CN116723213A - Method and device for processing equipment logs in track system, medium and electronic equipment - Google Patents
Method and device for processing equipment logs in track system, medium and electronic equipment Download PDFInfo
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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Abstract
The application discloses a method, a device, a medium and electronic equipment for processing equipment logs in a track system. According to the method, a large amount of noise data generated in the equipment log is removed in a keyword filtering mode, so that the data quantity of log data storage and uploading is reduced, the data uploading efficiency is improved, the bandwidth flow is reduced, and the memory space occupied by data storage is reduced. Meanwhile, newly added log data can be extracted through log file size analysis or log record time increase analysis and uploaded to a server, so that intelligent analysis and comparison of the log data are realized, the uploading data volume is further reduced, and the uploading efficiency is improved.
Description
Technical Field
The application belongs to the technical field of rail transit, and particularly relates to a method and a device for processing equipment logs in a rail system, a computer readable storage medium and electronic equipment.
Background
In the field of rail transit, log files generated by running equipment (such as a subway gate, a subway reader-writer and the like) are recorded in a local storage space (i.e. a hard disk) of the equipment. Regarding maintenance work of the log, it is necessary to manually maintain the storage amount or the storage space thereof. When the device log needs to be checked, the log needs to be manually copied from the device by using a USB flash disk or other storage media, or the device log needs to be sent to a specific checking device in a network mode. Since the log data generated by the device is usually more, when the log data is copied to the usb disk or transmitted to other devices, a large amount of log data needs to be transmitted, which results in lower data transmission efficiency.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the application and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The application aims to provide a processing method, a device, a medium and electronic equipment for equipment logs in a track system, so as to optimize the problem of low log data transmission efficiency in the related technology.
Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application.
According to an aspect of an embodiment of the present application, there is provided a method for processing a device log in a track system, including:
acquiring log data to be processed generated by equipment in a track system;
carrying out keyword recognition on the log data to be processed to determine keywords to be processed in the log data to be processed;
according to a keyword filtering mode corresponding to the keyword to be processed, keyword filtering processing is carried out on the log data to be processed, and target log data are obtained;
and uploading the target log data to a server.
According to an aspect of an embodiment of the present application, there is provided a processing apparatus for device log in a track system, including:
The data acquisition module is used for acquiring log data to be processed generated by equipment in the track system;
the keyword recognition module is used for recognizing keywords of the log data to be processed so as to determine keywords to be processed in the log data to be processed;
the data filtering module is used for filtering the keywords of the log data to be processed according to the keyword filtering mode corresponding to the keywords to be processed to obtain target log data;
and the log uploading module is used for uploading the target log data to a server.
In one embodiment of the application, the data filtering module comprises:
the deleting unit is used for deleting the keywords to be processed in the affiliated local love processing log data if the keyword filtering mode corresponding to the keywords to be processed is deleting processing, so as to obtain target log data;
the reservation unit is used for taking the keywords to be processed in the affiliated local love processing log data as target log data if the keyword filtering mode corresponding to the keywords to be processed is reservation processing;
and the replacing unit is used for replacing the keywords to be processed in the local love processing log data with preset keyword data if the keyword filtering mode corresponding to the keywords to be processed is replacement processing, so as to obtain target log data.
In one embodiment of the present application, the log uploading module includes:
the data recording unit is used for recording the target log data in a log file;
the to-be-uploaded data extraction unit is used for periodically extracting to-be-uploaded log data which generates changes in the log file according to the log uploading frequency;
and the data uploading unit is used for uploading the log data to be uploaded to a server.
In one embodiment of the present application, the data extraction unit to be uploaded is specifically configured to:
acquiring the current data volume of a log file at the current uploading time and the historical data volume of the log file at the previous uploading time;
if the current data amount is larger than the historical data amount, determining historical data in the log file according to the historical data amount;
and extracting the log data except the historical data from the log file as the log data to be uploaded.
In one embodiment of the present application, the data extraction unit to be uploaded is specifically configured to:
acquiring a historical time stamp of last writing target log data into the log file;
determining whether a target timestamp identical to the historical timestamp exists in the log file;
If the target time stamp exists, extracting log data after the target time stamp in the log file as log data to be uploaded;
and if the target time stamp does not exist, extracting the log data recorded by the log file as the log data to be uploaded.
