
the data analysis module divides all the clients suspending transacting the target service into a first client and a second client according to the service influence classification coefficient of each client suspending transacting the target service;
the data analysis module respectively counts the number of the first customers and the number of the second customers to obtain the number of the first customers and the number of the second customers;
and the data analysis module performs data verification on the first client quantity and the second client quantity according to the total traffic difference quantity, and takes the first client quantity as the first traffic difference quantity and the second client quantity as the second traffic difference quantity when the first client quantity and the second client quantity pass the verification.
In one embodiment, the expected traffic is the number of queued people for the target traffic; the actual business volume is the actual number of people handling the target business; the business influence classification data comprises business influence classification coefficients of all clients stopping handling the target business; the business influence classification coefficient is used for representing the reason why the client suspends the handling of the target business, when the business influence classification coefficient is zero, the corresponding client suspends the handling of the target business due to the self reason, and when the business influence classification coefficient is one time, the corresponding client suspends the handling of the target business due to the reason of the business handling center.
In one embodiment, the aggregate traffic differential is the total number of customers who discontinued transacting the target traffic; the first business difference is the number of clients who stop transacting the target business due to the clients; the second traffic difference is the number of clients that have suspended transacting the target traffic due to the traffic transaction center.
Optionally, the reason for suspending transaction is that the client suspends transaction of related services, for example, the client cannot perform service transaction in time according to the transaction sequence indicated by the queuing number due to some emergency. For example, after the client obtains the serial number of the business transaction through the self-service machine, the business transaction cannot be performed because the related certificate of the transaction business is lost or omitted, so that the transaction of the corresponding business is stopped. Or, the client needs to take off the body in the middle of the business transaction due to the emergency, and the transaction of the corresponding business is stopped.
Therefore, in the calculation process, the condition that part of clients do not finish business handling finally is considered, namely the interference of the irrelevant reasons is removed, and the number of business handling persons actually finishing business handling in the relevant historical business handling data is calculated, so that the predicted business demand is more accurate.
S2, the data analysis module extracts the service requirements of the historical service requirement data to obtain a first service requirement sequence; and then updating the first service demand sequence according to the correlation coefficient of each traffic interference factor and the target service to obtain a service disturbance sequence.
The first traffic demand sequence includes a traffic demand of the target traffic for each day over a historical statistics period.
The traffic interference factor is an external factor influencing the traffic demand of the relevant government affair platform. For example, the traffic demand, which is affected by time factors, is proliferating as: people tend to define marriage dates as certain dates having special significance, and the number of business transactions on the relevant government platform is increased on the specific dates such as 5 months, 20 days and 2 months, 14 days.
The traffic demand fluctuations affected by the specified factors are: when the new provision for outgoing channels lowers the threshold of the original object, the number of people handling related services will be increased, which will cause the service demand to rise, and when the new provision for outgoing channels raises the threshold of the original object, the number of people handling related services will be decreased, which will cause the service demand to fall.
According to the method and the device, when the service demand in the target prediction period is predicted, the influence of the service disturbance factor on the service demand is considered, so that the generated target service prediction function is more accurate, and the accuracy of the service demand prediction result in a future specified time period is improved.
In one embodiment, the obtaining of the first service requirement sequence by the data analysis module performing service requirement extraction on the historical service requirement data includes:
the data analysis module acquires the daily service demand of the target service in a historical statistical period according to the historical service demand data; the service demand is the number of people who have the target service to handle the demand;
the data analysis module carries out ascending arrangement on the business demand of each day in the historical statistical period according to the time sequence to obtain a first business demand sequence; the first traffic demand sequence includes a traffic demand of the target traffic for each day over a historical statistics period.
S3, the parameter separation module performs first parameter separation on the service disturbance sequence to obtain a first separation component and a first separation residual quantity, performs second parameter separation on the first separation residual quantity to obtain a second separation component and a second separation residual quantity, and performs third parameter separation on the second separation residual quantity to obtain a third separation component and a third separation residual quantity; performing iterative operations on the step until the separation residue cannot be separated continuously; and linearly summing the separation component of each parameter separation and the separation residual quantity of the last parameter separation to obtain a parameter separation sequence.
S4, the service prediction module generates a second service demand sequence according to the parameter separation sequence, establishes an initial service prediction function according to the second service demand sequence and the traffic interference factor, and then carries out de-perturbation and discretization on the initial service prediction function to obtain a target service prediction function; and predicting the daily service demand of the target service in the target prediction period according to the target service prediction function to obtain predicted service demand data and sending the predicted service demand data to the government affair management terminal.
