A data processing method and device

Abstract

The present invention discloses a data processing method and device, and relates to the field of computer technology. A specific implementation of the method includes: obtaining data to be processed, determining a fault analysis strategy corresponding to the dimension of the data; analyzing whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnosing the fault type of the data; and processing the data through a plan strategy corresponding to the fault type. This implementation adopts a strategy of automation as the main and manual as the auxiliary, based on a data processing center and a decision analysis center, to collect data of the smallest granularity, and to process abnormal data in a timely manner when it is found, so as to eliminate the fault in the embryonic stage and achieve the purpose of automatic discovery, identification and processing.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data processing method and device. Background Art

[0002] Enterprises usually set up some departments to manage the storage and query of specific information. The special positioning determines that the system used must have the highest level of availability, stability and the most timely problem handling and response capabilities. The current solution is as follows:

[0003] 1) Access the monitoring system to monitor specific indicators, and issue an alarm when the configured threshold is reached. The staff receives the alarm information and handles it, such as offline microservices, group switching, and system machine capacity removal (putting the machine into cold standby processing and no longer receiving new external requests).

[0004] 2) Access the log reporting and collection system: The staff regularly checks the log information, sorts out and filters the abnormal information contained in the log, analyzes the problems and handles them.

[0005] 3) Use configuration centers such as pcms.xx.com and duc.xx.com to perform switch, plan, and group switching operations on the corresponding configurations.

[0006] In the process of implementing the present invention, the inventors found that the prior art has at least the following problems:

[0007] 1) The monitoring system and configuration center can only be operated through the PC platform. If the system has an abnormal alarm, and there is no computer to log in and process it, it means that there is a long window period for the abnormality to exist, which is unacceptable for the super-level 0 system;

[0008] 2) Alarm processing, abnormal log retrieval, and emergency plan execution all rely on manual processing, which is prone to omissions, operational errors, and other hidden dangers. With the increase in the number of departments within the enterprise, it has become impossible to complete the above operations manually;

[0009] 3) With the existing method, the cause of the fault will be queried through platforms such as the monitoring center and the log collection and query system only after the fault is exposed (such as equipment failure or computer room failure). This is a post-fault repair and compensation method with a certain delay. Summary of the invention

[0010] In view of this, an embodiment of the present invention provides a data processing method and device, which can at least solve the problem of low system stability and availability caused by excessive reliance on manual operation and PC platform in the prior art.

[0011] To achieve the above object, according to one aspect of an embodiment of the present invention, a data processing method is provided, comprising:

[0012] Acquire the data to be processed, and determine the fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0013] Analyzing whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnosing the fault type of the data;

[0014] The data is processed using a contingency plan strategy corresponding to the fault type.

[0015] Optionally, analyzing whether the data is abnormal based on the fault analysis strategy includes:

[0016] Obtaining a first fault threshold in the fault analysis strategy, determining whether a value in the data exceeds the first fault threshold, and if so, determining that the data is abnormal;

[0017] If not, obtaining the device identification stored in the data, determining the device corresponding to the device identification and the computer room where the device is located, and then determining other computer rooms under the platform where the computer room is located and other devices located in other computer rooms;

[0018] Determine the first data corresponding to the dimension under the other device, calculate the difference between the data and the first data, and if the ratio of the difference to the first data exceeds a second fault threshold, determine that the data is abnormal.

[0019] Optionally, the diagnosing the fault type of the data includes: when the data abnormality is diagnosed through the first fault threshold, diagnosing the fault type as a device fault;

[0020] The processing of the data by using a contingency plan strategy corresponding to the fault type includes:

[0021] Calling the microservice instance interface through the device number of the device to determine whether the device belongs to the microservice type;

[0022] If yes, call the microservice offline interface to offline the device, otherwise call the protocol service offline interface to offline the device.