In one embodiment of the application, the apparatus further comprises:
the abnormality prompting module is used for acquiring the latest timestamp of writing target log data into the log file; and if the latest time stamp is smaller than the historical time stamp, generating abnormal prompt information.
In one embodiment of the application, the apparatus further comprises:
the memory capacity judging module is used for judging whether the memory capacity occupied by the target log data is larger than the current residual memory capacity of the target log memory area; the target log memory area is the sum of a plurality of target sub-memory areas allocated by the system memory of the device based on a plurality of memory applications;
the storage position determining module is used for determining a target storage position of the target log data in the target log memory area according to a preset strategy if the memory capacity occupied by the target log data is larger than the current residual memory capacity;
And the data storage module is used for storing the target log data to the target storage position.
According to an aspect of the embodiments of the present application, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements a method of processing map data as in the above technical solution.
According to an aspect of an embodiment of the present application, there is provided an electronic apparatus including: a processor; and a memory for storing executable instructions of the processor; wherein execution of the executable instructions by the processor causes the electronic device to perform the method of processing map data as in the above technical solution.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the processing method of map data as in the above technical solution.
In the technical scheme provided by the embodiment of the application, the key word recognition is carried out on the log data to be processed generated by the equipment in the track system to obtain the key word to be processed, and then the filtering processing is carried out according to the key word corresponding to the key word to be processed, and finally the filtered target log data is uploaded to the server, so that a large amount of noise data generated by the equipment log can be reduced, the data quantity involved in uploading daily data to the server is reduced, and the uploading efficiency is improved; meanwhile, the log data can be acquired and checked at any time through the server, so that the query efficiency and flexibility of the log data are greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
Fig. 1 schematically shows a block diagram of an exemplary system architecture to which the technical solution of the present application is applied.
Fig. 2 schematically illustrates a flowchart of a method for processing a device log in a track system according to an embodiment of the present application.
Fig. 3 schematically shows an exemplary system architecture schematic to which the technical solution of the present application is applied.
Fig. 4 schematically illustrates a flowchart of a method for processing a device log in a track system according to an embodiment of the present application.
Fig. 5 schematically illustrates a schematic diagram of an exemplary system architecture provided by one embodiment of the present application.
Fig. 6 schematically shows a block diagram of a device for processing equipment logs in a track system according to an embodiment of the present application.
Fig. 7 schematically shows a block diagram of a computer system of an electronic device for implementing an embodiment of the application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the application may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the application.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 schematically shows a block diagram of an exemplary track system architecture to which the technical solution of the application is applied.
As shown in fig. 1, the track system architecture 100 may include a device 110, a network 120, and a server 130. The device 110 may include various electronic devices or combinations of various electronic devices in a track system, such as various sensors (e.g., smoke sensors, temperature sensors, etc.), ticket gates, security check machines, etc., and devices composed of sensors and other electronic devices or terminal devices (e.g., sensor acquisition data is sent to the terminal device, then the terminal device and the sensors constitute the device 110), and the track system may be replaced with a subway system, a railway system, etc. The server 130 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service; the server 130 may be deployed locally or at the cloud. Network 120 may be a communication medium of various connection types capable of providing a communication link between device 110 and server 130, such as a wired communication link or a wireless communication link.
The system architecture in embodiments of the present application may have any number of electronic devices, networks, and servers, as desired for implementation. For example, the server 130 may be a server group composed of a plurality of server devices. In addition, the technical solution provided in the embodiment of the present application may be applied to the device 110, or may be implemented by the device 110 and the server 130 together, which is not limited in particular.
For example, the technical scheme of the present application is implemented by the device 110, and the device 110 is a device in a track system. First, the device 110 acquires log data to be processed. The device 110 then performs keyword recognition on the log data to be processed to determine keywords to be processed in the log data to be processed. Next, the device 110 performs keyword filtering processing on the log data to be processed according to a keyword filtering manner corresponding to the keyword to be processed, so as to obtain target log data. Finally, the device 110 uploads the target log data to a server, which may be the server 130.
The following describes a method for processing the equipment log in the track system according to the present application in detail with reference to a specific embodiment.
