A maintenance method and system for ERP server

Abstract

The present invention provides a maintenance method and system for an ERP server, which relate to the field of ERP technology. In the present invention, a state monitoring process is performed on a target ERP server to output server state data corresponding to the target ERP server, and the server state data is used to characterize whether the target ERP server currently has a phenomenon of discarding received data access requests. If the server state data characterizes that the target ERP server currently has a phenomenon of discarding received data access requests, a state prediction process is performed on the target ERP server to output state prediction data corresponding to the target ERP server, and the state prediction data is used to characterize the number of data access requests received by the target ERP server within a preset time period. The target ERP server is maintained according to the state prediction data. Based on the above method, the problem of poor maintenance effect of the ERP server in the prior art can be improved.

Description

Technical Field

[0001] The present invention relates to the field of ERP technology, and in particular to a maintenance method and system for an ERP server. Background Art

[0002] Enterprise Resource Planning (ERP) system refers to a management platform that is based on information technology and provides decision-making and operation means for enterprise decision-makers and employees with a systematic management concept. With the increasing application of ERP systems, a large amount of information resources of enterprises are stored in ERP systems. The storage of a large amount of information resources results in a large number of access requests being received, which easily leads to the problem of the corresponding access request processing equipment crashing. Therefore, corresponding maintenance is required. However, in the prior art, there is a problem of poor maintenance effect. Summary of the invention

[0003] In view of this, an object of the present invention is to provide a maintenance method and system for an ERP server, so as to improve the problem of poor maintenance effect of the ERP server in the prior art.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0005] A maintenance method for an ERP server, comprising:

[0006] Performing status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server, wherein the server status data is used to indicate whether the target ERP server currently discards the received data access request;

[0007] If the server status data indicates that the target ERP server currently discards the received data access requests, a status prediction process is performed on the target ERP server to output status prediction data corresponding to the target ERP server, wherein the status prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period;

[0008] The target ERP server is maintained according to the status prediction data.

[0009] In some preferred embodiments, in the above-mentioned maintenance method for the ERP server, the step of performing status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server includes:

[0010] Monitoring a pre-configured target communication port to output a corresponding first monitoring result, wherein the target communication port is used to receive a data access request processing result reported by a target ERP server, and the target ERP server is used to output the corresponding data access request processing result to the target communication port in the event that a received data access request is discarded;

[0011] If the first monitoring result is a data access request processing result reported by the target ERP server, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently discards the received data access request;

[0012] If the first monitoring result is that the data access request processing result reported by the target ERP server is not received, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently does not discard the received data access request.

[0013] In some preferred embodiments, in the above maintenance method for the ERP server, if the server status data indicates that the target ERP server currently discards the received data access request, the step of performing status prediction processing on the target ERP server to output status prediction data corresponding to the target ERP server includes:

[0014] If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period;

[0015] According to the multiple historical data sets, status prediction processing is performed on the target ERP server to output status prediction data corresponding to the target ERP server.

[0016] In some preferred embodiments, in the above maintenance method for the ERP server, the step of performing status prediction processing on the target ERP server according to the multiple historical data sets to output status prediction data corresponding to the target ERP server includes:

[0017] Sorting the plurality of historical data sets according to the order of the corresponding historical time periods from early to late, so as to form a historical set sequence corresponding to the plurality of historical data sets;

[0018] Performing set construction processing on data access requests received within a time period whose current length is the preset time length to form a corresponding current data set;

[0019] According to the historical set sequence and the current data set, the target ERP server is subjected to state prediction processing to output state prediction data corresponding to the target ERP server.

