Big data-based construction engineering construction progress information management system and method

Abstract

The application discloses a building engineering construction progress information management system and method based on big data, and relates to the technical field of building data processing. The application solves the problem of not comprehensively confirming and analyzing the time characteristics associated with each reporting node. Through analysis of the historical period reporting volume curve and determination of the volume period, the application can effectively identify peak and trough periods of data reporting, thereby reasonably allocating server resources, avoiding data congestion and processing delays, and significantly improving data processing efficiency. Through real-time monitoring and analysis of the server load rate, the application can timely discover periods of excessively high load and achieve load balancing by adjusting the reporting time of different progress reporting nodes, thereby avoiding server overload. Through determination of the volume period and optimization of the load period, the application can provide more scientific and accurate data support for managers, help them make more reasonable decisions, and improve the scientificity and effectiveness of construction progress management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of building data processing, in particular to a building engineering construction progress information management system and method based on big data. BACKGROUND

[0002] In the field of modern building engineering, with the acceleration of urbanization and the continuous rise of building scale and complexity, construction progress management is facing unprecedented severe challenges, and data processing problems have become the key bottleneck restricting its efficient operation.

[0003] Building engineering projects themselves have high complexity, covering a series of closely related links from planning and design, raw material procurement, site construction to completion and acceptance; each link continuously produces massive data, for example, detailed building drawing data is generated in the design stage, including accurate size annotations, complex structure design information, etc.; raw material procurement process involves various material supplier information, price fluctuation data, procurement batch and quantity records, etc.; construction site produces real-time data such as operation parameters of construction equipment, attendance and work time records of construction personnel, progress monitoring data of each construction part, etc.; these data not only come from a wide range of sources, but also have a wide variety of data types, including structured table data, semi-structured text reports, and unstructured image and video data, etc.

[0004] When monitoring the construction process of building engineering, designated monitoring sensors or personnel are used for data reporting and uploading, but the time characteristics associated with each reporting node are not comprehensively confirmed and analyzed, which may cause a large amount of data to be reported at the same time, resulting in excessive load on the corresponding server when managing information, affecting the performance of the server, and in severe cases, causing data loss or downtime. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a building engineering construction progress information management system and method based on big data, which solves the problem of not comprehensively confirming and analyzing the time characteristics associated with each reporting node.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a building engineering construction progress information management method based on big data, comprising the following steps:

[0007] Step 1, based on the volume data associated with different engineering progress reporting nodes, confirm the historical period reporting volume curve from the historical reporting process in detail as follows:

[0008] Confirm a set of historical periods based on the current time, the historical period is a preset period, confirm the capacity of different volume data received by the server at different times in the historical period, and sort the data capacity associated with different times in order according to the chronological relationship;

[0009] Confirm the point position of the sorted data capacity in the two-dimensional coordinate system, and connect the points to confirm the corresponding report volume curve. The horizontal coordinate axis of the coordinate system is the time line, and the vertical coordinate axis is the data capacity. The time is a preset time;

[0010] Based on the curve characteristics of the report volume curve, determine the associated peak point, and confirm the regular characteristics of the adjacent time associated between the associated peak points, lock the volume period, and the determination method is:

[0011] Based on the determined report volume curve, confirm the associated mean line in real time, and confirm the double mean line based on the mean line. The mean line and the double mean line are perpendicular to the vertical coordinate axis. The mean value associated with the double mean line is twice the mean value of the mean line. The peak point above the double mean line is marked as the associated peak point;

[0012] Confirm the time associated with several associated peak points, and sort several groups of time according to the chronological relationship to confirm the time sequence;

[0013] Regularly check the time sequence, execute several groups of checking processes: variance processing of the specific time length between several adjacent times in the time sequence, confirm the variance characteristics;

[0014] Randomly combine the times associated in the time sequence, denoted as combined time. The combined time can include a single time or multiple times. Take the end time of the combined time as the check time. From the first time of the time sequence, combine the confirmed several groups of check times, confirm the specific time length between several adjacent times, and process the confirmed several groups of specific time length by variance to confirm the variance characteristics. Confirm the variance characteristics associated with different checking processes in order;

[0015] Confirm the variance characteristics associated in several groups of checking processes to the standard. The checking process that meets the condition: variance characteristics ≤ Y1 is marked as a qualified process, Y1 is a preset value. If there is no qualified process, generate a feature recognition error signal for display;