Fig. 2 schematically illustrates a flowchart of a method for processing a device log in a track system according to an embodiment of the present application, as shown in fig. 2, where the method includes steps 210 to 240, specifically includes the following steps:
Step 210, obtaining log data to be processed generated by equipment in the track system.
In particular, a track system refers to a track traffic system, such as a train system, a subway system, etc. The equipment in the track system can generate log data in the running process, and the log data is recorded information of various operation behaviors and abnormal behaviors generated in the running process of the equipment. For example, if the smoke sensor detects smoke once every second, the smoke detection result is generated once every second, and a piece of log data can be formed by one smoke detection action and the smoke detection result. The problems occurring in the running process of the equipment can be located through the log data, so that the equipment needs to store the log data so as to facilitate subsequent calling and checking, and the log data to be processed is the log data waiting to be stored.
The device capable of generating log data may be a hardware device or a software device. For example, a smoke sensor is a hardware device and an application may also generate log data, such as a detection program that detects whether the smoke sensor is functioning properly. The log data generated by the software device may also be regarded as log data generated by a hardware device integrating the software device, for example, log data generated by an inspection program detecting the operation of the smoke sensor may also be regarded as log data generated by the smoke sensor.
In one embodiment of the application, log data may also be generated in concert by multiple devices. For example, the smoke sensor only performs smoke detection, has no data recording function, and typically such a smoke sensor may work in conjunction with a control device that sends detection instructions to the smoke sensor, the smoke sensor returns detection results, and the control device may generate log data based on the detection actions and the detection results.
Step 220, keyword recognition is performed on the log data to be processed to determine keywords to be processed in the log data to be processed.
Specifically, the keyword recognition means that the same keyword to be processed as the preset keyword is recognized from the log data to be processed according to at least one preset keyword set in advance. It should be noted that, in the present application, the term "keyword" is not limited to a word in a narrow sense, but may be set to an information data, which may be a character, a word, a field, an instruction, or other forms that meet requirements.
In one embodiment of the present application, keyword recognition may be implemented by means of matching strings, that is, matching strings corresponding to preset keywords in log data to be processed, and finding string data in the log data to be processed, where the string data is completely consistent with the string of the preset keywords, as the keywords to be processed.
And 230, performing keyword filtering processing on the log data to be processed according to a keyword filtering mode corresponding to the keyword to be processed, so as to obtain target log data.
Specifically, the keyword filtering means filtering the keywords to be processed in the log data to be processed, so as to reduce the data volume of the log data to be processed. The log data to be processed can contain a plurality of pieces of record information, and the keyword filtering mode can be used for filtering the keywords to be processed only, or can be used for filtering data segments or record information containing the keywords to be processed in the log data to be processed.
In one embodiment of the application, keyword filtering means may include, but are not limited to, keyword deletion, keyword retention, and keyword replacement. And deleting the log data to be processed, namely deleting the data corresponding to the keywords to be processed in the log data to be processed. For example, the log data to be processed includes heartbeat data among devices, and the heartbeat data among devices mostly includes repeated and less important data, the heartbeat data includes keywords "cmd" 1020", and the corresponding keyword processing mode may be deleting the data including" cmd "1020, so that the log data to be processed after deleting the heartbeat data among devices is the target log data.
And carrying out keyword preservation processing on the log data to be processed, namely only preserving data corresponding to the keywords to be processed in the log data to be processed, and deleting other data. For example, the log data to be processed includes a plurality of types of log data, the user only needs to keep the face verification data, the face verification data includes a keyword "face_check", and the corresponding keyword processing manner may be to keep only the data including the face_check, so that only the data including the face_check in the log data to be processed is the target log data.
And carrying out keyword replacement processing on the log data to be processed, namely replacing the data corresponding to the keyword to be processed of the log data to be processed with another piece of preset data. For example, the log data to be processed includes heartbeat data between devices, although the importance of the heartbeat data is low, the heartbeat data is not required to be deleted, and the data containing the heartbeat data can be replaced by another piece of shorter data, for example, the data containing "cmd" 1020 is replaced by "device_heartbeat" and the log data to be processed after the replacement processing is completed is the target log data.