[0023] Optionally, the diagnosing the fault type of the data includes: when the data abnormality is diagnosed through the second fault threshold, diagnosing the fault type as a computer room fault;

[0024] The processing of the data by using a contingency plan strategy corresponding to the fault type includes:

[0025] Determine a computer room switching strategy corresponding to a computer room failure, and obtain a switching configuration number corresponding to the computer room from the computer room switching strategy;

[0026] Selecting a normally operating computer room from other computer rooms corresponding to the switching configuration number;

[0027] The interface configuration parameters of the selected computer room are obtained, and the interface configuration parameters of the computer room are changed to the obtained interface configuration parameters, so as to switch the traffic or service connected to the computer room to the computer room.

[0028] Optionally, obtaining the data to be processed includes: based on the first cycle, capturing and storing data of multiple dimensions from at least one system under at least one platform; wherein the system includes at least one computer room, and the computer room includes at least one device.

[0029] Optionally, obtaining the data to be processed includes: based on a second cycle, obtaining the data to be processed from a data processing center; wherein, based on a third cycle, the data processing center captures and stores data of multiple dimensions from at least one system under at least one platform, and the second cycle is smaller than the third cycle.

[0030] To achieve the above object, according to another aspect of an embodiment of the present invention, a data processing device is provided, comprising:

[0031] An acquisition module, used to acquire data to be processed and determine a fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0032] A diagnosis module, used to analyze whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnose the fault type of the data;

[0033] A processing module is used to process the data using a contingency plan strategy corresponding to the fault type.

[0034] Optionally, the diagnosis module is used to: obtain a first fault threshold in the fault analysis strategy, determine whether a value in the data exceeds the first fault threshold, and if so, determine that the data is abnormal;

[0035] If not, obtaining the device identification stored in the data, determining the device corresponding to the device identification and the computer room where the device is located, and then determining other computer rooms under the platform where the computer room is located and other devices located in other computer rooms;

[0036] Determine the first data corresponding to the dimension under the other device, calculate the difference between the data and the first data, and if the ratio of the difference to the first data exceeds a second fault threshold, determine that the data is abnormal.

[0037] Optionally, the diagnosis module is used to: diagnose the fault type as a device fault when the data abnormality is diagnosed through the first fault threshold;

[0038] The processing module is used for:

[0039] Calling the microservice instance interface through the device number of the device to determine whether the device belongs to the microservice type;

[0040] If yes, call the microservice offline interface to offline the device, otherwise call the protocol service offline interface to offline the device.

[0041] Optionally, the diagnosis module is used to: when the data anomaly is diagnosed through the second fault threshold, diagnose the fault type as a computer room fault;

[0042] The processing module is used for:

[0043] Determine a computer room switching strategy corresponding to a computer room failure, and obtain a switching configuration number corresponding to the computer room from the computer room switching strategy;

[0044] Selecting a normally operating computer room from other computer rooms corresponding to the switching configuration number;

[0045] The interface configuration parameters of the selected computer room are obtained, and the interface configuration parameters of the computer room are changed to the obtained interface configuration parameters, so as to switch the traffic or service connected to the computer room to the computer room.

[0046] Optionally, the acquisition module is used to: based on the first cycle, capture and store data of multiple dimensions from at least one system under at least one platform; wherein the system includes at least one computer room, and the computer room includes at least one device.

[0047] Optionally, the acquisition module is used to: acquire the data to be processed from the data processing center based on a second cycle; wherein the data processing center captures and stores data of multiple dimensions from at least one system under at least one platform based on a third cycle, and the second cycle is smaller than the third cycle.

[0048] To achieve the above objective, according to another aspect of an embodiment of the present invention, a data processing electronic device is provided.

[0049] The electronic device of an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned data processing methods.

[0050] To achieve the above objective, according to another aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, any of the above data processing methods is implemented.