[0016] If there is only one group of qualified processes, the associated peak points corresponding to the check time and the first time in the qualified process are marked as feature peak points;

[0017] If there are multiple groups of target reaching processes, the group with the smallest variance feature is selected as the selected process, and the corresponding correlation peak point of the selected process at the verification time and the first time is recorded as the feature peak point;

[0018] The time period between adjacent feature peak points is recorded as the volume period;

[0019] Step 2, based on the volume period confirmed in the historical period, the server load rate associated with each volume period is analyzed, the load time in the volume period is confirmed, and based on the specific proportion of the load time, it is evaluated whether the volume load optimization is needed, and the specific method is:

[0020] The server load rate associated with different time points in different volume periods is numerically confirmed, and the server load rate associated with different time points in different volume periods is denoted as Fz i-q , where i represents different volume periods, and q represents different time points, and Fz i-q ≥Y2 is recorded as the load time, and Y2 is a preset value;

[0021] The proportion of time corresponding to the load time in the corresponding volume period is confirmed, and ZB i =total number of load times ÷ total number of volume periods, and the proportion of time corresponding to the load time in the corresponding volume period is confirmed ZB i

[0022] The proportion of time ZB i associated with multiple volume periods is processed, and the volume period feature is confirmed, if the volume period feature is greater than or equal to 50%, it means that the volume load optimization is needed, otherwise, no processing is needed, which means that the server can normally process the load and digest the large volume of data;

[0023] Step 3, the average value of the different server load rates associated with different time points in several groups of volume periods is processed, the feature average value is confirmed, if the feature average value is greater than 40%, it means that the server computing power is insufficient; if the feature average value is less than or equal to 40%, the time feature of the progress reporting node is adjusted, and the specific adjustment method is performed by Step 4.

[0024] Step 4, the load time period associated with the volume period is confirmed, and the feature node is selected from the progress reporting node associated with the load time, and the feature time is selected in the volume period based on the specific number of feature nodes, and the specific method is:

[0025] A group of volume periods is randomly selected for processing, and the continuously appearing load time is confirmed as a load time period based on the several groups of load time confirmed in the volume period;​

[0026] The progress reporting node of the volume data reporting in the load period is marked as a pending node, and the reporting period T of the pending node is confirmed k Wherein k represents different pending nodes, the period length of the volume period is marked as ST, and the following is met: 0.7*ST≤T k The pending node meeting the following: 0.7*ST≤T is marked as a feature node

[0027] Based on the number G of the feature nodes, the period length ST is equally divided into G equal periods, the time with the lowest volume data reporting amount in each equal period is confirmed, and is recorded as a feature time, the feature nodes and the feature times are randomly combined one by one, and the combination list after one-to-one combination is displayed.

[0028] Preferably, the building engineering construction progress information management system based on big data comprises:

[0029] The curve drawing end confirms the reporting volume curve of the historical period from the historical reporting process based on the volume data associated with different engineering progress reporting nodes

[0030] The volume period confirmation end locks the associated peak points existing in the internal based on the curve characteristics of the reporting volume curve, and confirms the adjacent time associated between the associated peak points, locks the time period with rules and records it as a volume period

[0031] The load optimization analysis end performs feature analysis on the server load rate associated in each volume period based on the volume period confirmed in the historical period, confirms the load time in the volume period, and evaluates whether volume load optimization is needed based on the specific proportion of the load time

[0032] The feasibility confirmation end performs mean processing on the server load rate in the corresponding volume period based on the different server load rates associated in different volume periods, and performs load optimization feasibility confirmation, based on the confirmation result, different load optimization modes are executed

[0033] The combination list output end confirms the load period associated in the volume period, selects the feature node from the progress reporting node associated in the load time, selects the feature time in the volume period based on the specific number of the feature node, and synchronously combines the feature node and the feature time into a combination list and outputs.

[0034] The application provides a building engineering construction progress information management system and method based on big data. Compared with the prior art, the following beneficial effects are possessed:

[0035] The application can effectively identify the peak period and the trough period of data reporting through the analysis of the historical period volume reporting curve and the determination of the volume period, thereby reasonably allocating server resources, avoiding data congestion and processing delay, and significantly improving data processing efficiency.

