WO2022257392A1 - 数据检索预测方法、装置、电子设备及可读介质 - Google Patents

数据检索预测方法、装置、电子设备及可读介质 Download PDF

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Publication number
WO2022257392A1
WO2022257392A1 PCT/CN2021/136264 CN2021136264W WO2022257392A1 WO 2022257392 A1 WO2022257392 A1 WO 2022257392A1 CN 2021136264 W CN2021136264 W CN 2021136264W WO 2022257392 A1 WO2022257392 A1 WO 2022257392A1
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storage
impact event
data
operation object
time period
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PCT/CN2021/136264
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English (en)
French (fr)
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杨春燕
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浙江宇视科技有限公司
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Priority to EP21944888.3A priority Critical patent/EP4354314A1/en
Publication of WO2022257392A1 publication Critical patent/WO2022257392A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F16/71—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/51—Indexing; Data structures therefor; Storage structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2477—Temporal data queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/53—Querying
    • G06F16/532—Query formulation, e.g. graphical querying
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • the embodiments of the present application relate to the field of computer technology, for example, to a data retrieval prediction method, device, electronic equipment, and readable medium.
  • the system regularly polls and retrieves the images of all cameras within a defined time period, and stores the results in the database, memory, and disk respectively.
  • queries stored images first query the memory, then query the database, and finally query the disk.
  • the memory, database, and disk are layered to a certain extent, the bottleneck of database pressure is obvious in the case of high business concurrency, and the memory size is limited, which cannot resolve the system pressure brought by a large number of concurrent searches.
  • Embodiments of the present application provide a data retrieval prediction method, device, electronic equipment, and readable medium, so as to realize intelligent prediction of data storage conditions and reduce the concurrency pressure of the overall system.
  • an embodiment of the present application provides a data retrieval prediction method, the method includes:
  • the storage situation of the data object in the target query time period is predicted and determined.
  • the embodiment of the present application also provides a data retrieval and prediction device, which includes:
  • the storage path determination module is configured to determine the data storage path information used by the data object under the target query time period
  • the operation object determination module is configured to determine the target operation object experienced by the data object written from the source end to the destination end from the data storage path information;
  • the data storage prediction module is configured to predict and determine the storage situation of the data object in the target query time period by analyzing the storage impact event on the target operation object.
  • an electronic device including:
  • At least one processing device At least one processing device
  • a storage device configured to store at least one program
  • the at least one processing device When the at least one program is executed by the at least one processing device, the at least one processing device implements the data retrieval prediction method in the embodiment of the present application.
  • the embodiment of the present application further provides a computer-readable medium on which a computer program is stored, and when the program is executed by the processing device, the data retrieval and prediction method in the embodiment of the present application is implemented.
  • Fig. 1 is a flow chart of a data retrieval prediction method provided in the embodiment of the present application
  • Fig. 2 is a flow chart of another data retrieval prediction method provided in the embodiment of the present application.
  • FIG. 3 is a structural block diagram of a data retrieval and prediction device provided in an embodiment of the present application.
  • Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
  • FIG. 1 is a flowchart of a data retrieval prediction method provided in an embodiment of the present application.
  • the technical solution of this embodiment is applicable to the situation of querying the data storage situation in the storage device due to data requirements.
  • the method can be executed by a data retrieval and prediction device, which can be implemented in the form of software and/or hardware, and integrated on any electronic device with network communication function.
  • the data retrieval prediction method in the embodiment of the present application may include the following steps:
  • the front-end acquisition device can collect the captured image data, and write the collected image data into the storage device through network transmission to realize the storage operation of the captured image; for example, the front-end camera IPC (Internet Protocol Camera, network camera) collected video data is written in the storage device through network transmission.
  • the front-end acquisition device is used as the starting point, and the storage terminal device is used as the destination point.
  • the data stream of the data object can only be realized through the network and device nodes, units, modules and various operations in the security system. storage.
  • the image code stream of the captured image passes through the front-end acquisition device, switch device, and storage device in sequence from the source end to the destination end.
  • Object predefinition for all nodes, units, modules and operations in the security system can be defined as operation objects, as shown in Table 1 .
  • operation objects as shown in Table 1 .
  • a unique object number can be set for each of the multiple predefined operational objects, and each operational object can be described to distinguish multiple operational objects.
  • the data code of the data object flows through at least one operation object that the source end reaches the destination end in the security system for storage, forming a corresponding data storage path; for example, one of the data storage path information shown in Table 2
  • a corresponding path code may be set for each preset data storage path under the data storage path information, and each data storage path may include at least two operation objects.
  • the data storage path information used by the data object at different times may be the same or different, that is, the operation objects experienced by the data object from the source end to the destination end in different time periods may be fixed.
  • the data object Part of the operation objects experienced by the object from the source end to the destination end in different time periods may also be different.
  • the data storage path information used by the data object in the target query time period involves multiple operation objects
  • different operation objects may have different impacts on the data storage in different query time periods. For example, some operation objects will affect the data storage. have a large impact, while some operation objects usually do not have a large impact on data storage.
  • the operation object that satisfies the preset influence conditions experienced by the data object written from the source end to the destination end during the target query time period can be selected as the target operation object to avoid
  • the storage of invalid operation objects affects event analysis and wastes analysis resources, making data retrieval prediction more accurate.
  • the operation objects experienced by the data object from the source end to the destination end during the target query time period may include at least two of the following: front-end acquisition equipment, server, switch equipment, storage end equipment, storage End device grouping, disk, array, user start and stop storage operations, and alarm linkage start and stop storage operations.
  • the data code stream of the data object is written from the source to the destination.
  • the target operation object is a necessary item for data storage. If a storage impact event occurs on the target operation object during the data storage process, it usually affects the storage process of the data object, resulting in an exception when the data object is written from the source to the data storage of the destination. Therefore, capture the impact of the target operation object on data storage, and then analyze whether the target operation object experienced by the data object written from the source to the destination will have a storage impact event during the target query time period to predict the target query time Storage of data objects under the segment.
  • the storage-impacting event occurring on the target operation object may include at least one of network disconnection, power failure, device offline restart, service offline restart, and user operation.