[0020] In some preferred embodiments, in the above maintenance method for the ERP server, the step of performing status prediction processing on the target ERP server according to the historical set sequence and the current data set to output status prediction data corresponding to the target ERP server includes:

[0021] Selecting a last first number of historical data sets from the historical set sequence, and sorting the first number of historical data sets and the current data set according to the chronological order of the corresponding time periods from early to late, so as to form a corresponding first set sequence, wherein the first set sequence includes a second number of data sets;

[0022] Performing sliding window processing on the historical set sequence according to the second number to form a plurality of second set sequences corresponding to the historical set sequence, wherein each of the plurality of second set sequences includes the second number of historical data sets;

[0023] For each of the second set sequences in the plurality of second set sequences, performing sequence similarity calculation processing on the second set sequence and the first set sequence to output a sequence similarity corresponding to the second set sequence, and performing size comparison on the sequence similarities corresponding to the second set sequence to output a size comparison result corresponding to the second set sequence;

[0024] For each of the plurality of second set sequences, if the size comparison result corresponding to the second set sequence indicates that the sequence similarity corresponding to the second set sequence is greater than or equal to the preset similarity, the second set sequence is determined as a candidate second set sequence, and a positive correlation coefficient determination process is performed according to the sequence similarity corresponding to the candidate second set sequence, so as to output a first matching coefficient corresponding to the candidate second set sequence;

[0025] For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and fusing processing is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence;

[0026] A fusion matching coefficient with a maximum value is extracted from the fusion matching coefficients corresponding to each of the candidate second set sequences, and the fusion matching coefficient is configured as a target fusion matching coefficient. The candidate second set sequence corresponding to the target fusion matching coefficient is then configured as a target second set sequence. In addition, state prediction processing is performed on the target ERP server according to the target second set sequence to output a state prediction number corresponding to the target ERP server.

[0027] In some preferred embodiments, in the above maintenance method for the ERP server, the step of performing matching processing on each of the candidate second set sequences according to the time period corresponding to the candidate second set sequence and the time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and performing fusion processing on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence includes:

[0028] For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence, so as to output a second matching coefficient corresponding to the candidate second set sequence;

[0029] For each of the candidate second set sequences, a weighted sum calculation is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence.

[0030] In some preferred embodiments, in the above maintenance method for the ERP server, the step of performing maintenance processing on the target ERP server according to the state prediction data includes:

[0031] Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server.

[0032] In the case where the target ERP server needs to be maintained, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

[0033] The embodiment of the present invention further provides a maintenance system for an ERP server, comprising:

[0034] A status monitoring module is used to perform status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server, wherein the server status data is used to indicate whether the target ERP server currently discards the received data access request;

[0035] A state prediction module, configured to perform state prediction processing on the target ERP server if the server state data indicates that the target ERP server currently discards received data access requests, so as to output state prediction data corresponding to the target ERP server, wherein the state prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period;

[0036] The server maintenance processing module is used to perform maintenance processing on the target ERP server according to the status prediction data.

[0037] In some preferred embodiments, in the above maintenance system for the ERP server, the state prediction module is specifically used for:

[0038] If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period;

[0039] According to the multiple historical data sets, status prediction processing is performed on the target ERP server to output status prediction data corresponding to the target ERP server.

[0040] In some preferred embodiments, in the above maintenance system for the ERP server, the server maintenance processing module is specifically used for:

[0041] Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server.

[0042] In the case where the target ERP server needs to be maintained, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

[0043] The embodiment of the present invention provides a maintenance method and system for an ERP server. First, a state monitoring process is performed on the target ERP server to output server state data corresponding to the target ERP server. The server state data is used to characterize whether the target ERP server currently discards the received data access request. Then, if the server state data characterizes that the target ERP server currently discards the received data access request, a state prediction process is performed on the target ERP server to output state prediction data corresponding to the target ERP server. The state prediction data is used to characterize the number of data access requests received by the target ERP server within a preset time period. Finally, the target ERP server is maintained according to the state prediction data. The target ERP server is maintained according to the state prediction data obtained by predicting the state in a future period of time, so that the target ERP server after maintenance can be adapted to a future period of time. Compared with the conventional technical solution of performing maintenance processing only according to the current state data, the maintenance effect can be improved, thereby improving the problem of poor maintenance effect of the ERP server in the prior art.