In one embodiment, the second sequence of service requirements is
D＝[d(0),d(1)…d(N-1)]
And D is the second service demand sequence, N is the number of days of the historical statistical period, and D (N-1) is the service demand of the (N-1) th day in the historical statistical period.
In one embodiment, the step of establishing, by the traffic prediction module, the initial traffic prediction function according to the second traffic demand sequence and the traffic interference factor includes:
wherein p (D | δ) is an initial service prediction function, δ is a traffic interference factor, D is a second service demand sequence, N is the number of days of the historical statistical period, t is a time period index, and D (t) is the traffic demand of the target service on the t-th day in the historical statistical period.
In one embodiment, the performing, by the traffic prediction module, the de-perturbation and discretization on the initial traffic prediction function to obtain the target traffic prediction function includes:
wherein R is a target traffic prediction function, dsAs an actual value of the traffic demand, dpThe predicted value of the service demand is obtained according to the initial service prediction function, N is the number of days of the historical statistical period, xi adjustment coefficient, a is a weight parameter, and b is a bias parameter.
In another embodiment, the government affair management terminal displays the service requirement data to corresponding government affair managers through a user interface, and the government affair managers make personnel scheduling plans according to the service requirement data.
The service demand data comprises daily service demand in a target prediction period, so that relevant government managers can make reasonable personnel scheduling plans, the utilization rate of human resources is improved, the idle working time of relevant service processing personnel is reduced, and the waiting time of business handling of customers is reduced.
In another embodiment, the government administration making a personnel scheduling plan according to the business requirement data comprises:
the service management personnel acquires the daily service demand in the target prediction period according to the service demand data;
the business management personnel obtain the supply and demand service ratio of the target business, and the supply and demand service ratio represents the number of clients each business processing personnel can service every day;
and the service management personnel customize a personnel scheduling plan in the target prediction period according to the supply-demand service ratio of the target service and the daily service demand in the target prediction period.
The method and the system predict the service demand of the target service in the appointed time period according to the collected historical service handling data of the target service, thereby helping government affair managers to make a reasonable personnel scheduling plan, fully utilizing human resources and avoiding the situation of human idle under the condition of improving user experience.
Fig. 2 is a schematic diagram of a server for implementing the government administration platform of the present invention. The data analysis module, the parameter separation module, the service prediction module and the database in the government affair management platform are all operated on the server.
As shown in fig. 2, the server may include a network interface, a machine-readable storage medium, a processor, and a bus. The processor may be one or more, and one processor is illustrated in fig. 2. The network interface, the machine-readable storage medium, and the processor may be connected by a bus or other means, such as the bus connection in fig. 2.
The machine-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DR RAM). In some examples, the machine-readable storage medium may further include memory located remotely from the processor, which may be connected to a server over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The server can perform information interaction with other equipment through the network interface. The network interface may be a circuit, bus, transceiver, or any other device that may be used to exchange information. The processor may send and receive information using the network interface.
The principles and embodiments of this document are explained herein using specific examples, which are presented only to aid in understanding the methods and their core concepts; meanwhile, for the general technical personnel in the field, according to the idea of this document, there may be changes in the concrete implementation and the application scope, in summary, this description should not be understood as the limitation of this document.
Claims (9)
1. A government affairs management system based on cloud computing and smart cities is characterized by comprising: the system comprises a government affair management terminal and a government affair management platform, wherein the government affair management platform is in communication connection with the government affair management terminal; the government affair management platform comprises a data analysis module, a parameter separation module, a service prediction module and a database, and communication connection is formed among the modules;
the government affair management terminal sends a government affair management request to the government affair management platform; the government affair management request comprises a business code and a target prediction period;
the data analysis module acquires historical service handling data of a target service from a database according to the service code and performs data analysis on the historical service handling data to obtain historical service requirement data;
the data analysis module extracts the service requirements of the historical service requirement data to obtain a first service requirement sequence; the first service demand sequence comprises the daily service demand of the target service in a historical statistical period;
the data analysis module acquires all traffic interference factors of the target service, acquires the correlation coefficient of each traffic interference factor and the target service, and updates the first service demand sequence according to the correlation coefficient of each traffic interference factor and the target service to obtain a service disturbance sequence;
the parameter separation module performs first parameter separation on the service disturbance sequence to obtain a first separation component and a first separation residual quantity, performs second parameter separation on the first separation residual quantity to obtain a second separation component and a second separation residual quantity, and performs third parameter separation on the second separation residual quantity to obtain a third separation component and a third separation residual quantity; performing iterative operations on the step until the separation residue cannot be separated continuously;
the parameter separation module linearly sums the separation component of each parameter separation and the separation residual quantity of the last parameter separation to obtain a parameter separation sequence;
the service prediction module generates a second service demand sequence according to the parameter separation sequence, establishes an initial service prediction function according to the second service demand sequence and the traffic interference factor, and then performs de-perturbation and discretization on the initial service prediction function to obtain a target service prediction function;
and the service prediction module predicts the daily service demand of the target service in the target prediction period according to the target service prediction function to obtain predicted service demand data and sends the predicted service demand data to the government affair management terminal.