[0051] According to the solution provided by the present invention, an embodiment of the above invention has the following advantages or beneficial effects: adopting a strategy of automation as the main and manual as the auxiliary, based on the data processing center and the decision analysis center, relying on the collected multi-dimensional big data information such as systems, applications, equipment, logs, etc., to discover equipment / computer room hidden dangers in advance and eliminate faults in the bud stage, so as to achieve the purpose of automatic discovery, automatic identification, and automatic processing of abnormal data, and solve the problem of manual search and processing of data on the PC side.

[0052] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0054] Figure 1 is a main flow chart of a data processing method according to an embodiment of the present invention;

[0055] Figure 2 It is a schematic diagram of the main process of analyzing whether the data is abnormal;

[0056] Figure 3 is a flowchart of an optional data processing method according to an embodiment of the present invention;

[0057] Figure 4 is a flowchart of another optional data processing method according to an embodiment of the present invention;

[0058] Figure 5 is a schematic diagram of main modules of a data processing device according to an embodiment of the present invention;

[0059] Figure 6 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0060] Figure 7 It is a schematic diagram of the structure of a computer system of a mobile device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0062] It should be noted that the data of this solution is obtained by capturing from the platform. A platform is bound to at least one system, each system is equipped with at least one computer room, and each computer room is equipped with at least one device (or machine) to provide different services, such as product reading service, master data service, unified service, basic middleware service, etc.

[0063] See also Figure 1 , which shows a main flow chart of a data processing method provided by an embodiment of the present invention, comprising the following steps:

[0064] S101: Acquire data to be processed, and determine a fault analysis strategy corresponding to a dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0065] S102: analyzing whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnosing the fault type of the data;

[0066] S103: Process the data using a contingency plan strategy corresponding to the fault type.

[0067] In the above implementation, for step S101, the decision analysis center periodically (i.e., the first cycle, such as 1s) captures and stores data of multiple dimensions from the platform; wherein the dimensions can be horizontally expanded, and this solution mainly considers data under the dimensions of the container data monitoring center, monitoring platform, configuration center, and log collection and query system. For example:

[0068] 1) Through the API (Application Program Interface) interface provided by the monitoring platform, obtain the data reported by the system dimension, including the availability rate of the reported key, the number of calls, TP99, TP999, and the specific data distribution of each device under these dimensions.

[0069] 2) Through the API interface opened by the container data monitoring center, capture device dimension data, including CPU, memory, connectivity, number of retransmissions, ingress and egress traffic, etc. that affect system stability.

[0070] 3) Log collection query system uploads log information. Since the log volume of normal business is large, you only need to pay attention to specific logs that affect system stability indicators, such as NullPointException, RPC Exception, or logs with specified special keywords.

[0071] To reduce subsequent workload, the captured data can be summarized and stored by time (from small to large), indicators (TP99, TP999, availability, number of calls), equipment (connectivity, CPU, memory, number of retransmissions, traffic), computer room (computer room information, network), log keywords (NullPointException, RPC Exception), etc. before storage.

[0072] Due to the large amount of data (usually over 100 million), it is preferred to use algorithms suitable for big data scenarios such as Bitmap (bitmap algorithm) and DFA (Deterministic Finite Automaton) when aggregating data. In addition, data is stored for a limited time (such as 7 days) and will be automatically processed if it exceeds the time limit.

[0073] Furthermore, in order to reduce the workload of the decision analysis center, a data processing center can also be set up to complete the above work through the data processing center. The decision analysis center is only used to analyze whether the data is abnormal and process it. To avoid data overload, the second cycle of the decision analysis center obtaining data from the data processing center must be shorter than the third cycle of the data processing center grabbing data from the platform, such as 1s and 5s.

[0074] For step S102, this solution pre-sets different fault analysis strategies according to different dimensions, and the fault types are mainly divided into two types: equipment fault and computer room fault (see Figure 2 shown):

[0075] Step 1: Obtain a first fault threshold in the fault analysis strategy, and determine whether a value in the data exceeds the first fault threshold. If so, determine that the data is abnormal;

[0076] Step 2: If it is not exceeded, obtain the device identification stored in the data, determine the device corresponding to the device identification and the computer room where the device is located, and then determine other computer rooms under the platform where the computer room is located and other devices located in other computer rooms;

[0077] Step three: determine the first data corresponding to the dimension under the other device, calculate the difference between the data and the first data, and if the ratio of the difference to the first data exceeds the second fault threshold, determine that the data is abnormal.