  • the data retrieval prediction method in the data retrieval scenario, determine the data storage path information used by the data object in the target query time period, and determine the data object in the target query time period indicated by the data storage path information Write the target operation object experienced by the destination from the source, and predict the storage status of the data object in the target query time period by analyzing whether the target operation object is disturbed by storage-affected events, without directly checking the stored data object itself Instead, use the operation objects experienced by data objects written from the source to the destination to analyze storage impact events to predict the data storage situation and disperse the system business pressure brought by a large number of concurrent data retrievals.
  • Fig. 2 is a flow chart of another data retrieval prediction method provided in the embodiment of the present application.
  • the embodiments of the present application are refined on the basis of the above embodiments, and the embodiments of the present application may be combined with various optional solutions in at least one of the foregoing embodiments.
  • the data retrieval prediction method provided in the embodiment of the present application may include the following steps:
  • the impact event configuration information includes storage impact event records that affect data storage when data objects are written from the source end to the destination end, and the storage impact event records include operation object identifiers that affect data storage, impact event descriptions, and start and end times of impact events.
  • a set of information that may affect data storage is pre-defined, which is recorded as impact event configuration information.
  • a storage impact event record will be generated for each operation object that may affect the storage of the data object, and the generated storage impact event record will be added to to the impact event configuration information.
  • the record field in each storage impact event record may include the event number of the storage impact event, the identifier of the operation object affecting data storage, the description of the impact event, and the start and end time of the impact event.
  • the data capture technology can be used to capture the impact of multiple operating objects in the security system on data storage in real time, and update the impact event configuration information in real time based on the capture results; for example, data capture can be performed on log records.
  • the storage impact event analysis is performed on the target operation object experienced by writing from the source end to the destination end, including steps A1-A2:
  • Step A1 if the query target operation object exists in the impact event configuration information, determine the start and end time periods of the impact event of the target operation object recorded in the storage impact event record of the impact event configuration information.
  • Step A2 If the start and end time period of the impact event of the target operation object overlaps with the target query time period, it is predicted that there is abnormal storage of the data object under the coincident time period.
  • the front-end acquisition equipment is in the normal acquisition and storage.
  • the data object is written from the source end to the destination end under the target query time period. All target operands experienced.
  • a traversal or directional query is performed on the impact event configuration information to determine whether the impact event configuration information includes a storage impact event record corresponding to the target operation object. If the storage impact event record corresponding to the target operation object is included, query the start and end time periods of the impact event of the target operation object recorded in the storage impact event record of the impact event configuration information. If the storage impact event record corresponding to the target operation object is not included, it is considered that the target operation object has no storage impact, and correspondingly there is no abnormal storage of the data object in the target query time period.
  • start and end time periods of the impact events of each target operation object For the start and end time periods of the impact events of each target operation object, compare the start and end time periods of the impact events of the target operation object with the target query time period, and determine whether the two time periods overlap. If the start and end time period of the impact event of the target operation object overlaps with the target query time period, it is predicted that there is an abnormal storage of the data object under the overlapping time period, indicating that there have been conditions affecting data storage in this overlapping time period, then determine There is abnormal storage of data objects in the overlapping time period of the target query time period, and it is determined that there is no stored data object in the overlapping time period.
  • start and end time period of the impact event of the target operation object does not overlap with the target query time period, it is determined that there is no abnormal storage of the data object under the target query time period, and it is determined that there is a stored data object in the target query time period.
  • the storage impact event analysis is performed on the target operation object experienced by writing from the source end to the destination end, including steps B1-B2:
  • Step B1 If the query impact event configuration information records the start and end time period of the impact event that matches the target query time period, then query the impact event start and end time period from the storage impact event record of the impact event configuration information and the target query time period. corresponding operation object.
  • Step B2 If the start and end time period of the impact event overlaps with the target query time period, and the operation object corresponding to the target operation object overlaps with the target operation object, predict that there is abnormal storage of the data object under the overlapping time period.
  • the analysis result of the storage impact event on the target operation object may include that the target operation object has a storage impact event in the time period in which the start and end time period of the impact event partially overlaps with the target query time period, causing at least part of the time period of the target query time period There is abnormal storage of the data object under the segment; or, the target operation object does not have an impact event start and end time period or the target operation object’s impact event start and end time does not overlap with the target query time period, and there is no abnormality in the data object under the target query time period storage.
  • the data retrieval prediction method provided in the embodiment of this application may further include the following steps:
  • the bystander device and the operation object affected by data storage are not in the same data storage path, and the two cannot watch each other.
  • a bystander is defined as a subject in a non-stored process that does not participate in the stored process, but is directly related to the subject of the store, that is, the operation object.
  • a data storage path is IPC-switch 1-switch 2-server 1-storage device 1
  • the bystander of the operation object of switch 2 can be the server 5 connected to it, and let server 5 confirm whether the switch 2 is running normally , such as whether the communication is normal.
  • a spectator device and a spectator event are predefined for each operation object in the preset data storage path.
  • the operation objects located in the same data storage path do not observe each other, or the observation device and the operation object affected by data storage are not in the same data storage path, and the observation device and the operation object affected by data storage do not observe each other (that is, with reference to the example in the previous paragraph, after the server 5 is used as the bystander of the operation object of the switch 2 connected to it, the switch 2 cannot be used as the bystander of the server 5), to increase credibility.
  • the storage impact event record of the operation object with abnormal storage in the impact event configuration information is updated and corrected through the bystander device associated with the operation object with data storage impact. For example, as shown in Table 5 below, when updating and correcting, the number of the operation object, the number of the bystander, and the number of confirmation information of the operation object by the bystander will be recorded.
  • the bystander mechanism uses the bystander mechanism to correct the results of the impact event configuration information. If within the impact event start time of the stored impact event record, the bystander’s determination result of the operation object of the stored impact event record is different from the result recorded in the impact event configuration information If it matches, it is considered that there is no video storage in this period of time. If it is contrary to the determined result obtained, a real data query is started, and the query result is taken as the final result. For example, if there is a storage impact event record indicating that a certain server is disconnected, and a bystander of the server also records that the server loses communication during the same time period, the result is considered to be consistent, and then it is determined that there is no image data storage in this time period. On the contrary, if it does not match, start an image data query of this time period to confirm the result.