Step 240, uploading the target log data to the server.
Specifically, after keyword filtering processing is performed on log data to be processed, uploading target log data to a server, and subsequently obtaining and checking log data generated by equipment in a track system at any time through the server.
In one embodiment of the present application, when uploading log data to a server, the log data may be compressed first to obtain compressed data; and then uploading the compressed data to the server. The server side can obtain the original log data through corresponding decompression processing.
In the technical scheme provided by the embodiment of the application, the key word recognition is carried out on the log data to be processed generated by the equipment in the track system to obtain the key word to be processed, and then the filtering processing is carried out according to the key word corresponding to the key word to be processed, and finally the filtered target log data is uploaded to the server, so that a large amount of noise data generated by the equipment log can be reduced, the data quantity involved in uploading daily data to the server is reduced, and the uploading efficiency is improved; meanwhile, the log data can be acquired and checked at any time through the server, so that the query efficiency and flexibility of the log data are greatly improved.
Exemplary, fig. 3 schematically shows an exemplary system architecture schematic to which the technical solution of the present application is applied. As shown in fig. 3, the system architecture includes a file extractor (FileExtractor), a data acquisition interface (API), and a Filter (Filter). The file extractor and the data acquisition interface are used for acquiring log data to be processed generated by equipment in the track system. The filter is used for carrying out keyword recognition and keyword filtration on log data to be processed, wherein the keyword filtration comprises discarding (namely deleting), storing (namely reserving) and replacing.
Fig. 4 schematically shows a flowchart of a method for processing a device log in a track system according to an embodiment of the present application, which is a further optimization of the above embodiment. As shown in fig. 4, the method includes steps 410 to 460, specifically as follows:
step 410, obtaining log data to be processed generated by equipment in the track system.
Step 420, keyword recognition is performed on the log data to be processed, so as to determine keywords to be processed in the log data to be processed.
And 430, performing keyword filtering processing on the log data to be processed according to a keyword filtering mode corresponding to the keyword to be processed, and obtaining target log data.
Steps 410-430 are the same as steps 210-230 in the previous embodiments, and are not described here again.
Step 440, the target log data is recorded in the log file.
Specifically, in order to facilitate management of log data, the log data is generally recorded in a log file, that is, a file whose content is log data, for example, a log file.
And 450, periodically extracting the log data to be uploaded, which are changed in the log file, according to the log uploading frequency.
Specifically, the log uploading frequency is the reciprocal of the log uploading period, for example, the log uploading frequency is 60Hz, which means that log data is uploaded once per second, that is, the log uploading period is 1s.
In one embodiment of the present application, whether the data in the log file changes may be determined according to the change of the size of the log file, which specifically includes: acquiring the current data volume of a log file at the current uploading time and the historical data volume of the log file at the previous uploading time; if the current data volume is larger than the historical data volume, determining historical data in the log file according to the historical data volume; and extracting the log data except the historical data from the log file as the log data to be uploaded.
Specifically, the data size of the log file indicates the size of the log file, and whether the log data in the log file changes can be determined through the change of the size of the log file corresponding to two adjacent periods. The log file sizes of two adjacent periods are the current data volume of the log file at the current uploading time and the historical data volume at the previous uploading time. If the current data amount is larger than the historical data amount, the fact that the log data is newly added in the log file is indicated, and the log data of the newly added part is the log data to be uploaded. And subtracting the historical log data from the log data in the log file to obtain the log data of the newly added part.
In one embodiment of the present application, if the current data amount is equal to the historical data amount, it indicates that there is no change in the data in the log file, and at this time, it may be selected not to upload the log data to the server. If the current data volume is smaller than the historical data volume, the data loss in the log file is indicated, and the data is in an abnormal condition, and at the moment, abnormal prompt information can be generated so as to be convenient for finding the abnormal condition by timing.
In one embodiment of the present application, the change in the size of the log file may be determined according to whether the timestamp of the data in the log file changes, which specifically includes: acquiring a historical timestamp of last writing of target log data into a log file; determining whether a target timestamp identical to the historical timestamp exists in the log file; if the target time stamp exists, extracting the log data after the target time stamp in the log file as the log data to be uploaded; and if the target time stamp does not exist, extracting the log data recorded by the log file as the log data to be uploaded.