[0078] For step 1, the fault analysis strategy sets a first fault threshold for the device and dimension to determine whether the device providing the data has a fault. For example, if the number of retransmissions of the device in the data exceeds the threshold, it will be diagnosed as a retransmission fault.

[0079] Usually, a fault analysis strategy only sets one threshold to determine whether a device is faulty. In addition to the threshold comparison, it can also include connectivity detection (ping), port survival detection, etc. This solution mainly considers the threshold comparison.

[0080] For steps 2 and 3, in the multi-computer room data dimension, even if the equipment is not faulty, the computer room where the equipment is located may also be faulty. If the data indicators (availability, TP performance, etc.) of a computer room are higher than the second fault threshold of the data indicators of other computer rooms, the computer room is diagnosed to have a fault. For example, the system includes three computer rooms ABC. If the data indicators of computer room A are higher than the data indicators of the other two computer rooms, and the difference is higher than 20% of the data indicators of the other two computer rooms, it is considered that computer room A has a fault at that moment.

[0081] In addition, you can Figure 2 The steps shown directly determine the fault type corresponding to the abnormal data. For example, the fault type determined by step one is a device fault. If the data is normal as determined by step one, but the data is abnormal as determined by steps two and three, the fault type is a computer room fault.

[0082] Depending on the type of fault, different fault tags can be added to the data so that the subsequent decision-making analysis center can determine the contingency plan strategy for handling abnormal data based on the fault tags, such as equipment fault tags and computer room fault tags.

[0083] After diagnosing a fault, the decision analysis center will first issue an alarm reminder through voice, text messages, etc. The background maintains a list of responsible persons at the system level, and determines the specific person in charge and sends an alarm message based on the platform information, system information, computer room information, equipment information, etc. carried in the data.

[0084] For step S103, this solution also sets different contingency plans and strategies for different fault types in the decision analysis center in advance to handle different fault data. Figure 3 Description shown.

[0085] The method provided in the above embodiment relies on the data processing center to capture and summarize multi-dimensional data, thereby solving the problem that scattered data can only be viewed through respective platforms. The decision analysis center analyzes whether the data is abnormal and diagnoses the type of fault, and finds the matching plan strategy to handle the abnormal data, thereby achieving the purpose of automation from positioning, alarm to problem handling, and reducing dependence on manual processing.

[0086] See also Figure 3 , shows a schematic flow chart of an optional data processing method according to an embodiment of the present invention, comprising the following steps:

[0087] S301: Acquire data to be processed, and determine a fault analysis strategy corresponding to a dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0088] S302: Obtain a first fault threshold in the fault analysis strategy, and determine whether a value in the data exceeds the first fault threshold;

[0089] S303: If exceeded, the data is determined to be abnormal, and the fault type is diagnosed as a device fault;

[0090] S304: calling a microservice instance interface through a device number of the device to determine whether the device belongs to a microservice type; wherein the device is determined through a device identifier in the data;

[0091] S305: If yes, call the microservice offline interface to offline the device;

[0092] S306: Otherwise, call the protocol service offline interface to offline the device.

[0093] In the above implementation, for step S301, see Figure 1 The description of step S101 is not repeated here.

[0094] In the above implementation, for steps S302 and S303, the fault analysis strategy sets a first fault threshold for the device and dimension to determine whether the device providing the data has a fault. For example, if the number of retransmissions of the device in the data exceeds the threshold, it will be diagnosed as a retransmission fault. Furthermore, a device fault tag can also be added to the data.

