Method and system for analyzing stoppage causes based on big data analysis

Abstract

The downtime cause analysis method and system based on big data analysis provided in this application embodiment intelligently and specifically crawls the big data of smart device operation for target interactive smart devices that meet the set requirements to obtain the big data of target smart device operation that is related to them. This determines the decision-making information of the target interactive smart device, avoiding the process of determining decision-making information by transmitting related smart device operation big data or equipment downtime event information. To a certain extent, this ensures the intelligence and specificity of the determination of decision-making information. In addition, since the target smart device operation big data of the target interactive smart device is determined from the equipment operation big data record, the decision-making information obtained based on this target smart device operation big data can ensure the accuracy and reliability of equipment downtime cause analysis.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a method and system for analyzing downtime causes based on big data analysis. Background Technology

[0002] The continuous development and optimization of big data technology has brought significant benefits to various industries. Taking smart devices as an example, with the help of big data technology, smart devices can achieve adaptive operation, thereby reducing unnecessary resource waste and improving their ability to cope with various operational needs. However, in practical applications, the inventors have found that smart devices may experience malfunctions and shutdowns in some situations, and related technologies for analyzing these malfunctions and shutdowns suffer from low accuracy and reliability. Summary of the Invention

[0003] To address the technical problems existing in related technologies, this application provides a method and system for analyzing downtime causes based on big data analysis.

[0004] In a first aspect, embodiments of this application provide a method for analyzing downtime causes based on big data analytics, applied to a big data downtime analysis system. The method includes: responding to the crawling process of smart device operation big data, and determining, under the premise that at least one interactive smart device meets a first set requirement, device operation big data records corresponding to the at least one interactive smart device; based on the at least one device operation big data record, determining a target interactive smart device that meets a second set requirement from the at least one interactive smart device; determining the target smart device operation big data corresponding to the target interactive smart device from the at least one device operation big data record; and determining at least one of the target smart device operation big data and the device downtime event information of the target interactive smart device as decision-making information for analyzing device downtime causes.

[0005] Thus, by responding to the crawling process of big data on smart device operation, and under the premise of identifying interactive smart devices that meet the first set requirements, the corresponding big data records of device operation are determined. Based on these records, target interactive smart devices that meet the second set requirements are marked, thereby further determining the target smart device operation big data of the target interactive smart devices. The target smart device operation big data and / or device downtime event information are then identified as decision-making information serving the analysis of device downtime causes. Based on this, during the big data crawling process, smart device operation big data can be intelligently and specifically crawled for target interactive smart devices that meet the set requirements to obtain related target smart device operation big data, thereby determining the decision-making information of the target interactive smart devices. This avoids the process of determining decision-making information by transmitting related smart device operation big data or device downtime event information, ensuring to a certain extent the intelligence and specificity of the determination of decision-making information. Furthermore, since the target smart device operation big data of the target interactive smart devices is determined from the device operation big data records, the decision-making information obtained based on this target smart device operation big data can ensure the accuracy and reliability of the analysis of device downtime causes.

[0006] In an alternative embodiment, the determination that at least one interactive smart device meets the first preset requirement includes at least one of the following: It was determined that the activation phase of at least one interactive smart device exceeded the set time constraint interval; Within the set judgment step size, multiple positioning results for at least one interactive smart device are found to be greater than the cumulative result judgment value.

[0007] An alternative embodiment, wherein determining the target interactive smart device that meets the second predetermined requirement from the not less than one interactive smart device based on the big data records of at least one of the device operations, includes: Analyze the usage time loss of interactive intelligent devices in the multimodal intelligent device operation information in at least one of the device operation big data records, and determine the interactive intelligent devices whose usage time loss is within the usage time loss judgment value as the target interactive intelligent devices. And / or, determine a global smart device operation big data evaluation of at least one device operation big data record corresponding to the interactive smart device, mark the device operation big data record whose global smart device operation big data evaluation reaches the set smart device operation big data evaluation index as the target device operation big data record, and determine the interactive smart device in the target device operation big data record as the target interactive smart device.