  • updating and correcting the storage impact event record of the target operation object with data storage impact in the impact event configuration information through a bystander device associated with the target operation object with data storage impact includes:
  • the determination result of the target operation object corresponding to the stored impact event record by the bystander device is consistent with the result recorded in the impact event configuration information, and the impact event start time There is no video storage in the affected event configuration information, and there is no need to update and correct the storage impact event record in the impact event configuration information; If the recorded result is opposite, start a real data query, take the query result as the final result, and determine whether to update and correct the storage impact event record in the impact event configuration information according to the final result.
  • the credibility of the storage impact event records does not require bystanders to authenticate. Directly determine that the information queried in the storage impact event record is credible.
  • Adopt the above scheme use the bystander confirmation mechanism and the impact event continuous supplement mechanism, use the bystander to confirm the storage impact event records recorded in the impact event configuration information, and continuously learn and update the storage impact event records in the impact event configuration information , to make the forecast more accurate.
  • the data retrieval prediction method provided in the embodiment of this application may further include the following steps:
  • the new operation object that causes data storage impact is determined through data retrieval sampling, and a new storage impact event record is added to the impact event configuration information.
  • the sampling algorithm can meet the requirements of not increasing the system pressure, and can cover all camera IPCs within a period of time. For example, select the time period when the system is relatively idle, and perform batch data query on the data of the important time period of the camera (for example, the important time period of school monitoring is going to school and leaving school, etc.). If no video recording time period is found in the random inspection, you need to find the reason (it can be manually located and recorded), and at the same time add the storage impact event records found due to the impact of new data storage to the impact event configuration information to accumulate more Influence event configuration information and improve the accuracy of subsequent retrieval predictions. In the initial stage, the configuration can follow the cameras in the same area, and the preset data storage paths should be as different as possible to reduce the impact of a single area failure.
  • the data retrieval prediction method determine the target operation object experienced by the data object from the source end to the destination end under the target query time period, and predict the target query time period by analyzing whether the target operation object has storage impact
  • For the storage of data objects it is not necessary to directly retrieve the stored data objects themselves, but to use the operation objects experienced by the data objects from the source end to the destination end to analyze the storage impact events to predict the data storage situation, and to disperse and resolve a large number of data objects.
  • the impact event configuration information of each node, level, and module of the system combined with the data storage impact of multiple operation objects in the entire storage path from the storage source to the destination, the data storage situation is predicted, and bystanders are used to confirm Mechanism and continuous learning update the impact event configuration information to make the prediction more accurate.
  • Fig. 3 is a structural block diagram of a device for data retrieval and prediction provided in an embodiment of the present application.
  • the technical solution of this embodiment is applicable to the situation of querying the data storage situation in the storage device due to data requirements.
  • the device can be implemented in the form of software and/or hardware, and can be integrated on any electronic device with network communication function.
  • the data retrieval prediction device in the embodiment of the present application may include: a storage path determination module 310 , an operation object determination module 320 and a data storage prediction module 330 . in:
  • the storage path determination module 310 is configured to determine the data storage path information used by the data object in the target query time period.
  • the operation object determination module 320 is configured to determine the target operation object through which the data object is written from the source end to the destination end from the data storage path information.
  • the data storage prediction module 330 is configured to predict and determine the storage situation of the data object in the target query time period by analyzing the storage impact event on the target operation object.
  • the data storage prediction module 330 implements storage impact event analysis on the target operation object in the following manner to predict and determine the storage situation of the data object in the target query time period:
  • the impact event configuration information includes storage impact event records that affect data storage when data objects are written from the source end to the destination end, and the storage impact events include operation object identifiers that indicate impact data storage, impact event descriptions, and impact event start and end times.
  • the storage impact event analysis is performed on the target operation object experienced by writing from the source to the destination, including:
  • the operation objects experienced by writing from the source to the destination may include front-end acquisition devices, servers, switch devices, storage devices, storage device groups, disks, arrays, user start and stop storage operations, and alarm linkage start and stop At least two of the storage operations.
  • the storage impact event includes at least one of network disconnection, power failure, device offline restart, service offline restart, and user operation.
  • the device further includes an impact event configuration information correction module;
  • the impact event configuration information correction module is set to update and correct the storage impact event record of the operation object with data storage impact in the impact event configuration information through the bystander device associated with the operation object with data storage impact;
  • the bystander device and the operation object affected by data storage are not in the same data storage path, and the two do not watch each other.
  • the device also includes a data retrieval sampling module
  • the data retrieval sampling module is set to determine the new operation object that causes data storage impact through the data retrieval sampling method during the non-high concurrency time period predicted by data retrieval, and form a new storage impact event record to add to the impact event configuration information.
  • the impact event configuration information correction module implements updating the storage impact event record of the operation object with data storage impact in the impact event configuration information through a bystander device associated with the operation object with data storage impact in the following manner fix:
  • the determination result of the operation object corresponding to the stored impact event record by the bystander device is consistent with the result recorded in the impact event configuration information, and the impact event start time No video storage is performed, and there is no need to update and correct the storage impact event record in the impact event configuration information; in response to the determination result of the bystander device on the operation object corresponding to the storage impact event record and the record in the impact event configuration information On the contrary, a real data query is started, the query result is taken as the final result, and whether to update and correct the storage impact event record in the impact event configuration information is determined according to the final result.
  • the data retrieval and prediction device provided in the embodiment of the present application can execute the data retrieval and prediction method provided in any embodiment of the above application, and has the corresponding functions for executing the data retrieval and prediction method, and the technologies that are not described in detail in the above embodiments For details, refer to the data retrieval prediction method provided in any embodiment of the present application.
  • Fig. 4 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
  • the electronic equipment provided in the embodiment of the present application includes: at least one processor 410 and a storage device 420; there may be at least one processor 410 in the electronic equipment, and one processor 410 is used in Figure 4 as Example; the storage device 420 is set to store at least one program; the at least one program is executed by the at least one processor 410, so that the at least one processor 410 realizes the data retrieval as described in any one of the embodiments of the present application method of prediction.
  • the electronic device may further include: an input device 430 and an output device 440 .
  • the processor 410, the storage device 420, the input device 430 and the output device 440 in the electronic device may be connected via a bus or in other ways.
  • connection via a bus is taken as an example.
