CN112306953A

CN112306953A - Smart city data archiving method and device and electronic equipment

Info

Publication number: CN112306953A
Application number: CN202010037041.3A
Authority: CN
Inventors: 袁修庭
Original assignee: Shenzhen Shinyo Blue Energy Technology Co ltd
Current assignee: Shenzhen Shinyo Blue Energy Technology Co ltd
Priority date: 2020-01-14
Filing date: 2020-01-14
Publication date: 2021-02-02

Abstract

The invention relates to the technical field of big data, and discloses a smart city data archiving method and device and electronic equipment. The method comprises the steps of obtaining data of all fields of the smart city, classifying the data of all fields of the smart city, obtaining the classified data of all fields, analyzing the classified data of all fields, and obtaining data usability levels, wherein the data usability levels are used for representing the possibility that the data are reused, and the data are filed according to usability levels of different data, so that the data can be checked at any time, and the storage space is saved.

Description

Smart city data archiving method and device and electronic equipment

Technical Field

The invention relates to the technical field of big data, in particular to a smart city data archiving method and device and electronic equipment.

Background

Along with the continuous development of information technology, the urban informatization application level is continuously improved, and the construction of smart cities is in due course. By applying the intelligent computing technology, the smart city can enable key infrastructure components and services of city composition such as city management, education, medical treatment, real estate, transportation, public utilities, public safety and the like to be more interconnected, efficient and intelligent.

The data category of the smart city is wide, and the data amount is huge. After the data is accumulated more, the historical data is typically archived. The traditional data filing mode is to file according to the time of data generation, the filing process is long, the efficiency is low, and the process of reusing after filing is complex, so that the data filing mode is not beneficial to viewing and occupies storage space.

Disclosure of Invention

Therefore, it is necessary to provide a method, an apparatus and an electronic device for smart city data archiving, which can not only check data at any time, but also save storage space.

In a first aspect, an embodiment of the present invention provides a smart city data archiving method, where the method includes:

acquiring data of each field of the smart city;

classifying the data of each field of the smart city to obtain the classified data of each field;

analyzing the classified data of each field to obtain data usability levels, wherein the data usability levels are used for representing the possibility of reusing the data;

and archiving the data according to the usability levels of different data.

In some embodiments, the classifying the data of each field of the smart city to obtain classified data of each field includes:

and classifying the data of each field of the smart city by using a preset classification rule to obtain the classified data of each field.

In some embodiments, the analyzing the classified data of each field to obtain the data usability level includes:

and analyzing the classified data of each field by using a preset neural network model based on machine learning to obtain the usability level of the data.

In some embodiments, the method further comprises:

a machine learning based neural network model is pre-trained.

In some embodiments, the pre-training of the machine learning based neural network model comprises:

obtaining a plurality of sample data of each classified field;

marking a corresponding label for each sample data;

and based on a machine learning algorithm, using the plurality of sample data and the corresponding label training model of each sample data to obtain the preset machine learning-based neural network model.

In some embodiments, the data usability levels include strong, medium and weak,

the archiving the data according to the usability level of the different data includes:

if the usability level of the data is high, prolonging the data filing period and temporarily storing the data to a real-time database;

and if the usability level of the data is medium or weak, storing the data into a historical database according to a preset period.

In some embodiments, after the archiving the data according to the preset period if the usability level of the data is weak, the method further includes:

and after the preset time is reached, compressing the data with the weak usability level.

In a second aspect, an embodiment of the present invention further provides a smart city data archiving device, where the smart city data archiving device includes:

the acquisition module is used for acquiring data of each field of the smart city;

the classification module is used for classifying the data of each field of the smart city to obtain the classified data of each field;

the analysis module is used for analyzing the classified data of each field to obtain a data usability level, wherein the data usability level is used for representing the possibility of reusing the data;

and the archiving module is used for archiving the data according to the usability levels of different data.

In a third aspect, an embodiment of the present invention further provides an electronic device, including:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described smart city data archiving method.

In a fourth aspect, the present invention further provides a non-volatile computer-readable storage medium, where the computer-readable storage medium stores computer-executable instructions, and when executed by a processor, causes the processor to execute the foregoing smart city data archiving method.

Compared with the prior art, the invention has the beneficial effects that: different from the situation of the prior art, in the smart city data archiving method in the embodiment of the invention, the electronic device acquires data of each field of the smart city, classifies the data of each field of the smart city to acquire the classified data of each field, analyzes the classified data of each field to acquire data usability levels for representing the possibility of reusing the data, and finally performs different archiving processing on the data according to usability levels of different data, so that the data can be viewed at any time, and the storage space is saved.