[0095] Usually, a fault analysis strategy only sets one threshold to determine whether a device is faulty. In addition to the threshold comparison, it can also include connectivity detection (ping), port survival detection, etc. This solution mainly considers the threshold comparison.

[0096] For steps S304 to S306, when the plan strategy background is entered, there are multiple check items, such as "automatic execution" and "manual execution", which depend on manual settings and are automatically executed by default.

[0097] For high-risk plans, or plans that require multiple people's confirmation before execution or non-emergency plans, manual processing is usually chosen. When manually executed, the data and fault information (equipment / machine room information, fault content, time) are pushed to the emergency platform interface for display. The staff can log in to the mobile interface anytime and anywhere to query the fault information and complete the confirmation. After that, the data processing process is consistent with automation.

[0098] The contingency plan strategy for device failure is configured with at least the following information: device failure label, execution mode (manual / automatic), execution content (device offline), first failure threshold (availability threshold, call count threshold, TP99 threshold, TP999 threshold, CPU threshold, retransmission count threshold, memory threshold, log keyword threshold, etc.).

[0099] The fault types include abnormal connectivity faults, abnormal log faults, etc. At this time, the object to be processed is directly the device that provides the data (the device ID is stored in the data), and the final measure taken is to take the device offline (JSF offline: microservice, device extraction).

[0100] Call the microservice instance interface to determine whether the device is a microservice type, such as Jeff, JSF (JavaServerFaces, standard framework) service. If so, call the microservice offline interface to offline the device; otherwise, determine that the device belongs to the http protocol service, and directly call the protocol service interface to offline the device.

[0101] The method provided in the above embodiment analyzes the microservice / protocol service type of the device and calls the corresponding offline interface to offline the device for device failure, thereby directly cutting off the source of abnormal data. The whole method can be executed automatically and has low dependence on human participation.

[0102] See also Figure 4 , shows a schematic flow chart of another optional data processing method according to an embodiment of the present invention, comprising the following steps:

[0103] S401: Acquire data to be processed, and determine a fault analysis strategy corresponding to a dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0104] S402: Obtain a first fault threshold in the fault analysis strategy, and determine whether a value in the data exceeds the first fault threshold;

[0105] S403: If not exceeded, obtain the device identification stored in the data, determine the device corresponding to the device identification and the computer room where the device is located, and then determine other computer rooms under the platform where the computer room is located and other devices located in other computer rooms;

[0106] S404: determining first data corresponding to the dimension under the other device, calculating a difference between the data and the first data, and determining whether a ratio of the difference to the first data exceeds a second fault threshold;

[0107] S405: If both are exceeded, the data is determined to be abnormal, and the fault type is diagnosed as a computer room fault;

[0108] S406: Determine a machine room switching strategy corresponding to the machine room failure, and obtain a switching configuration number corresponding to the machine room from the machine room switching strategy;

[0109] S407: Selecting a normally operating computer room from other computer rooms corresponding to the switching configuration number;

[0110] S408: Acquire interface configuration parameters of the selected computer room, and change the interface configuration parameters of the computer room to the acquired interface configuration parameters, so as to switch the traffic or service connected to the computer room to the computer room.

[0111] In the above implementation, for step S401, see Figure 1 The description of step S101 is not repeated here.

[0112] In the above implementation, for steps S402 to S405, in the multi-computer room data dimension, even if the equipment is not faulty, the computer room where the equipment is located may also be faulty. If the data indicators (availability, TP performance, etc.) of one computer room are higher than the second fault threshold of the data indicators of other computer rooms, the computer room is diagnosed to have a fault. For example, the system includes three computer rooms ABC. If the data indicators of computer room A are higher than the data indicators of the other two computer rooms, and the difference is higher than 20% of the data indicators of the other two computer rooms, it is considered that computer room A has a fault at that moment.

[0113] For steps S406 to S408, when the plan strategy background is entered, there are multiple check items, such as "automatic execution" and "manual execution", which depend on manual settings and are automatically executed by default.