  • the storage device 420 in the electronic device can be configured to store at least one program, and the program can be a software program, a computer-executable program, and a module, such as the data provided in the embodiment of this application retrieves the program instruction/module corresponding to the prediction method.
  • the processor 410 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the storage device 420 , that is, implements the data retrieval prediction method in the above method embodiments.
  • the storage device 420 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store data created according to the use of the electronic device, and the like.
  • the storage device 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices.
  • the storage device 420 may further include memory located remotely relative to the processor 410, and these remote memories may be connected to the device through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the input device 430 may be configured to receive input numbers or character information, and generate key signal input related to user settings and function control of the electronic device.
  • the output device 440 may include a display device such as a display screen.
  • the storage situation of the data object in the target query time period is predicted and determined.
  • An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it is used to perform a data retrieval prediction method, the method comprising:
  • the storage situation of the data object in the target query time period is predicted and determined.
  • the program when executed by the processor, it can also be used to execute the data retrieval and prediction method provided in any embodiment of the present application.
  • the computer storage medium in the embodiments of the present application may use any combination of at least one computer-readable medium.
  • the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
  • a computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
  • Computer-readable storage media include: electrical connections having at least one lead, portable computer disks, hard disks, Random Access Memory (RAM), Read Only Memory (Read Only) Only Memory, ROM), Erasable Programmable Read Only Memory (Erasable Programmable Read Only Memory, EPROM), flash memory, optical fiber, portable CD-ROM (Compact Disc-Read Only Memory, CD-ROM), optical storage devices, magnetic A storage device, or any suitable combination of the above.
  • a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with the instruction execution system, apparatus, or device.
  • a computer readable signal medium may include a data signal carrying computer readable program code in baseband or as part of a carrier wave. Such propagated data signals may take many forms, including but not limited to: electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wires, optical cables, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • any appropriate medium including but not limited to: wireless, wires, optical cables, radio frequency (Radio Frequency, RF), etc., or any suitable combination of the above.
  • Computer program code for performing the operations of the present application may be written in one or more programming languages or combinations thereof, including object-oriented programming languages—such as Java, Smalltalk, C++, and conventional Procedural Programming Language - such as "C" or a similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
