A project audit management system and method based on big data

Abstract

The present invention discloses a project audit management system and method based on big data. The present invention relates to the technical field of project data management, and solves the problem that when project audit data is managed, the storage characteristics of different storage nodes and different data are not analyzed, resulting in the easy chaos of project audit data. By accurately analyzing and determining the storage characteristics of three storage nodes, namely mechanical, solid state, and tape, the present invention can accurately allocate different types of data to the most suitable storage node according to the type and capacity of project data with a scientific and reasonable allocation logic; this allocation method not only gives full play to the advantages of each storage node, but also makes the storage time difference of each storage node when storing different types of data relatively small, ensuring the relative consistency of the storage process in terms of time characteristics, effectively avoiding the conflict of storing the previous and subsequent batches of project data in the same time period, and greatly improving the overall effect of data storage and processing.

Description

Technical Field

[0001] The present invention relates to the technical field of project data management, and specifically to a project audit management system and method based on big data. Background Art

[0002] Project audit data management refers to the management of a series of activities such as collecting, storing, processing, analyzing, and utilizing the data involved in the project audit process, aiming to ensure the accuracy, integrity, security, and availability of the data, and providing strong support for the project audit work. During a single project audit process, a large amount of project audit data belonging to different types will be generated.

[0003] The application with the publication number CN115759423B discloses a project audit management system and method based on big data. The system includes a change priority optimization module and an audit management module. The change priority optimization module analyzes the construction period change amount of each element corresponding to the engineering change area in the first array before and after the engineering change, optimizes the change priority of each element in the first array according to the analysis result, and obtains the optimized first array; the audit management module conducts audit management on the project according to the optimization result of the change priority of each element corresponding to the first array in the change priority optimization module. The present invention manages the change priority of the engineering change area, and at the same time gives an early warning for the unreasonable parts in the project change declaration materials, realizing the effective management of the project change declaration materials.

[0004] In the data management process associated with project audits, generally based on specific storage nodes, a large amount of project data generated in a specified batch is optimized for storage, and the storage process is optimized based on the storage rate generated during the storage process. However, the original storage processing method does not analyze the characteristics of different storage nodes and different data, resulting in a large deviation in the time difference for different storage nodes to complete the storage tasks when storing the corresponding batch of data. This will lead to different batches of data processed by different storage nodes, thus causing chaos in project audit data and the overall management quality of the data is not high. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a project audit management system and method based on big data, which solves the problem that project audit data is prone to chaos when being managed because the characteristics of different storage nodes and different data are not analyzed.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A project audit management method based on big data, including the following steps:

[0007] Step 1. Conduct initial management of the storage process for several groups of project data generated within a specified batch. Based on multiple groups of storage nodes preset in this audit system, initially determine the storage characteristics of different storage nodes. Based on the determined storage characteristics, determine the allocation and determination criteria process for different types of project data. According to the standard process, associate and allocate different types of project data to different storage nodes, and let different storage nodes perform storage processing on different types of project data;

[0008] The specific method for initially determining the storage characteristics of different storage nodes is as follows:

[0009] S11. Confirm multiple groups of storage nodes preset in this audit system, and determine the classification to which each group of storage nodes belongs. The classification includes mechanical storage nodes, solid-state storage nodes, and tape storage nodes. After completing the determination of the classification of the corresponding storage nodes, perform classification marking on the specified storage nodes. The mechanical storage nodes correspond to the classification mark A, the solid-state storage nodes correspond to the classification mark B, and the tape storage nodes correspond to the classification mark C;

[0010] S12. If the corresponding storage node belongs to the mechanical storage node with the classification mark A: Determine a group of traceability periods, and the traceability period is the preset period. Process several groups of storage rates generated within the traceability period. Perform mean processing on several groups of storage rates generated during the single-path data storage process to confirm the single-path storage rate characteristics. Then perform mean processing on several groups of storage rates generated during the multi-path data storage process to confirm the multi-path storage rate characteristics. Take the confirmed single-path storage rate characteristics and multi-path storage rate characteristics as the storage characteristics of this storage node;

[0011] If the corresponding storage node belongs to the solid-state storage node with the classification mark B: Determine a group of traceability periods, and the traceability period is the preset period. Perform mean processing on several groups of storage rates generated by this storage node within the traceability period to confirm the rate mean value. Take the confirmed rate mean value as the storage characteristics of this storage node;

[0012] If the corresponding storage node belongs to the tape storage node with the classification mark C: Similarly, determine a group of traceability periods, and the traceability period is the preset period. Confirm several groups of storage rates generated by this storage node for single-path data during the storage process within the traceability period, and perform mean processing on the confirmed several groups of storage rates to confirm the rate mean value. Take the confirmed rate mean value as the storage characteristics of this storage node;