[0044] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a structural block diagram of the ERP server management platform provided in an embodiment of the present invention.

[0046] Figure 2 A flowchart of the steps of a maintenance method for an ERP server provided in an embodiment of the present invention.

[0047] Figure 3 A schematic diagram of various modules included in a maintenance system for an ERP server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, an embodiment of the present invention provides an ERP server management platform, wherein the ERP server management platform may include a memory and a processor.

[0051] In detail, the memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The memory can store at least one software function module (computer program) that can exist in the form of software or firmware. The processor can be used to execute the executable computer program stored in the memory, thereby implementing the maintenance method for the ERP server provided in the embodiment of the present invention.

[0052] It can be selected that, in some embodiments, the memory can be, but not limited to, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electric erasable programmable read-only memory (EEPROM), etc. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0053] Optionally, in some implementations, the ERP server management platform may be one or more servers with data processing capabilities.

[0054] Combination Figure 2 The embodiment of the present invention also provides a maintenance method for an ERP server, which can be applied to the above-mentioned ERP server management platform. The method steps defined in the process related to the maintenance method for an ERP server can be implemented by the ERP server management platform.

[0055] The following will Figure 2 The specific process shown is explained in detail.

[0056] Step 110: Perform status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server.

[0057] In an embodiment of the present invention, the ERP server management platform can perform status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server. The server status data is used to indicate whether the target ERP server currently discards the received data access request.

[0058] Step 120: If the server status data indicates that the target ERP server currently discards the received data access request, a status prediction process is performed on the target ERP server to output status prediction data corresponding to the target ERP server.

[0059] In an embodiment of the present invention, the ERP server management platform can perform state prediction processing on the target ERP server to output state prediction data corresponding to the target ERP server when the server state data indicates that the target ERP server currently discards received data access requests. The state prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period.

[0060] Step 130: Perform maintenance processing on the target ERP server according to the status prediction data.

[0061] In the embodiment of the present invention, the ERP server management platform may perform maintenance processing on the target ERP server according to the status prediction data.

[0062] Based on the above-mentioned maintenance method for ERP server, first, the target ERP server is subjected to status monitoring processing to output the server status data corresponding to the target ERP server, and the server status data is used to characterize whether the target ERP server currently discards the received data access request. Then, if the server status data characterizes that the target ERP server currently discards the received data access request, the target ERP server is subjected to status prediction processing to output the status prediction data corresponding to the target ERP server, and the status prediction data is used to characterize the number of data access requests received by the target ERP server within a preset time period. Finally, the target ERP server is subjected to maintenance processing according to the status prediction data. By performing maintenance processing on the target ERP server according to the status prediction data obtained by predicting the status within a period of time in the future, the target ERP server after maintenance can be adapted to a period of time in the future. Compared with the conventional technical solution of performing maintenance processing only according to the current status data, the maintenance effect can be improved, thereby improving the problem of poor maintenance effect of the ERP server in the prior art.

[0063] Optionally, in some implementations, step 110 may include the following:

[0064] Monitoring a pre-configured target communication port to output a corresponding first monitoring result, wherein the target communication port is used to receive a data access request processing result reported by a target ERP server, and the target ERP server is used to output the corresponding data access request processing result to the target communication port in the event that a received data access request is discarded;

[0065] If the first monitoring result is a data access request processing result reported by the target ERP server, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently discards the received data access request;

[0066] If the first monitoring result is that the data access request processing result reported by the target ERP server is not received, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently does not discard the received data access request.

[0067] Optionally, in some implementations, step 120 may include the following:

[0068] If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period;

[0069] According to the multiple historical data sets, status prediction processing is performed on the target ERP server to output status prediction data corresponding to the target ERP server.

[0070] Optionally, in some implementations, the step of performing status prediction processing on the target ERP server according to the multiple historical data sets to output status prediction data corresponding to the target ERP server may include the following:

[0071] Sorting the plurality of historical data sets according to the order of the corresponding historical time periods from early to late, so as to form a historical set sequence corresponding to the plurality of historical data sets;

[0072] Performing set construction processing on data access requests received within a time period whose current length is the preset time length to form a corresponding current data set;

[0073] According to the historical set sequence and the current data set, the target ERP server is subjected to state prediction processing to output state prediction data corresponding to the target ERP server.