Specifically, the beginning of the log data is typically the generation timestamp of the log data. The last time the target log data is written into the historical timestamp of the log file refers to the timestamp of the first entry in the log file marked with the log data after the log data is uploaded to the server last time. And matching the historical time stamp in the log file, and when the historical time stamp is matched with the target time stamp which is the same as the historical time stamp, indicating that the data node which is uploaded by the log data last time is found, wherein the data behind the data node is the data which is not uploaded, so that the log data behind the target time stamp in the log file is extracted as the log data to be uploaded. When the target time stamp which is the same as the historical time stamp is not matched, the file of the whole log file is newly generated log data and is not uploaded, so that all log data recorded in the log file are used as log data to be uploaded at the moment.
In one embodiment of the present application, it may also be determined by a timestamp whether the log file has an exception, specifically: acquiring the latest time stamp for writing the target log data into the log file; if the latest time stamp is smaller than the historical time stamp, abnormal prompt information is generated.
Specifically, the latest timestamp of writing the target log data to the log file refers to the timestamp of the first entry of the last write to the log file. If the latest time stamp is smaller than the historical time stamp, the data record is disordered or the time stamp is disordered, and data loss can be caused, so that abnormal prompt information is generated, and a user can know and solve the abnormal situation in time.
In one embodiment of the present application, the log uploading period (or frequency) may be a preset fixed period (or frequency), or may be dynamically adjusted according to the current operation condition of the device. For example, the period may be dynamically adjusted based on traffic flow, equipment maintenance status, traffic priority, network resource usage, external input data, and the like. For example, the original period is to upload log data to the server once every 5 minutes, and when the traffic volume is increased (for example, the traffic volume of passengers in the morning and evening peak period of a subway is increased), the period is increased and is adjusted to be once every 15 minutes, so that the device uses more resources to cope with the increased traffic volume. The uploading of log data may be stopped when the device is in maintenance. Traffic priority also indicates the priority of log data, e.g., carbon monoxide sensor priority is higher than temperature sensor priority, then the log data upload period (e.g., 1 minute) for a carbon monoxide sensor should be less than the temperature sensor priority upload period (e.g., 5 minutes). When the network resource utilization rate is higher, a larger period can be configured; when the network resource utilization rate is smaller, a smaller period can be configured; the period may also be set in combination with network bandwidth and network resource usage. The external input data represents a human adjustment period, at which time the period may be set directly from the external input data.
In one embodiment of the application, the running condition data of the equipment can be collected as training data to train the machine learning model, and then the real-time running condition of the equipment is input into the trained machine learning model to obtain the current data uploading period.
Step 460, uploading the log data to be uploaded to the server.
According to the technical scheme provided by the embodiment of the application, the target log data is periodically uploaded to the server, so that the frequency of uploading the log data is reduced, and the network resources occupied by uploading the log data are reduced. Meanwhile, in the periodical uploading process, the data to be uploaded, which are changed in the log file, are extracted and uploaded to the server, so that the data quantity involved in uploading the log data is further reduced, a large amount of redundancy of the log data acquired by the server is avoided, and the data uploading efficiency and the network resource utilization rate are improved.
In one embodiment of the present application, after obtaining the target log data, the target log data may be stored in a system memory of the device, where the specific steps include: judging whether the memory capacity occupied by the target log data is larger than the current residual memory capacity of the target log memory area or not; the target log memory area is the sum of a plurality of target sub-memory areas allocated by the system memory of the device based on a plurality of memory applications; if the memory capacity occupied by the target log data is larger than the current residual memory capacity, determining a target storage position of the target log data in a target log memory area according to a preset strategy; the target log data is stored to a target storage location.
Specifically, the target log memory area needs to be applied to the system memory in advance, and application of memory with a larger space to the system at one time may be difficult to succeed. The preset strategy is a pre-configured log data storage strategy when the memory capacity occupied by the log data exceeds the memory capacity of the area in the target log, and is equivalent to a processing strategy of the stored history log data in the target log memory area.
By filtering the log data and then storing the log data, the storage space occupied by the log data can be effectively reduced, and the memory utilization rate is improved.