[0008] An alternative embodiment, wherein the device operation big data record that marks the global intelligent device operation big data evaluation as reaching the set intelligent device operation big data evaluation index is determined as the target device operation big data record, includes: The device operation big data record with the highest global intelligent device operation big data evaluation, and / or the device operation big data record whose global intelligent device operation big data evaluation exceeds the global intelligent device operation big data evaluation judgment value, is determined as the target device operation big data record.

[0009] An alternative embodiment, wherein determining the target smart device operation big data corresponding to the target interactive smart device from the not less than one of the device operation big data records, includes: From at least one of the device operation big data records, the X groups of smart device operation big data with the highest annotated smart device operation big data evaluation covering the target interactive smart device are determined as the target smart device operation big data, where X is a positive integer.

[0010] An alternative embodiment determines the device downtime event information of the target interactive smart device based on the following method: The device shutdown event characteristics of the target interactive smart device are mined from the big data of the target smart device's operation, and the mining results are determined as the device shutdown event information of the target interactive smart device. Alternatively, based on the configuration parameters of the web crawler for the big data of the smart device's operation during the crawling process and / or the stage of crawling the big data of the target smart device's operation, the device shutdown event information of the target interactive smart device can be determined.

[0011] In an alternative embodiment, the determination of device shutdown event information of the target interactive smart device, based on the configuration parameters of the web crawler for crawling big data of the smart device's operation and / or the stage of crawling the target smart device's operation big data during the crawling process, includes at least one of the following: The configuration parameters of the web crawler for crawling big data of smart device operation are determined, and the configuration parameters are processed to obtain the device shutdown event information of the target interactive smart device. The stages of crawling the target smart device's operational big data are associated with the stage descriptions in the set device shutdown event information queue. The device shutdown events corresponding to the associated stage descriptions are identified as the device shutdown event information of the target interactive smart device.

[0012] In an alternative embodiment, the determination of decision-making information for analyzing the causes of device downtime based on the big data of the target intelligent device's operation and / or the device downtime event information includes at least one of the following: Given that the target interactive smart device has corresponding device downtime event information in the target smart device operation big data, the target smart device operation big data and the corresponding device downtime event information are fused to obtain decision-making information for analyzing the causes of device downtime. If there is no corresponding device shutdown event information for the target interactive smart device in the target smart device operation big data, the target smart device operation big data is fused with the set device shutdown event information to obtain decision-making information for analyzing the causes of device shutdown.

[0013] An alternative embodiment of the method further includes: Identify the big data of intelligent device operation to be analyzed and mined; The big data of the operation of the intelligent device to be mined is associated with the big data of the operation of the target intelligent device in at least one of the decision-making information. Under the premise that the association is completed, the device shutdown event of the smart device to be analyzed is determined based on the device shutdown event information corresponding to the big data of the target smart device's operation. After determining the device shutdown event of the intelligent device to be analyzed, the method further includes: determining the semantic information of the intelligent device to be analyzed based on one or more of the following: the intelligent device operation big data to be mined, the device shutdown event of the intelligent device to be analyzed, and the determination stage of the intelligent device operation big data to be mined. The semantic information includes one or more of the following: the annotation stage, the annotation of the device shutdown event, and the annotation of the intelligent device operation big data.

[0014] Secondly, this application also provides a big data downtime analysis system, including a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the method described above. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] Figure 1 This is a schematic diagram of the hardware structure of a big data downtime analysis system provided in an embodiment of this application.

[0017] Figure 2This is a flowchart illustrating a method for analyzing downtime causes based on big data analysis, provided in an embodiment of this application. Detailed Implementation

[0018] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0020] The method embodiments provided in this application can be executed in a big data downtime analysis system, computer equipment, or similar computing device. Taking running on a big data downtime analysis system as an example, Figure 1 This is a hardware structure block diagram of a big data downtime analysis system that implements a downtime cause analysis method based on big data analysis, according to an embodiment of this application. Figure 1 As shown, the big data downtime analysis system 10 may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. Optionally, the above-described big data shutdown analysis system may further include a transmission device 106 for communication functions. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned big data downtime analysis system. For example, the big data downtime analysis system 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0021] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to a big data-based shutdown cause analysis method in this embodiment of the application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the big data shutdown analysis system 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0022] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the big data downtime analysis system 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0023] Based on this, please refer to Figure 2 , Figure 2 This is a flowchart illustrating a method for analyzing downtime causes based on big data analysis, provided by an embodiment of the present invention. This method is applied to a big data downtime analysis system, and can be further described by the technical solutions recorded in steps 11-14 below.