[0074] Optionally, in some implementations, the step of performing state prediction processing on the target ERP server according to the historical set sequence and the current data set to output state prediction data corresponding to the target ERP server may include the following:

[0075] Selecting a last first number of historical data sets from the historical set sequence, and sorting the first number of historical data sets and the current data set according to the chronological order of the corresponding time periods from early to late, so as to form a corresponding first set sequence, wherein the first set sequence includes a second number of data sets;

[0076] Performing sliding window processing on the historical set sequence according to the second number to form a plurality of second set sequences corresponding to the historical set sequence, wherein each of the plurality of second set sequences includes the second number of historical data sets;

[0077] For each of the second set sequences in the plurality of second set sequences, performing sequence similarity calculation processing on the second set sequence and the first set sequence to output a sequence similarity corresponding to the second set sequence, and performing size comparison on the sequence similarities corresponding to the second set sequence to output a size comparison result corresponding to the second set sequence;

[0078] For each of the plurality of second set sequences, if the size comparison result corresponding to the second set sequence indicates that the sequence similarity corresponding to the second set sequence is greater than or equal to the preset similarity, the second set sequence is determined as a candidate second set sequence, and a positive correlation coefficient determination process is performed according to the sequence similarity corresponding to the candidate second set sequence, so as to output a first matching coefficient corresponding to the candidate second set sequence;

[0079] For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and fusion processing (such as weighted sum calculation) is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence;

[0080] A fusion matching coefficient with a maximum value is extracted from the fusion matching coefficients corresponding to each of the candidate second set sequences, and the fusion matching coefficient is configured as a target fusion matching coefficient. The candidate second set sequence corresponding to the target fusion matching coefficient is then configured as a target second set sequence, and state prediction processing is performed on the target ERP server according to the target second set sequence to output state prediction data corresponding to the target ERP server (for example, the number of data access requests included in the data set after the last data set in the target second set sequence can be used as the state prediction data).

[0081] Alternatively, in some implementations, the step of performing sequence similarity calculation processing on each of the second set sequences in the plurality of second set sequences and the first set sequence to output the sequence similarity corresponding to the second set sequence may include the following:

[0082] For each sequence sorting position, performing a degree of overlap calculation on the data set at the sequence sorting position in the first set sequence and the data set at the sequence sorting position in the second set sequence to output a set overlap corresponding to the sequence sorting position, and then performing a first similarity calculation process according to the set overlap corresponding to each sequence sorting position to output a first similarity between the first set sequence and the second set sequence;

[0083] For each data set in the first set sequence, classify the multiple data access requests included in the data set according to similarities of request objects between the multiple data access requests included in the data set to form at least one first data subset corresponding to the data set, each of the at least one first data subset including at least one data access request;

[0084] For each data set in the second set sequence, classify the multiple data access requests included in the data set according to similarities between the multiple data access requests included in the data set regarding the request object, so as to form at least one second data subset corresponding to the data set, each of the at least one second data subset including at least one data access request;

[0085] For each of the first data subsets, based on similarities between the data access requests included in the first data subset regarding request objects, extract a data access request with the greatest similarity to other data access requests from the first data subset, and configure the data access request as the first data access request corresponding to the first data subset;

[0086] For each of the second data subsets, based on similarities between the data access requests included in the second data subset regarding request objects, extract a data access request with the greatest similarity to other data access requests from the second data subset, and configure the data access request as the second data access request corresponding to the second data subset;

[0087] For each data set in the first set sequence, set construction processing is performed according to a first data access request corresponding to each first data subset in at least one first data subset corresponding to the data set, so as to output a first updated data set corresponding to the data set; for each data set in the second set sequence, set construction processing is performed according to a second data access request corresponding to each second data subset in at least one second data subset corresponding to the data set, so as to output a second updated data set corresponding to the data set;