[0013] The specific method for associating and allocating different types of project data to different storage nodes is as follows:

[0014] S13. Identify the number G of data formats within a single group of different types of project data. If G = 1, label the project data of this type as single-path data; if G ≥ 2, label the project data of this type as multi-path data;

[0015] S14. Based on the calibrated single-path data capacity, sort the calibrated single-path data in ascending order of the capacity value to confirm the single-path data sequence, and simultaneously confirm the capacity of the multi-path data. Then, sort the multi-path data in ascending order of the capacity value to confirm the multi-path data sequence;

[0016] S15. For the mechanical storage nodes with classification mark A, preferentially select data from the end to the front within the single-path data sequence. After the single-path data sequence is selected, then select data from the front to the back within the multi-path data sequence;

[0017] For the solid-state storage nodes with classification mark B, preferentially select data from the end to the front within the multi-path data sequence. After the multi-path data sequence is selected, then randomly select the remaining data from the single-path data sequence;

[0018] For the tape storage nodes with classification mark C, only select data from the front to the back within the single-path data sequence;

[0019] S16. Randomly execute a number of selection processes. Each selection process distributes all the data within the single-path data sequence and the multi-path data sequence to different storage nodes, and confirm the time characteristic values associated with each group of selection processes. The specific method is as follows:

[0020] Based on the storage characteristics associated with the corresponding storage node, confirm the time characteristic value associated with the corresponding node, and identify whether the corresponding storage node is a mechanical storage node with classification mark A:

[0021] If so, record the total capacity of the single-path data and the total capacity of the multi-path data allocated to this storage node, and label the total capacity of the single-path data recorded as R 单 , label the total capacity of the multi-path data recorded as R 多 , label the single-path storage rate characteristic confirmed for this storage node as V 单 , label the multi-path storage rate characteristic confirmed as V 多 , adopt: R 单 ÷V 单 =T 单 and R 多 ÷V 多 =T 多 Determine the time characteristics associated with different types of data, and then T 单 and T多 Perform a summation process to determine the time eigenvalue ST of this storage node;

[0022] If not, record the total capacity Rz of all data allocated to this storage node, then confirm the storage feature Tz associated with this storage node, and use Rz÷Tz = JT to confirm the time eigenvalue JT belonging to this storage node;

[0023] S17. Process the different time eigenvalues associated with different storage nodes in different selection processes: Perform a variance process on the multiple groups of time eigenvalues associated with different storage nodes in a single group of selection processes to confirm the characteristic variance belonging to this selection process, and then use the same processing method for other selection processes to confirm the characteristic variance of this selection process;

[0024] Select the minimum value from the different characteristic variances associated with multiple groups of different selection processes, record the selection process corresponding to the minimum value as the standard process, and based on this standard process, associate and allocate different types of project data to different storage nodes;

[0025] Step 2. After the specific allocation of different types of project data is completed, perform real-time monitoring on the storage processes associated with each storage node, and based on the real-time monitored storage rate, readjust the CPU occupancy utilization rate associated with different storage nodes. The specific sub-steps are as follows:

[0026] S21. Based on the storage feature confirmed for the corresponding storage node, lock the monitoring interval for this storage node, and designate the storage feature as V i , where i represents different storage nodes, and the locked monitoring interval is [V i -Y1, V i +Y1], where Y1 is a preset value. If the corresponding storage node is a mechanical storage node with a classification mark A, then there are two groups of monitoring intervals for this type of storage node;

[0027] S22. Based on the monitoring interval confirmed for the corresponding storage node, perform real-time monitoring on the storage rate associated with different storage nodes during the storage process, and based on the specific results of the real-time monitoring, readjust the CPU occupancy utilization rate of this storage node:

[0028] If the monitored real-time storage rate Vs ∉ [V i -Y1, V i +Y1], then perform high and low adjustments. When Vs < V i -Y1, increase the CPU occupancy utilization rate of the corresponding storage node until the real-time monitored real-time storage rate Vs ∈ [V i -Y1, V i +Y1] stops. If Vs > V iWhen it is +Y1, the CPU occupancy ratio utilization of the corresponding storage node is reduced until the real-time storage rate Vs ∈ [V i -Y1, V i +Y1] is reached;

[0029] Step 3: After all the associated groups of project data in this batch are stored, an index sequence table for this batch is established based on the storage duration associated with the corresponding project data. The specific sub-steps are as follows:

[0030] S31. Confirm the storage duration of several groups of project data in this batch, and label the storage duration associated with different project data as SJ k , where k represents different project data;

[0031] S32. Based on the different specific values of different storage durations SJ k , in the order of sorting from small to large, sort the different project data associated with the corresponding storage duration SJ k to establish an index sequence table for this batch;

[0032] S33. When indexing the project data in this batch, index step by step from front to back according to this index sequence table.