In the embodiment of the application, when the target log memory area is applied, the memory application times can be calculated according to the preset log memory capacity and the memory application granularity, then the memory application is carried out for a plurality of times to the system memory of the equipment according to the memory application times and the memory application granularity, a plurality of target sub-memory areas are obtained, and then the target log memory area is determined according to the plurality of target sub-memory areas. The preset log memory capacity is a capacity of a target log memory area preset for storing log data. The memory application granularity refers to the memory capacity obtained by successfully performing a memory application. For example, if the preset log memory capacity is 10MB and the memory application granularity is 1MB, the application number is 10/1=10.
In one embodiment of the present application, the system memory may not pass through each memory application, i.e. there is a case of memory application failure, and then the target log memory area is the sum of a plurality of target sub-memory areas obtained by memory application success, and it can be seen that the number of target sub-memory areas included in the target log memory area is less than or equal to the number of memory applications.
In one embodiment of the present application, the memory application granularity may be predicted by a machine learning model based on the current device data and the preset log memory capacity. The current device data includes current system memory parameters, current operating system parameters, and current processor parameters. The current device data includes current system memory parameters, current operating system parameters, and current processor parameters. Current system memory parameters include, but are not limited to, total system memory capacity and current remaining system memory capacity. Current processor parameters include, but are not limited to, current processor dominant frequency, current processor utilization, and current processor architecture. Therefore, the machine learning model can determine the memory application granularity according to the current equipment data, so that the memory application can be matched with the current running condition of the equipment, and the success rate of the memory application is effectively improved.
In one embodiment of the application, the preset policy includes loop storage and disk storage. The cyclic storage refers to cyclic use of the target log memory area for data storage, and the disk storage refers to log data storage into a disk. When a circular storage mode is adopted, the current initial position of the target log memory area is used as the target storage position of the log data to be processed next, the current initial position is the storage position of the history log data stored in the target log memory area, which is farthest from the current moment, namely, when the data in the target log memory area is full, the initial position of the target log memory area is returned again to be continuously stored from front to back according to the address sequence, and thus the circular storage is realized. When adopting a disk storage mode, the history log data stored in the target log memory area is transferred to the disk, and then any storage position is selected from the target log memory area after the data transfer as the target storage position of the log data to be processed.
The flexibility of log data storage is improved through different storage strategies; meanwhile, the technical scheme that after certain log data are accumulated in the memory, the log data are written into the disk in batches is realized, so that the memory data are prevented from being written into the disk frequently, the read-write frequency of the disk is reduced, the disk is prevented from being damaged, and the maintenance cost of equipment is reduced.
In one embodiment of the present application, when the system memory responds to the memory application, the adjacent target sub-memory areas are preferentially allocated for the two adjacent memory applications, so that the target log memory area is a continuous memory area, and confusion between the storage of log data and the storage of other data in the memory is avoided.
In one embodiment of the present application, when storing log data, priority may be set for the log data, and when exceeding the capacity of the target log memory area, the storage location where the history log data with the lowest priority stored in the target log memory area is located may be used as the target storage location of the current log data to be processed. Thus, on the basis of realizing cyclic storage, the log data with high priority is ensured to be reserved for a longer time.
In one embodiment of the present application, the target log data may be stored in the system memory first, and then read from the system memory and uploaded to the server. When the log data is stored in the disk, the memory reads the log data from the disk and then uploads the log data to the server.
In one embodiment of the application, the data filtering can be implemented after forming a plurality of log data into a log file, namely, the log file is subjected to data filtering, so that the data filtering can be performed in a centralized and batched way, and the data filtering efficiency is improved.
By way of example, FIG. 5 schematically illustrates a schematic diagram of an exemplary system architecture provided by one embodiment of the present application. As shown in fig. 5, the system architecture includes: file extractor (FileExtractor), data acquisition interface (API), filter (Filter), consumer module (Consumer), and compression module (compression). The technical scheme of the application is implemented by a log module in the equipment, which is equivalent to a Client (Client), and the server comprises a log service platform (server) and other servers.