Drawings

One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the figures in which like reference numerals refer to similar elements and which are not to scale unless otherwise specified.

FIG. 1 is a schematic diagram illustrating an application scenario of a smart city data archiving method according to an embodiment of the present invention;

FIG. 2 is a schematic flow chart illustrating a smart city data archiving method according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of a training model according to an embodiment of the present invention;

FIG. 4 is a flow diagram illustrating data archiving according to different levels of data usability in one embodiment of the present invention;

FIG. 5 is a schematic diagram of a smart city data archive device according to an embodiment of the present invention;

fig. 6 is a schematic diagram of a hardware structure of an electronic device in an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be 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 some, but not all, embodiments of the present invention. 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 invention.

It should be noted that, if not conflicted, the various features of the embodiments of the invention may be combined with each other within the scope of protection of the invention. Additionally, while functional block divisions are performed in apparatus schematics, with logical sequences shown in flowcharts, in some cases, steps shown or described may be performed in sequences other than block divisions in apparatus or flowcharts. The terms "first", "second", "third", and the like used in the present invention do not limit data and execution order, but distinguish the same items or similar items having substantially the same function and action.

The smart city data archiving method provided by the embodiment of the invention is suitable for the application scene shown in fig. 1, and comprises at least one sensor and electronic equipment, wherein the electronic equipment is in communication connection with the sensor. Fig. 1 illustrates an electronic device 100, a sensor 1, a sensor 2, a. The sensor is connected to the electronic device through network communication, for example, the sensor is connected to the electronic device through local area network, wide area network, wireless network, Global System for Mobile communication (GSM), third generation Mobile communication network, fourth generation Mobile communication network, fifth generation Mobile communication network, and the like.

The sensor is used for gathering the data in each field of wisdom city to each field data of wisdom city with gathering sends for electronic equipment through the network, equipment is used for handling and filing each field data of wisdom city.

The electronic device may be, for example, a tablet computer, a personal computer, a laptop computer, or a server, such as a rack server, a blade server, a tower server, or a cabinet server, or a server cluster including a plurality of servers, or a cloud computing service center.

It should be noted that the method in the embodiment of the present invention may be further extended to other suitable application environments, and is not limited to the application environment shown in fig. 1. In a practical application environment, the application environment may also comprise more or fewer sensors and electronic devices.

As shown in fig. 2, an embodiment of the present invention provides a smart city data archiving method, where the method is executed by an electronic device, and includes:

step 202, obtaining data of each field of the smart city.

In the embodiment of the present invention, the data of each field of the smart city, for example, may be government administration data, city management data, civil service data, enterprise economic data, and basic data for ensuring normal and abnormal operation of the smart city, including: legal, population, enterprise, financial, statistical, resource, security, traffic, energy consumption, municipal, production, marketing, business, logistics, medical, health, education, property, community, etc. data. The type of the acquired data of each field of the smart city is not limited to audio, video or files. Specifically, the electronic device obtains data of each field of the smart city collected by the sensor.

And 204, classifying the data of each field of the smart city to obtain the classified data of each field.

In the embodiment of the invention, after the electronic device acquires the data of each field of the smart city, the data of each field of the smart city is classified, and specifically, the data of each field of the smart city can be classified by using a preset classification rule, so that the classified data of each field is acquired.

Specifically, the preset data classification rule may be set according to the data type and the data source field. Exemplary, the predetermined data classification rule is illustrated in the data source field. If the original data A belongs to city management data, the original data B belongs to government administration data, the original data C belongs to civil service data, and the original data D belongs to government administration data, the classification is carried out according to the source field of the data, the original data A and the original data C are separately divided into one class, and the original data B and the original data D are divided into the same class because the original data B and the original data D both belong to government administration data, so that the data in the same data source field are classified into one class. It should be noted that the preset data classification rule can be set according to the requirement, and is not limited to the limitation in this embodiment.

And step 206, analyzing the classified data of each field to obtain a data usability level, wherein the data usability level is used for representing the possibility of reusing the data.