[0114] The difference between automatic execution and manual execution is that manual execution requires first sending data and fault information (application information, fault content, occurrence time) to the emergency platform for display, and can only be processed after confirmation by the staff. The processing process is consistent with automatic processing.

[0115] The contingency plan strategy for equipment failure is configured with at least the following information: computer room fault label, execution mode (manual / automatic), execution content (computer room switching), second fault threshold (availability threshold, call count threshold, TP99 threshold, TP999 threshold, CPU threshold, retransmission count threshold, memory threshold, etc.).

[0116] When the fault type is a computer room fault, the processing object is the computer room (a computer room is equipped with at least one device, which is used to produce data), and the corresponding plan strategy is the computer room switching strategy, specifically:

[0117] 1) Pre-configure a switching configuration number for each computer room in the computer room switching strategy. The switching configuration number is different from the configuration number of the computer room itself and can correspond to multiple computer rooms. For example, the configuration number of computer room P is 123, and the switching configuration number is 234;

[0118] 2) Query the switch configuration number corresponding to the faulty computer room, determine other computer rooms corresponding to the switch configuration number, and select a normal computer room from them. For example, through the switch configuration number 234, computer room Q, computer room M and computer room N are queried, but computer room Q is also diagnosed as having a fault, so select one from the remaining computer rooms M and N;

[0119] 3) Obtain the interface configuration parameters of the selected normal computer room, and change the interface configuration parameters of the faulty computer room to the obtained interface configuration parameters, so as to switch the traffic connected to the faulty computer room to the selected normal computer room.

[0120] The method provided in the above embodiment determines the normal computer room through the switching configuration number corresponding to the faulty computer room, so as to switch the traffic or service connected to the faulty computer room to the normal computer room, thereby ensuring the stability of equipment operation and the security of data provided by the equipment.

[0121] The method provided in the embodiment of the present invention adopts a strategy of automation as the main and manual as the auxiliary, based on the data processing center and the decision analysis center, relying on the collected multi-dimensional big data information such as systems, applications, equipment, logs, etc., to discover equipment / computer room hidden dangers in advance:

[0122] 1) Monitor the smallest data granularity, analyze and process the data immediately after acquisition, eliminate faults in the bud, and achieve the purpose of automatically discovering, automatically identifying, and automatically processing abnormal data;

[0123] 2) Troubleshooting is no longer restricted by the platform and can be processed in batches. Developers can complete troubleshooting through the mobile terminal at any time;

[0124] 3) Even during non-working hours, it can still maintain highly stable and efficient operation, and solve the delays, omissions, uncertainties and uncontrollable labor costs of manual operations; it frees developers from the heavy operation and maintenance roles, allowing them to focus more on business processing.

[0125] See also Figure 5 , which shows a schematic diagram of main modules of a data processing device 500 provided in an embodiment of the present invention, including:

[0126] The acquisition module 501 is used to acquire the data to be processed and determine the fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0127] A diagnosis module 502, configured to analyze whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnose the fault type of the data;

[0128] The processing module 503 is used to process the data using a contingency plan strategy corresponding to the fault type.

[0129] In the implementation device of the present invention, the diagnostic module 502 is used to:

[0130] Obtaining a first fault threshold in the fault analysis strategy, determining whether a value in the data exceeds the first fault threshold, and if so, determining that the data is abnormal;

[0131] If not, obtaining the device identification stored in the data, determining the device corresponding to the device identification and the computer room where the device is located, and then determining other computer rooms under the platform where the computer room is located and other devices located in other computer rooms;

[0132] Determine the first data corresponding to the dimension under the other device, calculate the difference between the data and the first data, and if the ratio of the difference to the first data exceeds a second fault threshold, determine that the data is abnormal.

[0133] In the implementation device of the present invention, the diagnosis module 502 is used to: diagnose the fault type as a device fault when the data abnormality is diagnosed through the first fault threshold;

[0134] The processing module 503 is used to:

[0135] Calling the microservice instance interface through the device number of the device to determine whether the device belongs to the microservice type;

[0136] If yes, call the microservice offline interface to offline the device, otherwise call the protocol service offline interface to offline the device.