[0024] Step 11: In response to the crawling process of big data of smart device operation, under the premise of determining that at least one interactive smart device meets the first set requirement, determine the device operation big data records corresponding to the at least one interactive smart device respectively.

[0025] In one possible embodiment, determining in step 11 that at least one interactive smart device meets the first preset requirement can specifically include at least one of the following: determining that the activation phase of at least one interactive smart device exceeds a preset time constraint interval; and within a preset judgment step, repeatedly locating at least one interactive smart device to obtain a cumulative result greater than a cumulative result judgment value. Thus, by using the activation phase exceeding the preset time constraint interval and / or repeatedly locating at least one interactive smart device within the preset judgment step to obtain a cumulative result greater than a cumulative result judgment value as the first preset requirement, the interactive smart devices collected during the crawling process of smart device operating big data can be roughly selected. This reduces the impact of unexpected situations involving other interactive smart devices during the crawling process of smart device operating big data, thereby reducing the difficulty of subsequently obtaining target smart device operating big data and decision-making information, and thus improving the accuracy of obtaining decision-making information.

[0026] Step 12: Based on the big data records of at least one of the devices, determine the target interactive smart device that meets the second set requirement from the at least one interactive smart device.

[0027] In one possible embodiment, step 11, which involves determining the target interactive smart device that meets the second set requirement from the at least one interactive smart device based on at least one device operation big data record, may specifically include the following technical solution: performing usage time loss analysis on the multimodal smart device operation information in at least one device operation big data record, identifying interactive smart devices whose determined usage time loss is within the determined usage time loss value as the target interactive smart device; and / or, determining a global smart device operation big data evaluation of the device operation big data record corresponding to at least one interactive smart device, marking the device operation big data record whose global smart device operation big data evaluation reaches the set smart device operation big data evaluation index as the target device operation big data record, and identifying the interactive smart device in the target device operation big data record as the target interactive smart device. Thus, in the process of determining the target interactive smart device, the determination can be made according to the actual situation, thereby improving the efficiency of determining the target interactive smart device.

[0028] In one possible embodiment, the device operation big data record described above, which is marked as meeting the set intelligent device operation big data evaluation index, is determined as the target device operation big data record. Specifically, this may include: determining the device operation big data record with the highest global intelligent device operation big data evaluation, and / or the device operation big data record whose global intelligent device operation big data evaluation exceeds the global intelligent device operation big data evaluation judgment value, as the target device operation big data record. This ensures the quality of the determined target device operation big data record.

[0029] Step 13: Determine the target smart device operation big data corresponding to the target interactive smart device from at least one of the device operation big data records.

[0030] In one possible embodiment, step 13, which involves determining the target smart device operation big data corresponding to the target interactive smart device from the not less than one of the device operation big data records, may specifically include the following: from the not less than one of the device operation big data records, the X groups of smart device operation big data with the highest annotated smart device operation big data evaluation that cover the target interactive smart device are determined as the target smart device operation big data, where X is a positive integer.

[0031] Step 14: Determine at least one of the target intelligent device's operational big data and the target interactive intelligent device's device downtime event information as decision-making information for analyzing the causes of device downtime.

[0032] In one possible embodiment, step 14 determines the device downtime event information of the target interactive smart device based on the following methods: mining the device downtime event features of the target interactive smart device in the target smart device's operational big data, and determining the mining results as the device downtime event information of the target interactive smart device; or, determining the device downtime event information of the target interactive smart device based on the configuration parameters of the smart device's operational big data web crawler and / or the stage of crawling the target smart device's operational big data during the crawling process. This improves the flexibility of determining the device downtime event information of the target interactive smart device.

[0033] In one possible embodiment, the configuration parameters of the web crawler for crawling the big data of the smart device's operation and / or the stages of crawling the target smart device's big data during the crawling process recorded above, to determine the device shutdown event information of the target interactive smart device, may specifically include at least one of the following: determining the configuration parameters of the web crawler for crawling the smart device's big data, processing the configuration parameters to obtain the device shutdown event information of the target interactive smart device; associating the stages of crawling the target smart device's big data with the stage descriptions in a set device shutdown event information queue, and determining the device shutdown event corresponding to the associated stage description as the device shutdown event information of the target interactive smart device. This effectively improves the efficiency of obtaining device shutdown event information for the target interactive smart device.