The user creates the log file by other modes or calls the log module in the system architecture of the embodiment, the log data generated by other programs can be transmitted into the log module through the data acquisition interface, and the log data is supported to be sent to the server through the network (http, socket, websocket, etc.) in real time and without delay through the consumer module, or the log data is stored in the system memory (RAM). When the filtering requirement is not met, the filter can be disabled, and the log data is directly transmitted to the server. When the filtering requirement exists, a user can configure a keyword filtering mode adopted by the filter to filter the log data to be processed so as to obtain target log data. The target log data can be uploaded to the server after being compressed by the compression module.
The user can conduct log display, log configuration, data analysis, AI decision, log file pulling and log management through the log service platform.
Fig. 6 schematically illustrates a block diagram of a device for processing a device log in a track system according to an embodiment of the present application, where the device may implement a method for processing a device log in a track system according to any embodiment of the present application. As shown in fig. 6, a device for processing a device log in a track system according to an embodiment of the present application includes:
the data acquisition module 610 is configured to acquire log data to be processed generated by equipment in the track system;
the keyword recognition module 620 is configured to perform keyword recognition on the log data to be processed, so as to determine a keyword to be processed in the log data to be processed;
the data filtering module 630 is configured to perform keyword filtering processing on the log data to be processed according to a keyword filtering manner corresponding to the keyword to be processed, so as to obtain target log data;
and a log uploading module 640, configured to upload the target log data to a server.
In one embodiment of the application, the data filtering module 630 includes:
the deleting unit is used for deleting the keywords to be processed in the affiliated local love processing log data if the keyword filtering mode corresponding to the keywords to be processed is deleting processing, so as to obtain target log data;
The reservation unit is used for taking the keywords to be processed in the affiliated local love processing log data as target log data if the keyword filtering mode corresponding to the keywords to be processed is reservation processing;
and the replacing unit is used for replacing the keywords to be processed in the local love processing log data with preset keyword data if the keyword filtering mode corresponding to the keywords to be processed is replacement processing, so as to obtain target log data.
In one embodiment of the present application, log upload module 640 includes:
the data recording unit is used for recording the target log data in a log file;
the to-be-uploaded data extraction unit is used for periodically extracting to-be-uploaded log data which generates changes in the log file according to the log uploading frequency;
and the data uploading unit is used for uploading the log data to be uploaded to a server.
In one embodiment of the present application, the data extraction unit to be uploaded is specifically configured to:
acquiring the current data volume of a log file at the current uploading time and the historical data volume of the log file at the previous uploading time;
if the current data amount is larger than the historical data amount, determining historical data in the log file according to the historical data amount;
And extracting the log data except the historical data from the log file as the log data to be uploaded.
In one embodiment of the present application, the data extraction unit to be uploaded is specifically configured to:
acquiring a historical time stamp of last writing target log data into the log file;
determining whether a target timestamp identical to the historical timestamp exists in the log file;
if the target time stamp exists, extracting log data after the target time stamp in the log file as log data to be uploaded;
and if the target time stamp does not exist, extracting the log data recorded by the log file as the log data to be uploaded.
In one embodiment of the application, the apparatus further comprises:
the abnormality prompting module is used for acquiring the latest timestamp of writing target log data into the log file; and if the latest time stamp is smaller than the historical time stamp, generating abnormal prompt information.
In one embodiment of the application, the apparatus further comprises:
the memory capacity judging module is used for judging whether the memory capacity occupied by the target log data is larger than the current residual memory capacity of the target log memory area; the target log memory area is the sum of a plurality of target sub-memory areas allocated by the system memory of the device based on a plurality of memory applications;
The storage position determining module is used for determining a target storage position of the target log data in the target log memory area according to a preset strategy if the memory capacity occupied by the target log data is larger than the current residual memory capacity;
and the data storage module is used for storing the target log data to the target storage position.
Specific details of the processing device for device logs in a track system provided in each embodiment of the present application have been described in detail in the corresponding embodiments, and are not described herein again.
Fig. 7 schematically shows a block diagram of a computer system of an electronic device for implementing an embodiment of the application.
It should be noted that, the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 7, the computer system 700 includes a central processing unit 701 (Central Processing Unit, CPU) which can execute various appropriate actions and processes according to a program stored in a Read-Only Memory 702 (ROM) or a program loaded from a storage section 708 into a random access Memory 703 (Random Access Memory, RAM). In the random access memory 703, various programs and data necessary for the system operation are also stored. The central processing unit 701, the read only memory 702, and the random access memory 703 are connected to each other via a bus 704. An Input/Output interface 705 (i.e., an I/O interface) is also connected to bus 704.