[0036] Through real-time monitoring and analysis of the server load rate, the period of excessive load can be found in time, and load balancing can be achieved by adjusting the reporting time of different progress reporting nodes, thereby avoiding server overload and ensuring the stability and reliability of data processing.

[0037] Through the determination of the volume period and the optimization of the load period, more scientific and accurate data support can be provided to the management personnel, helping them make more reasonable decisions and improve the scientificity and effectiveness of construction progress management. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The figure is a schematic diagram of the method of the application;

[0039] Figure 2 The figure is a schematic diagram of the principle framework of the application. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application. EMBODIMENT

[0041] Please refer to Figure 1 The application provides a building engineering construction progress information management method based on big data, including the following steps:

[0042] Step 1, based on the volume data associated with different engineering progress reporting nodes, the reporting volume curve of the historical period is confirmed from the historical reporting process, and the volume period is locked based on the curve characteristics of the reporting volume curve. Specifically, in the construction site, different construction nodes have different monitoring sensors, and the reporting periods associated with different monitoring sensors are different and the volume data reported are different. When the periods of multiple large volume reporting volume data coincide, the processing characteristics of the corresponding time stage will be too loaded, thereby seriously restricting the information management and data processing process of the entire reporting entry, and in severe cases, data disorder may occur. Therefore, in order to effectively avoid such situations, the progress analysis is carried out based on the historical process data, the associated load process is evaluated whether it is reasonable and meets the standard, and comprehensive management is carried out based on the evaluation result.

[0043] In Step 1, the detailed determination method of the historical period reporting volume curve is determined as follows:

[0044] Based on the current time, a group of historical periods is confirmed, and the historical period is a preset period, generally 30 days. The specific value is set by the operator according to the actual construction site. The capacity of different volume data received by the server at different times in the historical period is confirmed, and the data capacity associated with different times is sequentially sorted according to the chronological relationship.

[0045] The data capacity after sorting is confirmed in the two-dimensional coordinate system, and the point is connected to confirm the corresponding reporting volume curve. The horizontal coordinate axis of the coordinate system is the time line, and the vertical coordinate axis is the data capacity. The time is a preset time, generally 1 min or 5 min, etc. The specific time is determined by the operator.

[0046] In Step 1, the detailed determination method of the volume period is determined as follows:

[0047] Based on the determined reporting volume curve, the associated mean line is confirmed in real time (that is, the data in the corresponding historical period is processed by mean value to confirm the mean line), and the double mean line is confirmed based on the mean line. The mean line and the double mean line are both perpendicular to the vertical coordinate axis (that is, the Y axis). The mean value associated with the double mean line is twice the mean value of the mean line. The peak point above the double mean line is marked as the associated peak point.

[0048] The time associated with the associated peak point is confirmed, and the time sequence is confirmed according to the chronological relationship.

[0049] The time sequence is checked for regularity, and a plurality of checking processes are performed: the specific time length between a plurality of adjacent time points in the time sequence is processed by variance to confirm the variance characteristics. The processing method of variance is common in the prior art, so it is not described in detail here.

[0050] Randomly combine the time points associated in the time point sequence, denoted as combined time points, which can include a single time point or multiple time points. The terminal time point of the combined time point (the order of time can confirm the relationship between the corresponding time points, so the terminal time point can be confirmed) is the verification time point. Starting from the first time point of the time point sequence, combine the confirmed several groups of verification time points, confirm the specific time length between several adjacent time points (here, adjacent time points include the first time point and the verification time point), and perform variance processing on the confirmed several groups of specific time lengths, confirm the variance characteristics, and sequentially confirm the variance characteristics associated with different verification processes. For example, the time point sequence is {X1, X2, X3, X4}, preferentially adopt three groups of interval time lengths for variance processing to confirm the first group of variance characteristics, then X2 and X3 are combined as combined time points, then X3 is converted into a verification time point, then the time length between X1 and X3 and X3 and X4 is again subjected to variance processing, and the corresponding variance characteristics are reconfirmed. For each implemented combined process, the corresponding variance characteristics need to be confirmed;

[0051] Confirm the variance characteristics associated in several groups of verification processes to meet: variance characteristics ≤ Y1, the verification process that meets the standard is designated as a standard process, Y1 is a preset value, and its specific value is determined by the operator according to experience. If there is no standard process, a feature recognition error signal is generated for display, indicating that the associated volume data is not strong enough in time characteristics and needs to be related to waiting;

[0052] If there is only one group of standard processes, the associated peak points corresponding to the verification time points and the first time point in the standard process are recorded as feature peak points.