[0088] For each sequence sorting position, a first updated data set corresponding to the data set at the sequence sorting position in the first set sequence and a second updated data set corresponding to the data set at the sequence sorting position in the second set sequence are calculated to output the update set coincidence corresponding to the sequence sorting position, and a second similarity calculation process is performed based on the update set coincidence corresponding to each sequence sorting position to output a second similarity between the first set sequence and the second set sequence;

[0089] A weighted sum calculation is performed on the first similarity and the second similarity between the first set sequence and the second set sequence to output the sequence similarity between the first set sequence and the second set sequence.

[0090] Optionally, in some implementations, the step of performing matching processing on each of the candidate second set sequences according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and performing fusion processing on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence may include the following:

[0091] For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence, so as to output a second matching coefficient corresponding to the candidate second set sequence;

[0092] For each of the candidate second set sequences, a weighted sum calculation is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence.

[0093] Optionally, in some implementations, step 130 may include the following:

[0094] Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server.

[0095] In the case where the target ERP server needs to be maintained, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

[0096] Combination Figure 3 The embodiment of the present invention further provides a maintenance system for an ERP server, which can be applied to the above-mentioned ERP server management platform. The maintenance system for an ERP server can include the following modules:

[0097] A status monitoring module is used to perform status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server, wherein the server status data is used to indicate whether the target ERP server currently discards the received data access request;

[0098] A state prediction module, configured to perform state prediction processing on the target ERP server if the server state data indicates that the target ERP server currently discards received data access requests, so as to output state prediction data corresponding to the target ERP server, wherein the state prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period;

[0099] The server maintenance processing module is used to perform maintenance processing on the target ERP server according to the status prediction data.

[0100] Optionally, in some implementations, the status monitoring module is specifically used to:

[0101] Monitoring a pre-configured target communication port to output a corresponding first monitoring result, wherein the target communication port is used to receive a data access request processing result reported by a target ERP server, and the target ERP server is used to output the corresponding data access request processing result to the target communication port in the event that a received data access request is discarded;

[0102] If the first monitoring result is a data access request processing result reported by the target ERP server, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently discards the received data access request;

[0103] If the first monitoring result is that the data access request processing result reported by the target ERP server is not received, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently does not discard the received data access request.

[0104] Optionally, in some embodiments, the state prediction module is specifically used to:

[0105] If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period;

[0106] According to the multiple historical data sets, status prediction processing is performed on the target ERP server to output status prediction data corresponding to the target ERP server.

[0107] Optionally, in some implementations, the server maintenance processing module is specifically used to:

[0108] Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server.

[0109] In the case where the target ERP server needs to be maintained, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

[0110] In summary, the present invention provides a maintenance method and system for an ERP server. First, a state monitoring process is performed on the target ERP server to output the server state data corresponding to the target ERP server. The server state data is used to characterize whether the target ERP server currently discards the received data access request. Then, if the server state data characterizes that the target ERP server currently discards the received data access request, a state prediction process is performed on the target ERP server to output the state prediction data corresponding to the target ERP server. The state prediction data is used to characterize the number of data access requests received by the target ERP server within a preset time period. Finally, the target ERP server is maintained according to the state prediction data. The target ERP server is maintained according to the state prediction data obtained by predicting the state in a future period of time. The target ERP server after maintenance can be adapted to a future period of time. Compared with the conventional technical solution of performing maintenance processing only according to the current state data, the maintenance effect can be improved, thereby improving the problem of poor maintenance effect of the ERP server in the prior art.