[0137] In the implementation device of the present invention, the diagnosis module 502 is used to: when the data abnormality is diagnosed through the second fault threshold, diagnose the fault type as a computer room fault;

[0138] The processing module 503 is used to:

[0139] Determine a computer room switching strategy corresponding to a computer room failure, and obtain a switching configuration number corresponding to the computer room from the computer room switching strategy;

[0140] Selecting a normally operating computer room from other computer rooms corresponding to the switching configuration number;

[0141] The interface configuration parameters of the selected computer room are obtained, and the interface configuration parameters of the computer room are changed to the obtained interface configuration parameters, so as to switch the traffic or service connected to the computer room to the computer room.

[0142] In the implementation device of the present invention, the acquisition module 501 is used to:

[0143] Based on the first cycle, data of multiple dimensions are captured and stored from at least one system under at least one platform; wherein the system includes at least one computer room, and the computer room includes at least one device.

[0144] In the implementation device of the present invention, the acquisition module 501 is used to:

[0145] Based on the second cycle, the data to be processed is obtained from the data processing center; wherein, based on the third cycle, the data processing center captures and stores data of multiple dimensions from at least one system under at least one platform, and the second cycle is smaller than the third cycle.

[0146] In addition, the specific implementation content of the device described in the embodiment of the present invention has been described in detail in the method described above, so the repeated content will not be described again here.

[0147] Figure 6 An exemplary system architecture 600 is shown in which embodiments of the present invention may be applied.

[0148] like Figure 6 As shown, the system architecture 600 may include terminal devices 601, 602, 603, a network 604 and a server 605 (just an example). The network 604 is used to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. The network 604 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0149] The user can use the terminal devices 601, 602, 603 to interact with the server 605 through the network 604 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 601, 602, 603.

[0150] The terminal devices 601, 602, 603 may be various electronic devices having display screens and supporting web browsing, and the server 605 may be a server providing various services.

[0151] It should be noted that the method provided in the embodiment of the present invention is generally executed by the server 605 , and accordingly, the device is generally set in the server 605 .

[0152] It should be understood that Figure 6The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0153] Reference below Figure 7 , which shows a schematic diagram of the structure of a computer system 700 of a terminal device suitable for implementing an embodiment of the present invention. Figure 7 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0154] like Figure 7 As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage part 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0155] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, a mouse, etc.; an output section 707 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, a modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 710 as needed, so that a computer program read therefrom is installed into the storage section 708 as needed.

[0156] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 709, and / or installed from the removable medium 711. When the computer program is executed by the central processing unit (CPU) 701, the above-mentioned functions defined in the system of the present invention are executed.

[0157] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0158] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0159] The modules involved in the embodiments of the present invention may be implemented by software or hardware. The modules described may also be set in a processor, for example, it may be described as: a processor includes an acquisition module, a diagnosis module, and a processing module. The names of these modules do not constitute a limitation on the modules themselves in some cases, for example, the acquisition module may also be described as a "data acquisition and fault analysis strategy module".

[0160] As another aspect, the present invention further provides a computer-readable medium, which may be included in the device described in the above embodiment; or may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes:

[0161] Acquire the data to be processed, and determine the fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage;

[0162] Analyzing whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnosing the fault type of the data;

[0163] The data is processed using a contingency plan strategy corresponding to the fault type.