  • the remote computer can be connected to the user computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (e.g. using an Internet Service Provider to connect via the Internet).
  • LAN Local Area Network
  • WAN Wide Area Network
  • Internet Service Provider to connect via the Internet

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Abstract

本申请实施例公开了一种数据检索预测方法、装置、电子设备及可读介质。所述方法包括：确定目标查询时间段下数据对象所使用的数据存储路径信息；从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。

Description

数据检索预测方法、装置、电子设备及可读介质
本申请要求在2021年6月9日提交中国专利局、申请号为202110642775.9的中国专利申请的优先权，该申请的全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及计算机技术领域，例如涉及一种数据检索预测方法、装置、电子设备及可读介质。
背景技术
当下对安防系统及数据的重视程度越来越高，安防系统向着超大型的方向发展，动辄几万路的摄像机规模。在一些情况下，例如图像检索过程中，通常需要大规模并发查询存储图像以及智能历史数据分析的过程中，系统承载着图像检索的巨大业务压力。
相关技术中，系统定时轮询检索一遍所有摄像机定义时间段内的图像，将结果分别存放于数据库、内存以及磁盘中。在查询存储图像时，先查内存、其次查询数据库以及最后查询磁盘。虽然通过内存、数据库以及磁盘进行了一定分层，但在高业务并发的情况下数据库压力瓶颈显著，内存大小有限，都无法化解大量并发检索带来的系统压力。
发明内容
本申请实施例中提供了一种数据检索预测方法、装置、电子设备及可读介质，以实现对数据存储情况进行智能预判，减小整体系统的并发压力。
第一方面，本申请实施例中提供了一种数据检索预测方法，所述方法包括：
确定目标查询时间段下数据对象所使用的数据存储路径信息；
从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
第二方面，本申请实施例中还提供了一种数据检索预测装置，该装置包括：
存储路径确定模块，设置为确定目标查询时间段下数据对象所使用的数据存储路径信息；
操作对象确定模块，设置为从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
数据存储预测模块，设置为通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
第三方面，本申请实施例中还提供了一种电子设备，包括：
至少一个处理装置；
存储装置，设置为存储至少一个程序；
当所述至少一个程序被所述至少一个处理装置执行，使得所述至少一个处理装置实现本申请实施例中的数据检索预测方法。
第四方面，本申请实施例中还提供了一种计算机可读介质，其上存储有计算机程序，该程序被处理装置执行时实现本申请实施例中的数据检索预测方法。
附图说明
图1是本申请实施例中提供的一种数据检索预测方法的流程图；
图2是本申请实施例中提供的另一种数据检索预测方法的流程图；
图3是本申请实施例中提供的一种数据检索预测装置的结构框图；
图4是本申请实施例中提供的一种电子设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作详细说明。
在更加详细地讨论示例性实施例之前，应当提到的是，一些示例性实施例 被描述成作为流程图描绘的处理或方法。虽然流程图将各项操作(或步骤)描述成顺序的处理，但是其中的许多操作(或步骤)可以被并行地、并发地或者同时实施。此外，各项操作的顺序可以被重新安排。当其操作完成时所述处理可以被终止，但是还可以具有未包括在附图中的附加步骤。所述处理可以对应于方法、函数、规程、子例程、子程序等等。
下面通过各实施例及各实施例的可选方案，对本申请方案中提供的数据检索预测方法、装置、电子设备以及可读介质进行详细阐述。
图1是本申请实施例中提供的一种数据检索预测方法的流程图。本实施例的技术方案可适用于因数据需求查询存储设备中的数据存储情况的情形。该方法可由数据检索预测装置执行，该装置可以采用软件和/或硬件的方式实现，并集成在任何具有网络通信功能的电子设备上。如图1所示，本申请实施例中的数据检索预测方法，可包括以下步骤：
S110、确定目标查询时间段下数据对象所使用的数据存储路径信息。
S120、从数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象。
在安防系统中，以数据对象为拍摄图像为例，前端采集设备可采集拍摄图像数据，并且将采集的图像数据通过网络传输写入存储端设备，实现拍摄图像的存储操作；比如，前端摄像机IPC(Internet Protocol Camera，网络摄像机)采集的视频数据通过网络传输写入存储设备中。可选地，在进行数据存储时，将前端采集设备作为起点，将存储端设备作为目地点，数据对象的数据码流经过安防系统中网络与设备节点、单元、模块以及各种操作才能实现数据存储。例如，拍摄图像的图像码流从源端到目的端依次经过前端采集设备、交换机设备以及存储端设备。
针对安防系统中的所有节点、单元、模块以及操作进行对象预定义。例如可将安防系统中涉及的服务器设备、交换机设备、存储端设备、存储端设备分组、磁盘、阵列、用户启停存储操作以及告警联动启停存储操作等定义为操作 对象，如表1所示。同时，针对预定义的多个操作对象可为每一个操作对象设定唯一的对象编号，并对每个操作对象进行描述，以对多个操作对象进行区分。
表1操作对象预定义
对象编号 对象描述
001 IPC
002 交换机
003 路由器
004 服务器
005 存储设备
006 阵列
007 磁盘
008 用户启停存储操作
009 告警联动启停存储
…… ……
在进行数据存储时，数据对象的数据码流经安防系统中源端到达目的端进行存储所经过的至少一个操作对象，形成对应的数据存储路径；比如表2所示的数据存储路径信息中一个数据存储路径的示例，针对数据存储路径信息下每一个预设数据存储路径可设置对应的路径编码，以及每个数据存储路径下可以包括至少两个操作对象。
表2数据存储路径信息
Figure PCTCN2021136264-appb-000001
可选地，数据对象在不同时间下所使用的数据存储路径信息可以相同也可以不相同，即数据对象在不同时间段从源端写入目的端所经历的操作对象可以固定不变，当然数据对象在不同时间段从源端写入目的端所经历的部分操作对象也可以是不同的。
从数据存储路径信息指示的多个操作对象中，选取数据对象从源端写入目 的端所经历的操作对象，这里记为目标操作对象。考虑到目标查询时间段下数据对象所使用的数据存储路径信息涉及到多个操作对象，在不同查询时间段不同操作对象对数据存储产生的影响可能不相同，例如有一些操作对象对数据存储会产生较大影响，而有一些操作对象对数据存储通常不会产生很大影响。可选地，可从数据存储路径信息指示的多个操作对象中选取目标查询时间段下数据对象从源端写入目的端所经历的满足预设影响条件的操作对象作为目标操作对象，避免对无效操作对象的存储影响事件分析浪费分析资源，使得数据检索预测更加准确。
作为一种可选方案，目标查询时间段下数据对象从源端写入目的端所经历的操作对象可包括以下内容中的至少两项：前端采集设备、服务器、交换机设备、存储端设备、存储端设备分组、磁盘、阵列、用户启停存储操作以及告警联动启停存储操作。
S130、通过对目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