[0053] If there are multiple groups of standard processes, select the group of standard processes with the smallest variance characteristics as the selected process, and record the associated peak points corresponding to the verification time points and the first time point in the selected process as the feature peak points.

[0054] The time period between adjacent feature peak points is recorded as the volume period. There may be some differences between adjacent volume periods, but the degree of difference is not large, representing the regular period of the corresponding volume appearing drastic changes.

[0055] Step 2, based on the confirmed volume period in the historical period, the server load rate associated with each volume period is analyzed, the load time in the volume period is confirmed, and based on the specific proportion of the load time, it is evaluated whether the volume load optimization is needed. When the load rate proportion in the corresponding volume period is not high, it means that the corresponding server can perform normal data processing, and there is no problem with the original information management method. It can continue and process normally. Otherwise, when the load is excessive, it means that there are such situations in each volume period, so load optimization is needed to change the reporting time of different reporting nodes to avoid data reporting in the same feature period.

[0056] In Step 2, the specific way to evaluate the load optimization is:

[0057] The server load rate associated with different time periods in different volume periods is numerically confirmed, and the server load rate associated with different time periods in different volume periods is denoted as Fz i-q , where i represents different volume periods, and q represents different time periods. Fz i-q ≥ Y2 is recorded as the load time, and Y2 is a preset value, generally 85%-95%.

[0058] Confirm the proportion of time when the load time is in the corresponding volume period, use ZB i = total number of load times ÷ total number of volume periods, and confirm the proportion of time ZB i associated with the corresponding volume period.

[0059] Then, the multiple time proportions ZB i associated with multiple volume periods are processed by the mean value to confirm the volume period characteristics (i.e. the specific mean value after processing). If the volume period characteristics ≥ 50%, it means that volume load optimization is needed. Otherwise, no processing is needed, which means that volume load optimization is not needed, and the server can normally process the load and digest the large volume of data.

[0060] Specifically, when the load characteristics of the corresponding server are excessive, the corresponding server cannot digest the data volume, which may result in data loss in subsequent data classification management and storage processes. Therefore, in order to avoid such situations, load optimization processing is needed.

[0061] Step 3, based on the different server load rates associated with different volume periods, the server load rate in the corresponding volume period is processed by the mean value, and the load optimization feasibility is confirmed. Based on the confirmation result, different load optimization methods are executed, and the specific method for feasibility confirmation is:

[0062] The mean value of several different server load rates associated with several different time points in several groups of volume periods is processed to confirm the feature mean value. If the feature mean value is greater than 40%, it means that the server computing power is insufficient, and a server optimization signal is generated and displayed. It means that the version of the module in the server is too low or the hardware performance is poor, which is not enough to support the management of large volume data, and the server needs to be optimized.

[0063] If the feature mean value is less than or equal to 40%, the time feature of different progress reporting nodes is adjusted, and the specific adjustment method is performed by Step4.

[0064] Specifically, the progress reporting nodes with large volume and relatively close time are adjusted in time, and are divided into other related time periods, so that load balancing can be effectively performed to achieve better load optimization processing effect.

[0065] Step4, the load period associated with the volume period is confirmed, and the feature node is selected from the progress reporting nodes associated with the load time. Based on the specific number of feature nodes, the feature time is selected in the volume period, and the feature node and the feature time are combined into a combination column and output for external operator confirmation.

[0066] In Step4, the specific way of selecting the feature time is:

[0067] A group of volume periods are randomly selected for processing (since the data features of each volume period are relatively the same, selecting a group of volume periods for processing can better confirm the features). Based on the several groups of load time confirmed in the volume period, the continuously appearing load time is confirmed as a load period (that is, the period associated with the continuously appearing load time is the corresponding load period).

[0068] The progress reporting nodes that exist in the volume data reporting in the load period are marked as pending nodes, and the reporting period T of the pending nodes is confirmed k (that is, the interval period of the reporting time, which is set in advance by the relevant operator), where k represents different pending nodes, the period length of the volume period is marked as ST, and the pending nodes that satisfy 0.7×ST≤T k ≤ST are marked as feature nodes.