The following components are connected to the input/output interface 705: an input section 706 including a keyboard, a mouse, and the like; an output section 707 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and the like, a speaker, and the like; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a local area network card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the input/output interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read therefrom is mounted into the storage section 708 as necessary.
In particular, the processes described in the various method flowcharts may be implemented as computer software programs according to embodiments of the application. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. The computer programs, when executed by the central processor 701, perform the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection 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, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A method for processing a device log in a track system, comprising:
acquiring log data to be processed generated by equipment in a track system;
carrying out keyword recognition on the log data to be processed to determine keywords to be processed in the log data to be processed;
according to a keyword filtering mode corresponding to the keyword to be processed, keyword filtering processing is carried out on the log data to be processed, and target log data are obtained;
and uploading the target log data to a server.
2. The method for processing an equipment log in a track system according to claim 1, wherein the step of performing keyword filtering processing on the log data to be processed according to a keyword filtering manner corresponding to the keyword to be processed to obtain target log data includes:
if the keyword filtering mode corresponding to the keyword to be processed is deleting processing, deleting the keyword to be processed in the local love processing log data to obtain target log data;
if the keyword filtering mode corresponding to the keyword to be processed is reserved processing, the keyword to be processed in the affiliated local love processing log data is used as target log data;
And if the keyword filtering mode corresponding to the keyword to be processed is replacement processing, replacing the keyword to be processed in the local love processing log data with preset keyword data to obtain target log data.
3. The method for processing the device log in the track system according to claim 1, wherein uploading the target log data to the server side comprises:
recording the target log data in a log file;
periodically extracting log data to be uploaded, which are changed in the log file, according to the log uploading frequency;
and uploading the log data to be uploaded to a server.
4. A method of processing a device log in a track system according to claim 3, wherein extracting log data to be uploaded that generates changes in the log file comprises:
acquiring the current data volume of a log file at the current uploading time and the historical data volume of the log file at the previous uploading time;
if the current data amount is larger than the historical data amount, determining historical data in the log file according to the historical data amount;
and extracting the log data except the historical data from the log file as the log data to be uploaded.
5. A method of processing a device log in a track system according to claim 3, wherein extracting log data to be uploaded that generates changes in the log file comprises:
acquiring a historical time stamp of last writing target log data into the log file;
determining whether a target timestamp identical to the historical timestamp exists in the log file;
if the target time stamp exists, extracting log data after the target time stamp in the log file as log data to be uploaded;
and if the target time stamp does not exist, extracting the log data recorded by the log file as the log data to be uploaded.
6. The method of processing a device log in a track system of claim 5, further comprising:
acquiring the latest time stamp for writing target log data into the log file;
and if the latest time stamp is smaller than the historical time stamp, generating abnormal prompt information.
7. The method of processing equipment logs in a rail system according to any of the claims 1-6, characterized in that after obtaining target log data, the method further comprises:
Judging whether the memory capacity occupied by the target log data is larger than the current residual memory capacity of the target log memory area or not; the target log memory area is the sum of a plurality of target sub-memory areas allocated by the system memory of the device based on a plurality of memory applications;
if the memory capacity occupied by the target log data is larger than the current residual memory capacity, determining a target storage position of the target log data in the target log memory area according to a preset strategy;
and storing the target log data to the target storage position.
8. A device for processing equipment logs in a track system, comprising:
the data acquisition module is used for acquiring log data to be processed generated by equipment in the track system;
the keyword recognition module is used for recognizing keywords of the log data to be processed so as to determine keywords to be processed in the log data to be processed;
the data filtering module is used for filtering the keywords of the log data to be processed according to the keyword filtering mode corresponding to the keywords to be processed to obtain target log data;
and the log uploading module is used for uploading the target log data to a server.
9. A computer readable medium on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a method for processing a device log in a track system according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein execution of the executable instructions by the processor causes the electronic device to perform the method of processing device logs in the track system of any one of claims 1 to 7.
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