In embodiments of the present invention, the usability levels of the data include strong, medium and weak, and the data usability levels are used to characterize the likelihood that the data will be reused. Specifically, after the electronic device classifies the data of each field of the smart city, the classified data of each field is analyzed to obtain data with strong, medium and weak data usability levels, if the usability level of the data is strong, the possibility that the data is reused is high, if the usability level of the data is medium, the possibility that the data is reused is high, and if the usability level of the data is weak, the possibility that the data is reused is low.

The usability level of the data can be identified by using a level identifier, and when the level identifier is a first identifier, the usability level of the data is strong; when the level mark is the second mark, the usability level of the data is represented as middle; when the level flag is the third flag, it indicates that the usability level of the data is weak. The first identifier may be 1, the second identifier may be 2, and the third identifier may be 3. In addition, the first identifier may be 0, the second identifier may be 1, and the third identifier may be 2, which is only an example and is not limited thereto.

Step 208, archiving the data according to the usability level of the different data.

Specifically, after the electronic device analyzes the classified data of each field and obtains the usability level of the data, the electronic device saves the data with different usability levels to different databases according to the usability levels of different data, thereby completing the filing operation.

In the embodiment of the invention, the electronic equipment acquires data of each field of the smart city acquired by the sensor, classifies the data of each field of the smart city to acquire the classified data of each field, analyzes the classified data of each field to acquire the usability level of the data for representing the possibility of reusing the data, and finally files the data according to the usability levels of different data, so that the data can be viewed at any time and the storage space is saved.

In some embodiments, the analyzing the classified data of each field to obtain the data usability level includes: and analyzing the classified data of each field by using a preset neural network model based on machine learning to obtain the usability level of the data.

In the embodiment of the invention, the neural network model based on machine learning can be trained on other equipment in advance and then directly loaded on the electronic equipment for operation. Specifically, after the electronic equipment classifies data of each field of the smart city by using a preset classification rule, the classified data of each field is analyzed by using a neural network model based on machine learning in the electronic equipment, so that the usability level of the data is obtained. Illustratively, when the electronic device classifies data of all fields of the smart city according to a preset classification rule, namely a data source field, three different types of data, namely data A, data B and data C, are obtained, wherein the data A belongs to city management data, the data B belongs to government administration data, and the data C belongs to civil service data. Each type of data comprises at least one piece of sub-data, and the data C belongs to the civil service data, and exemplarily comprises sub-data such as town population, country population, minimum income population of urban residents, minimum income population of rural residents, local financial education expenditure, road mileage and the like. And then, analyzing the data C by using a neural network model based on machine learning in the electronic equipment, specifically analyzing subdata contained in the data C, so as to obtain the usability level of the subdata under the data C.

Specifically, the data C is analyzed by using a neural network model based on machine learning, and the data C belongs to the data of the civil service, including data of town population, country population, the minimum income population of urban residents, the minimum income population of rural residents, local financial education expenditure, road mileage and the like. After the data C is analyzed by using a neural network model based on the mechanics, the data with strong usability level are town population and country population data; the data with the medium usability level of data is the data of the lowest income number of urban residents, the lowest income number of rural residents and the expense data of local financial education, and the data with the weak usability level of data is the data of highway mileage.

In some embodiments, as shown in fig. 3, the pre-training the machine learning-based neural network model includes:

step 302, obtaining a plurality of sample data of each classified field.

And step 304, marking a corresponding label for each sample data.

Specifically, a large amount of classified data of each field of the smart city is stored in a database of the electronic device in advance, the electronic device can directly acquire a plurality of classified sample data of each field from the database, corresponding labels are sequentially marked for the acquisition of the plurality of sample data, and the more the sample data of each field is, the more accurate the subsequent data identification of each field of the smart city is. When the electronic equipment acquires data A, data B and data C from the database, the data A belongs to city management data, the data B belongs to government administration data, and the data C belongs to civil service data. Illustratively, the data C belongs to the civil service data and includes subdata such as town population, country population, minimum income population of urban residents, minimum income population of rural residents, local financial education expenditure, road mileage and the like. The usability level of the data is high for the labels corresponding to the population data of the town population and the country population; the label corresponding to the minimum income number of urban residents, the minimum income number of rural residents and the local financial education expenditure data is the middle data usability level; the data usability level of the label corresponding to the road mileage is weak.

It is understood that in other embodiments, the electronic device may also obtain data of various fields of a plurality of smart cities directly from the internet and classify the data.

And 306, based on a machine learning algorithm, using the plurality of sample data and the corresponding label training model of each sample data to obtain the preset machine learning-based neural network model.