[0069] Based on the number G of feature nodes, the period length ST is equally divided into G equal time periods, the time point with the lowest volume data reporting amount is confirmed in each equal time period, and is marked as a feature time. The feature node and the feature time are randomly combined one by one, and the combined column after one-to-one combination is displayed for external operator confirmation.

[0070] Specifically, by using this processing method, the over-clustering of large-volume data reporting nodes can be effectively avoided, different data reporting nodes are evenly divided in time periods, so as to effectively share the load in each evenly divided time period, improve the load utilization rate, further improve the overall management effect of the corresponding progress information, and achieve better optimization effect. Embodiments

[0071] In combination Figure 2 The building engineering construction progress information management system based on big data comprises:

[0072] The curve drawing end determines the reporting volume curve of the historical period from the historical reporting process based on the volume data associated with different engineering progress reporting nodes.

[0073] The volume period determination end locks the associated peak points existing in the inside based on the curve characteristics of the reporting volume curve, and determines the adjacent time associated between the associated peak points, locks the time period with rules and records it as the volume period.

[0074] The load optimization analysis end determines the load time in the volume period based on the volume period determined in the historical period, and determines the server load rate associated in each volume period, and determines whether the volume load optimization is needed based on the specific proportion of the load time.

[0075] The feasibility determination end determines the server load rate in the corresponding volume period based on the different server load rates associated in different volume periods, and performs mean processing on the server load rate in the corresponding volume period, and performs load optimization feasibility determination, and executes different load optimization modes based on the determination result.

[0076] The combination column output end determines the load period associated in the volume period, selects the feature node from the progress reporting nodes associated in the load time, and selects the feature time in the volume period based on the specific number of the feature node, and synchronously combines the feature node and the feature time into a combination column and outputs.

[0077] Some data in the above formula are dimensionless numerical calculations, and the contents not described in detail in the specification all belong to the prior art known to those skilled in the art.

[0078] The above embodiments are only used to illustrate the technical method of the present application and are not limited. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A method for managing construction progress information of a building project based on big data, characterized by, The method comprises the following steps: Step 1: Based on the volume data associated with different engineering progress reporting nodes, the historical period reporting volume curve is confirmed from the historical reporting process, and based on the curve characteristics of the reporting volume curve, the associated peak points are determined, and the adjacent time associated with the associated peak points is confirmed for regularity characteristics, and the volume period is locked; Step 2: Based on the volume period confirmed in the historical period, the characteristics of the server load rate associated with each volume period are analyzed, the load time in the volume period is confirmed, and based on the specific proportion of the load time, it is evaluated whether the volume load optimization is needed, and the specific method is: The server load rates associated with different time periods within different scale cycles are numerically confirmed, and the server load rates associated with different time periods within different scale cycles are denoted as Fz. i-q Where i represents different volume periods and q represents different moments, it will satisfy: Fz i-q The moment when Y2 is greater than or equal to Y2 is recorded as the load moment, where Y2 is a preset value; Confirm the proportion of time when the load is located in the corresponding volume cycle, adopt ZB i = total number of load time ÷ total number of volume cycle, confirm the proportion of time associated with the corresponding volume cycle ZB i ; The proportion of time points ZB associated with multiple volume periods i The mean value is processed to confirm the volume period characteristics. If the volume period characteristics are greater than or equal to 50%, it means that the volume load optimization needs to be performed. Otherwise, no processing is needed, which means that the volume load optimization is not needed, and the server can normally perform load processing and digest large volume data. Step 3: Based on the different server load rates associated with different volume periods, the server load rate in the corresponding volume period is processed by mean value, and the load optimization feasibility is confirmed, based on the confirmation result, different load optimization methods are executed; Step 4: The load period associated with the volume period is confirmed, and the feature nodes are selected from the progress reporting nodes associated with the load time, and the feature time is selected based on the specific number of feature nodes in the volume period, and the feature nodes and the feature time are combined into a combination column and output, and the specific method is: A group of volume periods are randomly selected for processing, based on the several groups of load time confirmed in the volume period, the continuously appearing load time is confirmed for the time period and is marked as the load period; The progress reporting node of the volume data reporting in the load period is calibrated as a pending node, and the reporting period T of the pending node is confirmed k Wherein k represents different pending nodes, the period length of the volume period is calibrated as ST, and the following is met: 0.7×ST≤T k The pending node that meets 0.7×ST≤T is calibrated as a feature node; Based on the number G of feature nodes, the period length ST is equally divided into G equal periods, the time with the lowest volume data reporting amount in each equal period is confirmed and recorded as the feature time, the feature nodes and the feature time are randomly combined one by one, and the combined column after one-to-one combination is displayed.