[0111] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A maintenance method for an ERP server, characterized in that: include: Performing status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server, wherein the server status data is used to indicate whether the target ERP server currently discards the received data access request; If the server status data indicates that the target ERP server currently discards the received data access requests, a status prediction process is performed on the target ERP server to output status prediction data corresponding to the target ERP server, wherein the status prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period; Performing maintenance processing on the target ERP server according to the status prediction data; If the server status data indicates that the target ERP server currently discards the received data access request, the step of performing status prediction processing on the target ERP server to output status prediction data corresponding to the target ERP server includes: If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period; According to the plurality of historical data sets, performing state prediction processing on the target ERP server to output state prediction data corresponding to the target ERP server; The step of performing status prediction processing on the target ERP server according to the plurality of historical data sets to output status prediction data corresponding to the target ERP server comprises: Sorting the plurality of historical data sets according to the order of the corresponding historical time periods from early to late, so as to form a historical set sequence corresponding to the plurality of historical data sets; Performing set construction processing on data access requests received within a time period whose current length is the preset time length to form a corresponding current data set; According to the historical set sequence and the current data set, a state prediction process is performed on the target ERP server to output state prediction data corresponding to the target ERP server; The step of performing state prediction processing on the target ERP server according to the historical set sequence and the current data set to output state prediction data corresponding to the target ERP server includes: Selecting a last first number of historical data sets from the historical set sequence, and sorting the first number of historical data sets and the current data set according to the chronological order of the corresponding time periods from early to late, so as to form a corresponding first set sequence, wherein the first set sequence includes a second number of data sets; Performing sliding window processing on the historical set sequence according to the second number to form a plurality of second set sequences corresponding to the historical set sequence, wherein each of the plurality of second set sequences includes the second number of historical data sets; For each of the second set sequences in the plurality of second set sequences, performing sequence similarity calculation processing on the second set sequence and the first set sequence to output a sequence similarity corresponding to the second set sequence, and performing size comparison on the sequence similarities corresponding to the second set sequence to output a size comparison result corresponding to the second set sequence; For each of the plurality of second set sequences, if the size comparison result corresponding to the second set sequence indicates that the sequence similarity corresponding to the second set sequence is greater than or equal to the preset similarity, the second set sequence is determined as a candidate second set sequence, and a positive correlation coefficient determination process is performed according to the sequence similarity corresponding to the candidate second set sequence, so as to output a first matching coefficient corresponding to the candidate second set sequence; For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and fusing processing is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence; A fusion matching coefficient with a maximum value is extracted from the fusion matching coefficients corresponding to each of the candidate second set sequences, and the fusion matching coefficient is configured as a target fusion matching coefficient. The candidate second set sequence corresponding to the target fusion matching coefficient is then configured as a target second set sequence. In addition, state prediction processing is performed on the target ERP server according to the target second set sequence to output a state prediction number corresponding to the target ERP server.

2. The maintenance method for an ERP server according to claim 1, characterized in that: The step of performing status monitoring processing on the target ERP server to output server status data corresponding to the target ERP server includes: Monitoring a pre-configured target communication port to output a corresponding first monitoring result, wherein the target communication port is used to receive a data access request processing result reported by a target ERP server, and the target ERP server is used to output the corresponding data access request processing result to the target communication port in the event that a received data access request is discarded; If the first monitoring result is a data access request processing result reported by the target ERP server, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently discards the received data access request; If the first monitoring result is that the data access request processing result reported by the target ERP server is not received, the server status data corresponding to the target ERP server is configured to indicate that the target ERP server currently does not discard the received data access request.

3. The maintenance method for an ERP server according to claim 1, characterized in that: The step of performing matching processing on each of the candidate second set sequences according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and performing fusion processing on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence includes: For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence, so as to output a second matching coefficient corresponding to the candidate second set sequence; For each of the candidate second set sequences, a weighted sum calculation is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fusion matching coefficient corresponding to the candidate second set sequence.