[0164] According to the technical solution of the embodiment of the present invention, a strategy of automation as the main and manual as the auxiliary is adopted, based on the data processing center and the decision analysis center, relying on the collected multi-dimensional big data information such as systems, applications, equipment, logs, etc., to discover equipment / computer room hidden dangers in advance:

[0165] 1) Monitor the smallest data granularity and eliminate faults in the early stages, achieving the purpose of automatically discovering, identifying, and processing abnormal data;

[0166] 2) Troubleshooting is no longer restricted by the platform and can be processed in batches. Developers can complete troubleshooting through the mobile terminal at any time;

[0167] 3) Even during non-working hours, it can still maintain highly stable and efficient operation, and solve the delays, omissions, uncertainties and uncontrollable labor costs of manual operations; it frees developers from the heavy operation and maintenance roles, allowing them to focus more on business processing.

[0168] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processing method, characterized in that: include: Acquire the data to be processed, and determine the fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage; Analyze whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnose the fault type of the data; wherein, analyzing whether the data is abnormal based on the fault analysis strategy includes: obtaining a first fault threshold in the fault analysis strategy, judging whether the value in the data exceeds the first fault threshold, and if exceeded, determining that the data is abnormal; if not exceeded, obtaining a device identifier stored in the data, determining the device corresponding to the device identifier and the computer room where the device is located, and then determining other computer rooms under the platform where the computer room is located and other devices located in other computer rooms; determining the first data corresponding to the dimension under the other devices, calculating the difference between the data and the first data, and if the ratio of the difference to the first data exceeds the second fault threshold, determining that the data is abnormal; The data is processed using a contingency plan strategy corresponding to the fault type.

2. The method according to claim 1, characterized in that The diagnosing the fault type of the data includes: when the data is diagnosed as abnormal through the first fault threshold, diagnosing the fault type as a device fault; The processing of the data by using a contingency plan strategy corresponding to the fault type includes: Calling the microservice instance interface through the device number of the device to determine whether the device belongs to the microservice type; If yes, call the microservice offline interface to offline the device, otherwise call the protocol service offline interface to offline the device.

3. The method according to claim 1, characterized in that The diagnosing the fault type of the data includes: when the data is diagnosed as abnormal through the second fault threshold, diagnosing the fault type as a computer room fault; The processing of the data by using a contingency plan strategy corresponding to the fault type includes: Determine a computer room switching strategy corresponding to a computer room failure, and obtain a switching configuration number corresponding to the computer room from the computer room switching strategy; Selecting a normally operating computer room from other computer rooms corresponding to the switching configuration number; The interface configuration parameters of the selected computer room are obtained, and the interface configuration parameters of the computer room are changed to the obtained interface configuration parameters, so as to switch the traffic or service connected to the computer room to the computer room.

4. The method according to claim 1, characterized in that: The obtaining of data to be processed includes: Based on the first cycle, data of multiple dimensions are captured and stored from at least one system under at least one platform; wherein the system includes at least one computer room, and the computer room includes at least one device.

5. The method according to claim 1, characterized in that The obtaining of data to be processed includes: Based on the second cycle, the data to be processed is obtained from the data processing center; wherein, based on the third cycle, the data processing center captures and stores data of multiple dimensions from at least one system under at least one platform, and the second cycle is smaller than the third cycle.

6. A data processing device, characterized in that: include: An acquisition module, used to acquire data to be processed and determine a fault analysis strategy corresponding to the dimension of the data; wherein the dimension is the minimum granularity of data storage; A diagnosis module, used for analyzing whether the data is abnormal based on the fault analysis strategy, and if abnormal, diagnosing the fault type of the data; wherein, the analyzing whether the data is abnormal based on the fault analysis strategy includes: obtaining a first fault threshold in the fault analysis strategy, judging whether the value in the data exceeds the first fault threshold, and if so, determining that the data is abnormal; if not, obtaining a device identifier stored in the data, determining the device corresponding to the device identifier and the computer room where the device is located, and then determining other computer rooms under the platform where the computer room is located and other devices located in other computer rooms; determining first data corresponding to the dimension under the other devices, calculating the difference between the data and the first data, and if the ratio of the difference to the first data exceeds the second fault threshold, determining that the data is abnormal; A processing module is used to process the data using a contingency plan strategy corresponding to the fault type.

7. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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