2. The big data-based construction work progress information management method according to claim 1, characterized by, In the Step 1, the detailed determination method of the historical period reporting volume curve is: Based on the current time, a group of historical periods is confirmed, the historical period is a preset period, the capacity of different volume data received by the corresponding server at different times in the historical period is confirmed, and the data capacity associated with different times is sequentially sorted according to the time sequence; The data capacity after sorting is confirmed in the two-dimensional coordinate system, and the points are connected to confirm the corresponding reporting volume curve, the horizontal coordinate axis of the coordinate system is the time line, the vertical coordinate axis is the data capacity, and the time is the preset time.

3. The big data-based construction work progress information management method according to claim 2, characterized by, In the Step 1, the detailed determination method of the volume period is: Based on the determined reporting volume curve, the mean value line associated with it is confirmed in real time, and the double mean value line is confirmed based on the mean value line, the mean value line and the double mean value line are perpendicular to the vertical coordinate axis, the mean value associated with the double mean value line is twice the mean value line, and the peak point above the double mean value line is marked as the associated peak point; The time associated with the several associated peak points is confirmed, and according to the time sequence, the several groups of time are sorted to confirm the time sequence; The time sequence is checked for regularity, and several groups of checking processes are executed: the specific time length between several adjacent times in the time sequence is processed by variance, and the variance characteristics are confirmed; Randomly combine the time points associated in the time sequence, denoted as combined time points, which can contain a single time point or multiple time points. Take the end time point of the combined time point as the check time point. Combine the confirmed several groups of check time points from the first time point of the time sequence. Confirm the specific time length between several adjacent time points. Process the variance of the confirmed several groups of specific time lengths. Confirm the variance characteristics. And confirm the variance characteristics associated with different check processes in turn. Confirm the variance characteristics associated with several groups of check processes. If the variance characteristics satisfy: variance characteristics ≤ Y1, the check process is marked as a qualified process, and Y1 is a preset value. If there is no qualified process, a feature recognition error signal is generated for display. If there is only one group of qualified processes, the associated peak points corresponding to the check time points and the first time point in the qualified process are recorded as feature peak points. If there are multiple groups of qualified processes, select the group of qualified processes with the smallest variance characteristics as the selected process, and record the associated peak points corresponding to the check time points and the first time point in the selected process as the feature peak points. The time period between adjacent feature peak points is recorded as the volume period.

4. The big data-based construction work progress information management method according to claim 1, characterized by, The specific way to perform the feasibility confirmation in Step 3 is: Process the mean value of several different server load rates associated with several different time points in several volume periods. Confirm the feature mean value. If the feature mean value > 40%, it means that the server computing power is insufficient, and a server optimization signal is generated for display. If the feature mean value ≤ 40%, adjust the time characteristics of different progress reporting nodes. The specific adjustment method is performed by Step 4.

5. A big data-based construction progress information management system for performing the big data-based construction progress information management method according to claims 1-4, characterized by, It includes: Curve drawing end, based on the volume data associated with different engineering progress reporting nodes, confirm the reporting volume curve of the historical period from the historical reporting process; Volume period confirmation end, based on the curve characteristics of the reporting volume curve, lock the associated peak points existing inside, and confirm the adjacent time points associated between the associated peak points, lock the time period with regularity and record it as the volume period; Load optimization analysis end, based on the volume period confirmed in the historical period, analyze the feature of the server load rate associated in each volume period, confirm the load time in the volume period, and based on the specific proportion of the load time, assess whether volume load optimization is needed; Feasibility confirmation end, based on different server load rates associated in different volume periods, process the mean value of the server load rate in the corresponding volume period, and perform load optimization feasibility confirmation. Based on the confirmation result, different load optimization methods are executed; Combined column output end, confirm the load period associated in the volume period, select the feature node from the progress reporting node associated in the load time, select the feature time in the volume period based on the specific number of feature nodes, and combine the feature node and the feature time into a combined column and output.

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