[0033] Preferably, a project audit management system based on big data includes:

[0034] Node feature determination end, which first determines the classification of the corresponding storage node according to multiple groups of storage nodes preset in this audit system, and then processes the historical completed data of storage nodes of different classifications in different ways to initially determine the storage characteristics of different storage nodes;

[0035] Project data distribution end, based on the different storage characteristics determined by different storage nodes, determines the standard process for distributing different types of project data, and according to the standard process, associates and distributes different types of project data to different storage nodes, and different storage nodes perform storage processing on different types of project data;

[0036] CPU occupancy ratio utilization adjustment end, after the specific distribution of different types of project data is completed, monitors the storage process associated with each storage node in real time, and based on the real-time monitored storage rate, readjusts the CPU occupancy ratio utilization associated with different storage nodes;

[0037] Index sequence table construction end, after all the associated groups of project data in this batch are stored, based on the storage duration associated with the corresponding project data, establishes an index sequence table for this batch in the order of storage duration from small to large.

[0038] The present invention provides a project audit management system and method based on big data. Compared with the prior art, it has the following beneficial effects:

[0039] By accurately analyzing and determining the storage characteristics of three types of storage nodes, namely mechanical, solid-state, and magnetic tape, the present invention can accurately allocate different types of data to the most suitable storage nodes according to the type (single-path or multi-path data) and capacity of the project data with a scientific and reasonable allocation logic. This allocation method not only gives full play to the advantages of each storage node but also makes the storage time difference of each storage node when storing different types of data relatively small, ensuring the relative consistency of the storage process in terms of time characteristics, effectively avoiding the conflict of storing the project data of the previous and subsequent batches in the same time period, and greatly improving the overall effect and efficiency of data storage and processing.

[0040] During the data storage process, the storage process of each storage node is monitored in real time, and the CPU occupancy rate of the storage node is flexibly adjusted according to the preset monitoring interval and storage characteristics. When the monitored real-time storage rate deviates from the normal interval, it can be adjusted up and down in a timely manner to ensure that the storage rate is always within a reasonable range, thereby realizing the effective control of storage balance, greatly reducing the data storage time, and further improving the quality and efficiency of storage processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0042] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Please refer to Figure 1 , the present application provides a project audit management method based on big data, including the following steps:

[0045] Step 1: Conduct initial management of the storage process for several groups of project data generated within a specified batch. Based on multiple groups of storage nodes preset in this audit system, initially determine the storage characteristics of different storage nodes. Based on the determined storage characteristics, determine the allocation criteria process for different types of project data. According to the criteria process, associate and allocate different types of project data to different storage nodes, and let different storage nodes perform storage processing on different types of project data. Specifically, within this audit system, there are three different types of corresponding storage nodes, namely mechanical storage nodes, solid-state storage nodes, and tape storage nodes, and each different storage node has different storage characteristics:

[0046] For the mechanical storage node, it has strong single-path data read and write performance and fast read and write speed, but for multi-path data, its read and write speed is slower;

[0047] For the solid-state storage node, whether for single-path data or multi-path data, its read and write speed is fast, and its overall comprehensive performance is strong;

[0048] For the tape storage node, it can only process the storage process of single-path data. For multi-path data, it cannot execute the associated storage process, and its read and write speed for single-path data is low;

[0049] For its single-path data, this type of data is simply single-group formatted data, and only a single-group path is required to implement the read and write process of this type of data, that is: pure text data, pure code data, or pure video data, pure audio data, etc.;

[0050] For multi-path data, this type of data is combined data generated by multiple groups of different formatted data. When reading, writing, and storing this type of multi-path data, the corresponding storage node needs to execute multiple different storage paths to complete the complete storage process of this type of multi-path data;

[0051] Among them, the specific method for initially determining the storage characteristics of different storage nodes is:

[0052] S11. Confirm multiple groups of storage nodes preset in this audit system, and determine the classification to which each group of storage nodes belongs. The classifications include mechanical storage nodes, solid-state storage nodes, and tape storage nodes. After determining the classification of the corresponding storage node, perform a classification mark on the specified storage node. The mechanical storage node corresponds to the classification mark A, the solid-state storage node corresponds to the classification mark B, and the tape storage node corresponds to the classification mark C;

[0053] S12. If the corresponding storage node belongs to a mechanical storage node with classification label A (i.e., this storage node belongs to a mechanical storage node): Determine a set of traceability periods. The traceability period is a preset period, which is formulated in advance by relevant operators according to experience. Process several groups of storage rates generated within the traceability period. Perform mean processing on several groups of storage rates generated during the single-path data storage process to confirm the single-path storage rate characteristics. Then perform mean processing on several groups of storage rates generated during the multi-path data storage process to confirm the multi-path storage rate characteristics. Take the confirmed single-path storage rate characteristics and multi-path storage rate characteristics as the storage characteristics of this storage node. Its traceability period is a historical period. For the mechanical storage node, the associated storage rates for single-path data or multi-path data are all different. Therefore, for such storage nodes, it is necessary to determine the storage characteristics related to the corresponding storage node based on the storage conditions of the corresponding different data;