在目标查询时间段下数据对象的数据码流从源端写入目的端所经历的多个操作对象中，目标操作对象是数据存储所需要经历的必须项。如果数据存储过程中目标操作对象发生存储影响事件，那么通常会对数据对象的存储过程产生影响，导致数据对象从源端写入目的端的数据存储出现异常。因此，捕捉目标操作对象的对数据存储产生影响的情况，进而分析数据对象从源端写入目的端所经历的目标操作对象在目标查询时间段内是否会出现存储影响事件，来预测目标查询时间段下数据对象的存储情况。
作为一种可选方案，可以预先定义数据存储过程中多个操作对象对数据存储造成影响的各种影响因素，例如包括但不限于断网、断电、设备重启、服务重启、用户的某种操作等影响因素，如表3所示。在此基础上，可对操作对象进行存储影响事件分析，确定操作对象在查询时间段内是否出现预定义影响因素对应的存储影响事件。对于目标操作对象而言，目标操作对象出现的存储影 响事件可包括断网、断电、设备下线重启、服务下线重启以及用户操作中的至少一项。
表3操作对象的影响因素预定义
预设影响因素编号 预设影响因素描述
10001 断电
10002 断网
10003 死机
10004 ……
10005 磁盘下线
10006 ……
10007 ……
10008 XX服务down
10009 用户操作1
…… ……
根据本申请实施例中提供的数据检索预测方法，在数据检索场景下，确定目标查询时间段下数据对象所使用的数据存储路径信息，并确定数据存储路径信息指示的目标查询时间段下数据对象从源端写入目的端所经历的目标操作对象，通过分析目标操作对象是否存在受到存储影响事件的干扰，来预测目标查询时间段下数据对象的存储情况，不需要直接对存储的数据对象本身进行检索，而是利用数据对象从源端写入目的端所经历的操作对象进行存储影响事件的分析来预测数据存储情况，分散化解大量并发数据检索带来的系统业务压力。
图2是本申请实施例中提供的另一种数据检索预测方法的流程图。本申请实施例在上述实施例的基础上进行细化，本申请实施例可以与上述至少一个实施例中各个可选方案结合。如图2所示，本申请实施例中提供的数据检索预测方法，可包括以下步骤：
S210、确定目标查询时间段下数据对象所使用的数据存储路径信息。
S220、从数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象。
S230、基于预先更新存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析。
其中，影响事件配置信息中包括对数据对象从源端写入目的端产生数据存储影响的存储影响事件记录，存储影响事件记录包括影响数据存储的操作对象标识、影响事件描述以及影响事件起止时间。
针对安防系统中的所有节点、单元、模块以及操作，预先定义一个所有可能影响到数据存储的信息集合，记为影响事件配置信息。如表4所示，在数据对象从源端写入目的端过程中，对于每一个可能影响到数据对象进行存储的操作对象均会生成一条存储影响事件记录，并将生成的存储影响事件记录添加到影响事件配置信息中。其中，每一条存储影响事件记录中记录字段可包括存储影响事件的事件编号、影响数据存储的操作对象标识、影响事件描述以及影响事件起止时间。
表4影响事件配置信息表
Figure PCTCN2021136264-appb-000002
在获取预先存储的影响事件配置信息后，可查询影响事件配置信息中记录的多个存储影响事件记录，通过查询多个存储影响事件记录确定从源端写入目 的端所经历的目标操作对象是否在影响事件配置信息进行记录，实现对从源端写入目的端所经历的目标操作对象进行存储影响事件分析。可选地，可通过数据捕捉技术实时捕捉安防系统中多个操作对象的对数据存储的影响情况，并基于捕捉结果实时对影响事件配置信息进行更新；比如可对日志记录进行数据捕捉。
在本实施例的一种可选方案中，基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析，包括步骤A1-A2：
步骤A1、若查询目标操作对象存在于影响事件配置信息中，则确定影响事件配置信息的存储影响事件记录中记录的目标操作对象的影响事件起止时间段。
步骤A2、若目标操作对象的影响事件起止时间段与目标查询时间段存在重合时间段，则预测重合时间段下数据对象存在异常存储。
对于安防系统而言前端采集设备均处于正常采集存储中，在用户或者智能系统按照所需查询要求的查询时间进行数据检索时，查询到目标查询时间段下数据对象从源端写入目的端所经历的所有目标操作对象。进而，对影响事件配置信息进行遍历或定向查询，确定影响事件配置信息中是否包括目标操作对象对应的存储影响事件记录。若包括目标操作对象对应的存储影响事件记录，则查询影响事件配置信息的存储影响事件记录中记录的目标操作对象的影响事件起止时间段。若不包括目标操作对象对应的存储影响事件记录，则认为目标操作对象不存在存储影响，相应地目标查询时间段下数据对象不存在异常存储。
针对每一个目标操作对象的影响事件起止时间段，对目标操作对象的影响事件起止时间段和目标查询时间段进行比对，判断两个时间段之间是否重合时间段。若目标操作对象的影响事件起止时间段与目标查询时间段存在重合时间段，则预测该重合时间段下数据对象存在异常存储，表明在此重合时间段中出现过影响数据存储的条件，则确定在目标查询时间段的该重合时间段下数据对象存在异常存储，判定这个重合时间段内无存储的数据对象。若目标操作对象的影响事件起止时间段与目标查询时间段不存在重合时间段，则确定在目标查 询时间段下数据对象不存在异常存储，判定这个目标查询时间段内存在存储的数据对象。
在本实施例的另一可选方案中，基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析，包括步骤B1-B2：
步骤B1、若查询影响事件配置信息中记录与目标查询时间段匹配的影响事件起止时间段，则从影响事件配置信息的存储影响事件记录查询影响事件起止时间段与目标查询时间段存在重合时间段对应的操作对象。
步骤B2、若影响事件起止时间段与目标查询时间段存在重合时间段对应的操作对象与目标操作对象存在重合对象，则预测重合时间段下数据对象存在异常存储。
采用上述方案，利用系统各节点、层次、模块的预定义影响因素对应的存储影响事件，结合数据对象从存储源端到目的端整条路径中多个目标操作对象出现的预定义存储影响事件来预测数据存储情况，使得数据检索预测结果趋向准确，分散系统高并发数据检索造成的业务压力，也能更直观的确认数据存储情况。
S240、依据对目标操作对象的存储影响事件分析结果，预测确定目标查询时间段下数据对象的存储情况。
参见表4，对目标操作对象的存储影响事件分析结果可包括目标操作对象在影响事件起止时间段与目标查询时间段存在部分重合时间段内存在存储影响事件，造成目标查询时间段的至少部分时间段下数据对象存在异常存储；或者，目标操作对象不存在影响事件起止时间段或者目标操作对象的影响事件起止时间与目标查询时间段不存在重合时间段，目标查询时间段下数据对象不存在异常存储。
在本实施例的一种可选方案中，本申请实施例中提供的数据检索预测方法还可包括以下步骤：
通过与存在数据存储影响的操作对象关联的旁观设备，对影响事件配置信 息中存在数据存储影响的操作对象的存储影响事件记录进行更新修正。
可选地，旁观设备与存在数据存储影响的操作对象不在相同数据存储路径，且两者之间不能互相旁观。
在对存储数据对象进行检索预测时，预测机制具有小概率的不确定性，故引入旁观者确认机制进行一次结果是非确认，形成双向认定。旁观者定义为非存储流程中的主体，不参与存储过程，但是与存储当事主体，即与操作对象有直接的关联。例如，一个数据存储路径是IPC—交换机1—交换机2—服务器1—存储设备1，那么交换机2这个操作对象的旁观者可以是与它相连的服务器5，让服务器5来确认交换机2是否运行正常，比如是否通信正常。
针对每一个预设数据存储路径中的操作对象预定义一个旁观设备以及旁观事件。可选地，位于同一数据存储路径的操作对象不互相旁观，或者旁观设备与存在数据存储影响的操作对象不在同一个数据存储路径，且旁观设备与存在数据存储影响的操作对象之间不互相旁观(即参照上一段中的例子，以服务器5作为与之相连的交换机2这个操作对象的旁观者后，交换机2不可以作为服务器5的旁观者)，以增加可信度。通过与存在数据存储影响的操作对象关联的旁观设备，对影响事件配置信息中存在异常存储的操作对象的存储影响事件记录进行更新修正。例如，如下表5所示，在更新修正时，会记录操作对象编号、旁观者编号以及旁观者对操作对象的确认信息编号。