In the embodiment of the present invention, the machine learning algorithm is a method that trains a model using data and then uses the model for prediction. Specifically, the electronic device obtains a plurality of sample data of each classified field and a corresponding label training model from the database, so that a preset machine learning-based neural network model is obtained, and then the trained machine learning-based neural network model can be directly used for analyzing data of each field of the smart city, thereby being beneficial to improving the accuracy of data analysis.

It is understood that in other embodiments, there may be one or more machine learning based neural network models. When the number of the neural network models based on the machine learning is multiple, the data under different classifications are analyzed by using different neural network models based on the machine learning for each field of the smart city.

In some embodiments, as shown in fig. 4, the archiving the data according to the usability level of the different data includes:

step 402, if the usability level of the data is strong, extending the data filing period and temporarily storing the data to a real-time database.

In the embodiment of the invention, the real-time database is used for temporarily storing the data with strong usability level. Specifically, after the electronic device analyzes the data with the strong data usability level, the filing period of the data with the strong data usability level is prolonged and temporarily stored in the real-time database, so that the data can be conveniently viewed at any time.

And step 404, if the usability level of the data is medium or weak, storing the data into a historical database according to a preset period.

In an embodiment of the present invention, the history database is used to store data with a medium data usability level and data with a weak data usability level. Specifically, after the electronic device analyzes that the data usability level is medium or weak, the data with the medium or weak usability level is stored in the historical database according to a preset period. The preset period may be, for example, one month or one week, and the preset period may be set according to the service requirement without being limited by the definition in this embodiment.

In some other embodiments, after the archiving the data according to the preset period if the usability level of the data is weak, the method further includes: and after the preset time is reached, compressing the data with the weak usability level.

Specifically, the preset time may be, for example, 1 month, or 2 months or the like. When the electronic equipment analyzes that the usability level of the data is weak and stores the data in the historical database, and when the storage time of the data with the weak usability level in the historical database reaches a preset time, namely 1 month, the electronic equipment compresses the data with the weak usability level to obtain a compressed packet of the data with the weak usability level, so that the storage space can be saved.

Correspondingly, an embodiment of the present invention further provides a smart city data archiving apparatus 500, as shown in fig. 5, the apparatus includes:

an obtaining module 502, configured to obtain data of each field of a smart city;

the classification module 504 is configured to classify data of each field of the smart city to obtain classified data of each field;

an analysis module 506, configured to analyze the classified data of each domain to obtain a data usability level, where the data usability level is used to represent a possibility of data being reused;

an archiving module 508 for archiving the data according to the usability level of the different data.

According to the smart city data filing device provided by the embodiment of the invention, the data of each field of the smart city is obtained through the obtaining module, then the data of each field of the smart city is classified through the classifying module to obtain the classified data of each field, then the classified data of each field is analyzed through the analyzing module to obtain the data usability level for representing the possibility of reusing the data, and finally the data with different usability levels are filed through the filing module, so that the data can be viewed at any time, and the storage space is saved.

Optionally, in some embodiments, the classification module 504 is specifically configured to:

Optionally, in some embodiments, the analysis module 506 is specifically configured to:

Optionally, in some embodiments, as shown in fig. 5, the apparatus 500 further includes:

a training module 510 for pre-training the machine learning based neural network model.

Optionally, in some embodiments, the training module 510 is specifically configured to:

obtaining a plurality of sample data of each classified field;

marking a corresponding label for each sample data;

Optionally, in some embodiments, the data usability level includes strong, medium, and weak, and the archive module 508 is specifically configured to:

Optionally, in some other embodiments, as shown in fig. 5, the apparatus 500 further includes:

and the compression module 512 is configured to compress the data with the weak usability level after the preset time is reached.

It should be noted that the smart city data filing apparatus can execute the smart city data filing method provided by the embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

Fig. 6 is a schematic diagram of a hardware structure of an electronic device provided in the present invention, and as shown in fig. 6, the electronic device 600 includes:

one or more processors 602 and memory 604, one processor 602 being illustrated in fig. 6.

The processor 602 and the memory 604 may be connected by a bus or other means, such as by a bus in FIG. 6.

The memory 604, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as instructions/modules corresponding to the smart city data archiving method in the embodiment of the present invention (for example, the obtaining module 502, the classifying module 504, the analyzing module 506, the archiving module 508, the training module 510, and the compressing module 512 shown in fig. 5). The processor 602 executes various functional applications and data processing of the electronic device by running the non-volatile software programs, instructions and modules stored in the memory 604, so as to implement the smart city data archiving method of the above method embodiment.