4. The maintenance method for an ERP server according to any one of claims 1 to 3, characterized in that: The step of performing maintenance processing on the target ERP server according to the status prediction data includes: Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server. In the case where maintenance processing is required for the target ERP server, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

5. A maintenance system for an ERP server, characterized in that: include: A status monitoring module is used to perform status monitoring processing on a target ERP server to output server status data corresponding to the target ERP server, wherein the server status data is used to indicate whether the target ERP server currently discards a received data access request; A state prediction module, configured to perform state prediction processing on the target ERP server if the server state data indicates that the target ERP server currently discards received data access requests, so as to output state prediction data corresponding to the target ERP server, wherein the state prediction data is used to indicate the number of data access requests received by the target ERP server within a preset time period; A server maintenance processing module, used for performing maintenance processing on the target ERP server according to the status prediction data; The state prediction module is specifically used for: If the server status data indicates that the target ERP server currently discards the received data access request, multiple historical data sets corresponding to the target ERP server are extracted, each of the multiple historical data sets includes each historical data access request received by the target ERP server in a corresponding historical time period, and the length of the historical time period is equal to the preset time period; According to the plurality of historical data sets, performing state prediction processing on the target ERP server to output state prediction data corresponding to the target ERP server; The performing state prediction processing on the target ERP server according to the plurality of historical data sets to output state prediction data corresponding to the target ERP server includes: Sorting the plurality of historical data sets according to the order of the corresponding historical time periods from early to late, so as to form a historical set sequence corresponding to the plurality of historical data sets; Performing set construction processing on data access requests received within a time period whose current length is the preset time length to form a corresponding current data set; According to the historical set sequence and the current data set, a state prediction process is performed on the target ERP server to output state prediction data corresponding to the target ERP server; The performing state prediction processing on the target ERP server according to the historical set sequence and the current data set to output state prediction data corresponding to the target ERP server includes: Selecting a last first number of historical data sets from the historical set sequence, and sorting the first number of historical data sets and the current data set according to the chronological order of the corresponding time periods from early to late, so as to form a corresponding first set sequence, wherein the first set sequence includes a second number of data sets; Performing sliding window processing on the historical set sequence according to the second number to form a plurality of second set sequences corresponding to the historical set sequence, wherein each of the plurality of second set sequences includes the second number of historical data sets; For each of the second set sequences in the plurality of second set sequences, performing sequence similarity calculation processing on the second set sequence and the first set sequence to output a sequence similarity corresponding to the second set sequence, and performing size comparison on the sequence similarities corresponding to the second set sequence to output a size comparison result corresponding to the second set sequence; For each of the plurality of second set sequences, if the size comparison result corresponding to the second set sequence indicates that the sequence similarity corresponding to the second set sequence is greater than or equal to the preset similarity, the second set sequence is determined as a candidate second set sequence, and a positive correlation coefficient determination process is performed according to the sequence similarity corresponding to the candidate second set sequence, so as to output a first matching coefficient corresponding to the candidate second set sequence; For each of the candidate second set sequences, matching processing is performed according to a time period corresponding to the candidate second set sequence and a time period corresponding to the second set sequence to output a second matching coefficient corresponding to the candidate second set sequence, and fusing processing is performed on the second matching coefficient corresponding to the candidate second set sequence and the first matching coefficient corresponding to the candidate second set sequence to output a fused matching coefficient corresponding to the candidate second set sequence; A fusion matching coefficient with a maximum value is extracted from the fusion matching coefficients corresponding to each of the candidate second set sequences, and the fusion matching coefficient is configured as a target fusion matching coefficient. The candidate second set sequence corresponding to the target fusion matching coefficient is then configured as a target second set sequence. In addition, state prediction processing is performed on the target ERP server according to the target second set sequence to output a state prediction number corresponding to the target ERP server.

6. The maintenance system for an ERP server according to claim 5, characterized in that: The server maintenance processing module is specifically used for: Compare the state prediction data with pre-configured standard state data to determine whether maintenance processing is required for the target ERP server. If the state prediction data is greater than or equal to the standard state data, maintenance processing is required for the target ERP server. If the state prediction data is less than the standard state data, maintenance processing is not required for the target ERP server. In the case where maintenance processing is required for the target ERP server, the pre-configured standby data access thread and the currently used data access thread are used to process the data access request within a preset time period thereafter.

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