2. The system according to claim 1, wherein the government administration terminal is a device having a calculation function, a storage function and a communication function used by government administration personnel, and comprises: smart mobile phones, desktop computers, notebook computers, smart watches, and smart wearable devices.
3. The system of claim 1 or 2, wherein the data analysis module performing service requirement extraction on the historical service requirement data to obtain the first service requirement sequence comprises:
the data analysis module acquires the daily service demand of the target service in a historical statistical period according to the historical service demand data; the service demand is the number of people who have the target service to handle the demand;
the data analysis module carries out ascending arrangement on the business demand of each day in the historical statistical period according to the time sequence to obtain a first business demand sequence; the first traffic demand sequence includes a traffic demand of the target traffic per day over a historical statistics period.
4. The system of claim 3, wherein the data analysis module performing data analysis based on the historical business transaction data to obtain historical business requirement data comprises:
the data analysis module acquires expected traffic, actual traffic and traffic influence classification data of the target service in a historical statistical period each day according to historical service transaction data, and calculates total traffic difference of the target service in the historical statistical period each day according to the expected traffic and the actual traffic;
the data analysis module acquires a first traffic difference and a second traffic difference according to the total traffic difference and the traffic influence classification data of each day in the historical statistical period, and acquires the traffic demand of each day in the historical statistical period according to the total traffic difference, the first traffic difference and the second traffic difference.
5. The system of any of claims 1 to 4, wherein the data analysis module obtains the first traffic delta amount and the second traffic delta amount according to the total traffic delta amount and the traffic impact classification data comprises:
the data analysis module acquires the business influence classification coefficient of each client stopping transacting the target business according to the business influence classification data; the business influence classification data comprises business influence classification coefficients of all clients stopping transacting the target business;
the data analysis module divides all the clients suspending transacting the target service into a first client and a second client according to the service influence classification coefficient of each client suspending transacting the target service;
the data analysis module respectively counts the number of the first customers and the number of the second customers to obtain the number of the first customers and the number of the second customers;
and the data analysis module performs data verification on the first client quantity and the second client quantity according to the total traffic difference quantity, and takes the first client quantity as the first traffic difference quantity and the second client quantity as the second traffic difference quantity when the first client quantity and the second client quantity pass the verification.
6. The system of claim 5, wherein the traffic prediction module establishing the initial traffic prediction function according to the second traffic demand sequence and the traffic interference factor comprises:
wherein p (D | δ) is an initial service prediction function, δ is a traffic interference factor, D is a second service demand sequence, N is the number of days of the historical statistical period, t is a time period index, and D (t) is the traffic demand of the target service on the t-th day in the historical statistical period.
7. The system of claim 6, wherein the traffic prediction module de-perturbing and discretizing the initial traffic prediction function to obtain the target traffic prediction function comprises:
wherein R is a target traffic prediction function, dsAs an actual value of the traffic demand, dpThe predicted value of the service demand is obtained according to the initial service prediction function, N is the number of days of the historical statistical period, xi adjustment coefficient, a is a weight parameter, and b is a bias parameter.
8. The system of claim 7, wherein the service code is configured to uniquely identify a service; the services include house passing, marriage registration, company registration, and business licensing.
9. The system of claim 8, wherein the traffic interference factor is a factor affecting traffic demand, including time, regulation, and news; the historical service transaction data comprises expected service volume, actual service volume and service influence classification data of the target service in a historical statistic period every day.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308964A (en) * | 2023-05-23 | 2023-06-23 | 南京小微网络软件有限责任公司 | Internet civil service system platform based on big data |
| CN117539648A (en) * | 2024-01-09 | 2024-02-09 | 天津市大数据管理中心 | Service quality management method and device for electronic government cloud platform |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116308964A (en) * | 2023-05-23 | 2023-06-23 | 南京小微网络软件有限责任公司 | Internet civil service system platform based on big data |
| CN116308964B (en) * | 2023-05-23 | 2023-08-15 | 南京小微网络软件有限责任公司 | Internet civil service system platform based on big data |
| CN117539648A (en) * | 2024-01-09 | 2024-02-09 | 天津市大数据管理中心 | Service quality management method and device for electronic government cloud platform |
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