[0034] In one possible embodiment, the decision-making information recorded in step 14 based on the target smart device's operational big data and / or the device downtime event information, used for analyzing the causes of device downtime, may specifically include at least one of the following: If the target interactive smart device in the target smart device's operational big data has corresponding device downtime event information, the target smart device's operational big data is fused with the corresponding device downtime event information to obtain decision-making information used for analyzing the causes of device downtime; if the target interactive smart device in the target smart device's operational big data does not have corresponding device downtime event information, the target smart device's operational big data is fused with the set device downtime event information to obtain decision-making information used for analyzing the causes of device downtime. This allows for the selection of appropriate methods based on actual circumstances, which can reduce the difficulty of obtaining decision-making information to a certain extent, while also expanding the application scenarios for obtaining decision-making information.

[0035] In one possible embodiment, based on the above, the method may further include the technical solutions recorded in steps 15-17.

[0036] Step 15: Identify the big data of smart device operation to be analyzed.

[0037] Step 16: Associate the big data of the operation of the intelligent device to be mined with the big data of the operation of the target intelligent device in at least one of the decision-making information.

[0038] Step 17: Assuming the association is complete, determine the device shutdown event of the smart device to be analyzed based on the device shutdown event information corresponding to the big data of the target smart device's operation.

[0039] It is understandable that when implementing steps 15-17, decision-making information can be effectively used to mine equipment downtime events of the intelligent device to be analyzed, thereby making full use of the obtained decision-making information.

[0040] Furthermore, after determining the device downtime event of the intelligent device to be analyzed, the process also includes: Based on one or more of the following: the big data of intelligent device operation to be analyzed, the device shutdown events of the intelligent device to be analyzed, and the determination stage of the big data of intelligent device operation to be analyzed, the semantic information of the intelligent device to be analyzed is determined, wherein the semantic information includes one or more of the following: annotation stage, annotation of device shutdown events, and annotation of big data of intelligent device operation.

[0041] When implementing the technical solutions recorded in steps 11-14, in response to the crawling process of smart device operation big data, under the premise of determining that the interactive smart device meets the first set requirements, the corresponding device operation big data record is determined, and based on the device operation big data record, the target interactive smart device that meets the second set requirements is marked, thereby further determining the target smart device operation big data of the target interactive smart device, and determining the target smart device operation big data and / or the device shutdown event information of the target interactive smart device as decision-making information to serve the analysis of the cause of device shutdown. Based on this, during the process of crawling big data on the operation of smart devices, intelligent and targeted crawling of big data on the operation of target interactive smart devices that meet the set requirements can be performed to obtain the big data on the operation of related target smart devices. This allows for the determination of decision-making information for the target interactive smart devices, avoiding the process of determining decision-making information by transmitting relevant smart device operation big data or equipment downtime event information. This, to a certain extent, ensures the intelligence and targeting of decision-making information determination. Furthermore, since the target smart device operation big data is determined from the equipment operation big data records, the decision-making information obtained based on this target smart device operation big data can ensure the accuracy and reliability of the analysis of equipment downtime causes.

[0042] Furthermore, a readable storage medium is provided on which a program is stored, which, when executed by a processor, implements the above-described method.