The memory 604 may 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 smart city data archive device usage, and the like. Further, the memory 604 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 604 optionally includes memory located remotely from processor 602, and such remote memory may be connected to a smart city data archive 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 one or more modules stored in the memory 604, when executed by the one or more electronic devices, perform the method for smart city data archiving in any of the above-described method embodiments, e.g., performing the above-described method steps 202-208 of fig. 2, method steps 302-306 of fig. 3, and method steps 402-404 of fig. 4; the functions of blocks 502 to 512 in fig. 5 are implemented.

The electronic device 600 in the embodiment of the present invention exists in various forms, including but not limited to a server or a terminal device, and may be, for example:

(1) tower server

The general tower server chassis is almost as large as the commonly used PC chassis, while the large tower chassis is much larger, and the overall dimension is not a fixed standard.

(2) Rack-mounted server

Rack-mounted servers are a type of server that has a standard width of 19 inch racks, with a height of from 1U to several U, due to the dense deployment of the enterprise. Placing servers on racks not only facilitates routine maintenance and management, but also may avoid unexpected failures. First, placing the server does not take up too much space. The rack servers are arranged in the rack in order, and no space is wasted. Secondly, the connecting wires and the like can be neatly stored in the rack. The power line, the LAN line and the like can be distributed in the cabinet, so that the connection lines accumulated on the ground can be reduced, and the accidents such as the electric wire kicking off by feet can be prevented. The specified dimensions are the width (48.26cm ═ 19 inches) and height (multiples of 4.445 cm) of the server. Because of its 19 inch width, a rack that meets this specification is sometimes referred to as a "19 inch rack".

(3) Blade server

A blade server is a HAHD (High Availability High Density) low cost server platform designed specifically for the application specific industry and High Density computer environment, where each "blade" is actually a system motherboard, similar to an individual server. In this mode, each motherboard runs its own system, serving a designated group of different users, without any relationship to each other. Although system software may be used to group these motherboards into a server cluster. In the cluster mode, all motherboards can be connected to provide a high-speed network environment, and resources can be shared to serve the same user group.

(4) Cloud server

The cloud server (ECS) is a computing Service with simplicity, high efficiency, safety, reliability, and flexible processing capability. The management mode is simpler and more efficient than that of a physical server, and a user can quickly create or release any plurality of cloud servers without purchasing hardware in advance. The distributed storage of the cloud server is used for integrating a large number of servers into a super computer, and a large number of data storage and processing services are provided. The distributed file system and the distributed database allow access to common storage resources, and IO sharing of application data files is achieved. The virtual machine can break through the limitation of a single physical machine, dynamically adjust and allocate resources to eliminate single-point faults of the server and the storage equipment, and realize high availability.

(5) Mobile communication devices, which are characterized by mobile communication capabilities and are primarily targeted at providing voice and data communications. Such terminals include smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.

(6) The ultra-mobile personal computer equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include PDA, MID, and UMPC devices, such as ipads.

(7) Portable entertainment devices such devices may display and play multimedia content. Such devices include audio and video players (e.g., ipods), handheld game consoles, electronic books, as well as smart toys and portable car navigation devices.

Embodiments of the present invention also provide a computer program product, including a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform: method steps 202 through 208 in fig. 2.

The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; within the idea of the invention, also technical features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims

1. A smart city data archiving method, characterized in that the method comprises:

acquiring data of each field of the smart city;

and archiving the data according to the usability levels of different data.

2. The method of claim 1, wherein the classifying the data of each domain of the smart city to obtain the classified data of each domain comprises:

3. The method of claim 1, wherein analyzing the classified domain data to obtain data usability levels comprises:

4. The method of claim 3, further comprising:

a machine learning based neural network model is pre-trained.

5. The method of claim 4, wherein the pre-training the machine learning based neural network model comprises:

obtaining a plurality of sample data of each classified field;

marking a corresponding label for each sample data;

6. The method of claim 1, wherein the data usability levels include strong, medium, and weak,

7. The method of claim 6, wherein if the data usability level is weak, after archiving the data according to a predetermined period, the method further comprises:

8. A smart city data archiving device, characterized in that the device includes:

9. An electronic device, comprising:

at least one processor; and the number of the first and second groups,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, cause the processor to perform the method of any one of claims 1-7.