表5旁观事件配置信息
Figure PCTCN2021136264-appb-000003
Figure PCTCN2021136264-appb-000004
使用旁观机制对影响事件配置信息的结果进行修正，若在存储影响事件记录的影响事件起始时间内，旁观者对该存储影响事件记录的操作对象的确定结果与影响事件配置信息中记录的结果相符，则认为此时间段内没有进行录像存储。若与得到的确定结果相反，则启动一次真实数据查询，将查询结果作为最终结果。例如有存储影响事件记录中指示某个服务器断线了，且在相同时间段中服务器的旁观者也同时记录了服务器失去通信，则认为结果相符，那么就认定此时间段中无图像数据存储，反之不符时，则启动一次此时间段的图像数据查询确认结果。
可选的，所述通过与存在数据存储影响的目标操作对象关联的旁观设备，对影响事件配置信息中存在数据存储影响的目标操作对象的存储影响事件记录进行更新修正，包括：
响应于在存储影响事件记录的影响事件起始时间内，旁观设备对所述存储影响事件记录对应的目标操作对象的确定结果与影响事件配置信息中记录的结果相符，所述影响事件起始时间内没有进行录像存储，不需要对影响事件配置信息中的所述存储影响事件记录进行更新修正；响应于旁观设备对所述存储影响事件记录对应的目标操作对象的确定结果与影响事件配置信息中记录的结果相反，启动一次真实数据查询，将查询结果作为最终结果，并根据所述最终结果确定是否对影响事件配置信息中的所述存储影响事件记录进行更新修正。
可选地，在影响事件配置信息中，手动启停存储和告警联动启停存储产生的存储影响事件记录中不存在旁观者，其存储影响事件记录的可信度不需要旁观者进行认证，可直接判定存储影响事件记录中查询的信息是可信的。
采用上述方案，利用旁观者的确认机制和影响事件不断补充机制，利用旁观者对影响事件配置信息中记录的存储影响事件记录进行是非确认，以及不断学习更新影响事件配置信息中的存储影响事件记录，来使得预测趋于准确。
在本实施例的一种可选方案中，本申请实施例中提供的数据检索预测方法还可包括以下步骤：
在数据检索预测的非高并发时间段，通过数据检索抽检方式确定造成数据存储影响的新的操作对象，并形成一条新的存储影响事件记录添加到影响事件配置信息。
对认为有数据存储的时间段进行录像检索抽检，抽检算法可满足不增加系统压力，且一段时间内可覆盖全部摄像机IPC。例如，选择系统较空闲的时间段，对摄像机重要时间段(例如学校监控的重要时间段是上学和放学等)的数据进行分批次的数据查询。若在抽检中发现无录像时间段，则需要找到原因(可人工定位后记录)，同时将查找的由于新的数据存储影响形成的存储影响事件记录加入到影响事件配置信息中，以积累更多影响事件配置信息，提高后续检索预测的准确性。在初始阶段，配置时可遵循同一区域的相机，预设数据存储路径尽量不同，减少单区域故障带来的影响。
根据本申请实施例中提供的数据检索预测方法，确定目标查询时间段下数据对象从源端写入目的端所经历的目标操作对象，通过分析目标操作对象是否存在存储影响来预测目标查询时间段下数据对象的存储情况，不需要直接对存储的数据对象本身进行检索，而是利用数据对象从源端写入目的端所经历的操作对象进行存储影响事件分析来预测数据存储情况，分散化解大量并发数据检索带来的系统业务压力。此外，通过利用系统各节点、层次、模块的影响事件配置信息，结合存储源端到目的端整条存储路径中多个操作对象出现的数据存储影响情况来预测数据存储情况，以及利用旁观者确认机制和不断学习更新影响事件配置信息，来使得预测趋于准确。
图3是本申请实施例中提供的一种数据检索预测装置的结构框图。本实施例的技术方案可适用于因数据需求查询存储设备中的数据存储情况的情形。该装置可以采用软件和/或硬件的方式实现，并集成在任何具有网络通信功能的电子设备上。如图3所示，本申请实施例中的数据检索预测装置，可包括：存储 路径确定模块310、操作对象确定模块320和数据存储预测模块330。其中：
存储路径确定模块310，设置为确定目标查询时间段下数据对象所使用的数据存储路径信息。
操作对象确定模块320，设置为从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象。
数据存储预测模块330，设置为通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
可选地，数据存储预测模块330通过以下方式实现通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况：
基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析；
依据对目标操作对象的存储影响事件分析结果，预测确定目标查询时间段下数据对象的存储情况；
其中，影响事件配置信息中包括对数据对象从源端写入目的端产生数据存储影响的存储影响事件记录，存储影响事件包括指示影响数据存储的操作对象标识、影响事件描述以及影响事件起止时间。
可选地，基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析，包括：
若查询所述目标操作对象存在于影响事件配置信息中，则确定影响事件配置信息的存储影响事件记录中记录的所述目标操作对象的影响事件起止时间段；
若所述目标操作对象的影响事件起止时间段与目标查询时间段存在重合时间段，则预测重合时间段下数据对象存在异常存储。
可选地，从源端写入目的端所经历的操作对象可包括前端采集设备、服务器、交换机设备、存储端设备、存储端设备分组、磁盘、阵列、用户启停存储操作以及告警联动启停存储操作中的至少两项。
可选地，所述存储影响事件包括断网、断电、设备下线重启、服务下线重 启以及用户操作中的至少一项。
可选地，所述装置还包括影响事件配置信息修正模块；
所述影响事件配置信息修正模块，设置为通过与存在数据存储影响的操作对象关联的旁观设备，对影响事件配置信息中存在数据存储影响的操作对象的存储影响事件记录进行更新修正；
其中，旁观设备与存在数据存储影响的操作对象不在相同数据存储路径，且两者之间不互相旁观。
可选地，所述装置还包括数据检索抽检模块；
所述数据检索抽检模块，设置为在数据检索预测的非高并发时间段，通过数据检索抽检方式确定造成数据存储影响的新的操作对象，并形成一条新的存储影响事件记录添加到影响事件配置信息。
可选的，所述影响事件配置信息修正模块通过以下方式实现通过与存在数据存储影响的操作对象关联的旁观设备，对影响事件配置信息中存在数据存储影响的操作对象的存储影响事件记录进行更新修正：
响应于在存储影响事件记录的影响事件起始时间内，旁观设备对所述存储影响事件记录对应的操作对象的确定结果与影响事件配置信息中记录的结果相符，所述影响事件起始时间内没有进行录像存储，不需要对影响事件配置信息中的所述存储影响事件记录进行更新修正；响应于旁观设备对所述存储影响事件记录对应的操作对象的确定结果与影响事件配置信息中记录的结果相反，启动一次真实数据查询，将查询结果作为最终结果，并根据所述最终结果确定是否对影响事件配置信息中的所述存储影响事件记录进行更新修正。
本申请实施例中所提供的数据检索预测装置可执行上述本申请任意实施例中所提供的数据检索预测方法，具备执行该数据检索预测方法相应的功能，未在上述实施例中详尽描述的技术细节，可参见本申请任意实施例中所提供的数据检索预测方法。
图4是本申请实施例中提供的一种电子设备的结构示意图。如图4所示结 构，本申请实施例中提供的电子设备包括：至少一个处理器410和存储装置420；该电子设备中的处理器410可以是至少一个，图4中以一个处理器410为例；存储装置420设置为存储至少一个程序；所述至少一个程序被所述至少一个处理器410执行，使得所述至少一个处理器410实现如本申请实施例中任一项所述的数据检索预测方法。
该电子设备还可以包括：输入装置430和输出装置440。
该电子设备中的处理器410、存储装置420、输入装置430和输出装置440可以通过总线或其他方式连接，图4中以通过总线连接为例。