[0043] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0044] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0045] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a media service server 10, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0046] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for analyzing downtime causes based on big data analytics, characterized in that, The method, applied to a big data downtime analysis system, includes: In response to the crawling process of big data of smart device operation, under the premise of determining that at least one interactive smart device meets the first set requirement, the device operation big data records corresponding to the at least one interactive smart device are determined respectively; Based on big data records of at least one of the devices, a target interactive smart device that meets the second set requirement is determined from the at least one interactive smart device; Determine the target intelligent device operation big data corresponding to the target interactive intelligent device from at least one of the device operation big data records; The target intelligent device's operational big data and the target interactive intelligent device's device downtime event information are identified as decision-making information to serve the analysis of device downtime causes; The device shutdown event information of the target interactive smart device is determined based on the following method: The device shutdown event characteristics of the target interactive smart device are mined from the big data of the target smart device's operation, and the mining results are determined as the device shutdown event information of the target interactive smart device. Alternatively, based on the configuration parameters of the web crawler for big data operation of the smart device and the stage of crawling the big data operation of the target smart device during the big data crawling process, the device shutdown event information of the target interactive smart device can be determined. The configuration parameters of the web crawler for the smart device's operational big data and the stages of crawling the target smart device's operational big data during the crawling process determine the device shutdown event information of the target interactive smart device, including at least one of the following: The configuration parameters of the web crawler for crawling big data of smart device operation are determined, and the configuration parameters are processed to obtain the device shutdown event information of the target interactive smart device. The stages of crawling the target smart device's operating big data are associated with the stage descriptions in the set device shutdown event information queue. The device shutdown events corresponding to the associated stage descriptions are determined as the device shutdown event information of the target interactive smart device. The step of determining the target intelligent device's operational big data and the target interactive intelligent device's device downtime event information as decision-making information for analyzing the causes of device downtime includes at least one of the following: Given that the target interactive smart device has corresponding device downtime event information in the target smart device operation big data, the target smart device operation big data and the corresponding device downtime event information are fused to obtain decision-making information for analyzing the causes of device downtime. If there is no corresponding device shutdown event information for the target interactive smart device in the target smart device operation big data, the target smart device operation big data is fused with the set device shutdown event information to obtain decision-making information for analyzing the causes of device shutdown. The method further includes: Identify the big data of intelligent device operation to be analyzed and mined; The big data of the operation of the intelligent device to be mined is associated with the big data of the operation of the target intelligent device in at least one of the decision-making information. Under the premise that the association is completed, the device shutdown event of the smart device to be analyzed is determined based on the device shutdown event information corresponding to the big data of the target smart device's operation. After determining the device shutdown event of the intelligent device to be analyzed, the method further includes: determining the semantic information of the intelligent device to be analyzed based on one or more of the following: the intelligent device operation big data to be mined, the device shutdown event of the intelligent device to be analyzed, and the determination stage of the intelligent device operation big data to be mined. The semantic information includes one or more of the following: the annotation stage, the annotation of the device shutdown event, and the annotation of the intelligent device operation big data.

2. The method as described in claim 1, characterized in that, The determination that at least one interactive smart device meets the first preset requirement includes at least one of the following: It was determined that the activation phase of at least one interactive smart device exceeded the set time constraint interval; Within the set judgment step size, multiple positioning results for at least one interactive smart device are found to be greater than the cumulative result judgment value.

3. The method as described in claim 2, characterized in that, The step of determining the target interactive smart device that meets the second set requirement from the not less than one interactive smart device based on the big data records of at least one of the devices includes: Analyze the usage time loss of interactive intelligent devices in the multimodal intelligent device operation information in at least one of the device operation big data records, and determine the interactive intelligent devices whose usage time loss is within the usage time loss judgment value as the target interactive intelligent devices. And / or, determine a global smart device operation big data evaluation of at least one device operation big data record corresponding to the interactive smart device, mark the device operation big data record whose global smart device operation big data evaluation reaches the set smart device operation big data evaluation index as the target device operation big data record, and determine the interactive smart device in the target device operation big data record as the target interactive smart device.

4. The method as described in claim 3, characterized in that, The device operation big data record that marks the global intelligent device operation big data evaluation as reaching the set intelligent device operation big data evaluation index is determined as the target device operation big data record, including: The device operation big data record with the highest global intelligent device operation big data evaluation, and / or the device operation big data record whose global intelligent device operation big data evaluation exceeds the global intelligent device operation big data evaluation judgment value, is determined as the target device operation big data record.

5. The method as described in claim 1, characterized in that, The step of determining the target intelligent device operation big data corresponding to the target interactive intelligent device from the not less than one of the device operation big data records includes: From at least one of the device operation big data records, the X groups of smart device operation big data with the highest annotated smart device operation big data evaluation covering the target interactive smart device are determined as the target smart device operation big data, where X is a positive integer.

6. A big data downtime analysis system, characterized in that, It includes a processor and a memory; the processor and the memory are communicatively connected, and the processor is configured to read a computer program from the memory and execute it to implement the method described in any one of claims 1-5.

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