该电子设备中的存储装置420作为一种计算机可读存储介质，可设置为存储至少一个程序，所述程序可以是软件程序、计算机可执行程序以及模块，如本申请实施例中所提供的数据检索预测方法对应的程序指令/模块。处理器410通过运行存储在存储装置420中的软件程序、指令以及模块，从而执行电子设备的各种功能应用以及数据处理，即实现上述方法实施例中数据检索预测方法。
存储装置420可包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据电子设备的使用所创建的数据等。此外，存储装置420可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储装置420可进一步包括相对于处理器410远程设置的存储器，这些远程存储器可以通过网络连接至设备。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置430可设置为接收输入的数字或字符信息，以及产生与电子设备的用户设置以及功能控制有关的键信号输入。输出装置440可包括显示屏等显示设备。
并且，当上述电子设备所包括的至少一个程序被所述至少一个处理器410执行时，程序进行如下操作：
确定目标查询时间段下数据对象所使用的数据存储路径信息；
从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
当然，本领域技术人员可以理解，当上述电子设备所包括至少一个程序被所述至少一个处理器410执行时，程序还可以进行本申请任意实施例中所提供的数据检索预测方法中的相关操作。
本申请实施例中提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时用于执行数据检索预测方法，该方法包括：
确定目标查询时间段下数据对象所使用的数据存储路径信息；
从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
可选的，该程序被处理器执行时还可以用于执行本申请任意实施例中所提供的数据检索预测方法。
本申请实施例的计算机存储介质，可以采用至少一个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有至少一个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(Random Access Memory，RAM)、只读存储器(Read Only Memory，ROM)、可擦式可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、闪存、光纤、便携式只读光盘(Compact Disc-Read Only Memory，CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。计算机可读存储介质可以是任何包含或存储程序的 有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于：电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、无线电频率(RadioFrequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本申请操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(Local Area Network，LAN)或广域网(Wide Area Network，WAN)——连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。

Claims (11)

  1. 一种数据检索预测方法，包括：
    确定目标查询时间段下数据对象所使用的数据存储路径信息；
    从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
    通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
  2. 根据权利要求1所述的方法，其中，通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况，包括：
    基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析；
    依据对目标操作对象的存储影响事件分析结果，预测确定目标查询时间段下数据对象的存储情况；
    其中，影响事件配置信息中包括对数据对象从源端写入目的端产生数据存储影响的存储影响事件记录，存储影响事件记录包括影响数据存储的操作对象标识、影响事件描述以及影响事件起止时间。
  3. 根据权利要求2所述的方法，其中，基于预先存储的影响事件配置信息，对从源端写入目的端所经历的目标操作对象进行存储影响事件分析，包括：
    响应于查询所述目标操作对象存在于影响事件配置信息中，确定影响事件配置信息的存储影响事件记录中记录的所述目标操作对象的影响事件起止时间段；
    响应于所述目标操作对象的影响事件起止时间段与目标查询时间段存在重合时间段，预测重合时间段下数据对象存在异常存储。
  4. 根据权利要求1所述的方法，其中，所述从源端写入目的端所经历的目标操作对象包括前端采集设备、服务器、交换机设备、存储端设备、存储端设备分组、磁盘、阵列、用户启停存储操作以及告警联动启停存储操作中的至少两项。
  5. 根据权利要求1所述的方法，其中，所述存储影响事件包括断网、断电、设备下线重启、服务下线重启以及用户操作中的至少一项。
  6. 根据权利要求2所述的方法，还包括：
    通过与存在数据存储影响的目标操作对象关联的旁观设备，对影响事件配置信息中存在数据存储影响的目标操作对象的存储影响事件记录进行更新修正；
    其中，旁观设备与存在数据存储影响的目标操作对象满足以下条件：不在相同数据存储路径且两者之间不互相旁观。
  7. 根据权利要求2所述的方法，还包括：
    在数据检索预测的非高并发时间段，通过数据检索抽检方式确定造成数据存储影响的新的操作对象，并形成一条新的存储影响事件记录添加到影响事件配置信息。
  8. 根据权利要求6所述的方法，所述通过与存在数据存储影响的目标操作对象关联的旁观设备，对影响事件配置信息中存在数据存储影响的目标操作对象的存储影响事件记录进行更新修正，包括：
    响应于在存储影响事件记录的影响事件起始时间内，旁观设备对所述存储影响事件记录对应的目标操作对象的确定结果与影响事件配置信息中记录的结果相符，所述影响事件起始时间内没有进行录像存储，不需要对影响事件配置信息中的所述存储影响事件记录进行更新修正；响应于旁观设备对所述存储影响事件记录对应的目标操作对象的确定结果与影响事件配置信息中记录的结果相反，启动一次真实数据查询，将查询结果作为最终结果，并根据所述最终结果确定是否对影响事件配置信息中的所述存储影响事件记录进行更新修正。
  9. 一种数据检索预测装置，包括：
    存储路径确定模块，设置为确定目标查询时间段下数据对象所使用的数据存储路径信息；
    操作对象确定模块，设置为从所述数据存储路径信息中，确定数据对象从源端写入目的端所经历的目标操作对象；
    数据存储预测模块，设置为通过对所述目标操作对象进行存储影响事件分析，预测确定目标查询时间段下数据对象的存储情况。
  10. 一种电子设备，包括：
    至少一个处理装置；
    存储装置，设置为存储至少一个程序；
    当所述至少一个程序被所述至少一个处理装置执行，使得所述至少一个处理装置实现权利要求1-8中任一所述的数据检索预测方法。
  11. 一种计算机可读介质，所述计算机可读介质上存储有计算机程序，所述计算机程序被处理装置执行时实现权利要求1-8中任一所述的数据检索预测方法。
PCT/CN2021/136264 2021-06-09 2021-12-08 数据检索预测方法、装置、电子设备及可读介质 WO2022257392A1 (zh)

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