[0054] If the corresponding storage node belongs to a solid-state storage node with classification label B (i.e., this storage node belongs to a solid-state storage node): Determine a set of traceability periods (this traceability period is the same as the traceability period mentioned in the previous paragraph). The traceability period is a preset period. Perform mean processing on several groups of storage rates generated by this storage node within the traceability period to confirm the rate mean. Take the confirmed rate mean as the storage characteristic of this storage node (for the solid-state storage node, the average speeds generated for single-path data or multi-path data are relatively consistent. Therefore, a unified confirmation method can be used to determine the storage rate associated with the corresponding storage node, so as to determine the storage characteristic associated with the corresponding node);

[0055] If the corresponding storage node belongs to a tape storage node with classification label C: Similarly, determine a set of traceability periods. The traceability period is a preset period. Confirm several groups of storage rates generated by this storage node for single-path data during the storage process within the traceability period, and perform mean processing on the confirmed several groups of storage rates to confirm the rate mean. Take the confirmed rate mean as the storage characteristic of this storage node;

[0056] The specific method of associating and allocating different types of project data to different storage nodes is as follows:

[0057] S13. Identify the number G of data formats within a single group of different types of project data. If G = 1, label this type of project data as single-path data. If G ≥ 2, label this type of project data as multi-path data;

[0058] S14. Based on the calibrated single-path data capacity, sort the calibrated single-path data in ascending order of the capacity value to confirm the single-path data sequence, simultaneously confirm the capacity of the multi-path data, and sort the multi-path data in ascending order of the capacity value to confirm the multi-path data sequence;

[0059] S15. For the mechanical storage nodes with classification label A, preferentially select data from the end to the front within the single-path data sequence. After the single-path data sequence is selected, then select data from the front to the back within the multi-path data sequence;

[0060] For the solid-state storage nodes with classification label B, preferentially select data from the end to the front within the multi-path data sequence. After the multi-path data sequence is selected, then randomly select the remaining data from the single-path data sequence;

[0061] For the tape storage nodes with classification label C, only select data from the front to the back within the single-path data sequence;

[0062] S16. Randomly execute several selection processes. Each selection process distributes all the data within the single-path data sequence and the multi-path data sequence to different storage nodes, and confirm the time characteristics associated with each group of selection processes:

[0063] S161. Based on the storage characteristics associated with the corresponding storage node, confirm the time characteristic value associated with the corresponding node, and identify whether the corresponding storage node is a mechanical storage node with classification label A:

[0064] If so, record the total capacity of the single-path data and the total capacity of the multi-path data allocated to this storage node, and calibrate the total capacity of the single-path data recorded as R 单 , calibrate the total capacity of the multi-path data recorded as R 多 , calibrate the single-path storage rate characteristic confirmed for this storage node as V 单 , calibrate the multi-path storage rate characteristic confirmed as V 多 , adopt: R 单 ÷V 单 =T 单 and R 多 ÷V 多 =T 多 to determine the time characteristics associated with different types of data, and then sum up T 单 and T 多 to determine the time characteristic value ST of this storage node;

[0065] If not, record the total capacity Rz of all data allocated to this storage node, then confirm the storage feature Tz associated with this storage node, and use Rz÷Tz = JT to confirm the time feature value JT belonging to this storage node;

[0066] S17. Process the different time feature values associated with different storage nodes in different selection processes: perform variance processing on multiple groups of time feature values associated with different storage nodes in a single group of selection processes to confirm the feature variance belonging to this selection process, and then use the same processing method for other selection processes to confirm the feature variance of this selection process;

[0067] Select the minimum value from the different feature variances associated with multiple groups of different selection processes, record the selection process corresponding to the minimum value as the standard process, and based on this standard process, associate and allocate different types of project data to different storage nodes;

[0068] Specifically, using this processing method, first, according to the storage features of different storage nodes and the specific types of different data, allocate different types of data into storage nodes with different storage features. According to the determined specific data features and specific allocation logic, the corresponding storage nodes can effectively store different types of data, and when each different storage node stores different types of data, the associated storage times are relatively consistent in terms of differences, which can achieve a better data allocation effect. Before the arrival of the next batch of data, there will be no situation where the project data of the previous and next batches are stored in the same time period, which can effectively improve the data storage and processing effect and ensure that the time features of different storage processes are relatively consistent;

[0069] Step 2. After the specific allocation of different types of project data is completed, monitor the storage processes associated with each storage node in real time, and based on the real-time monitored storage rate, readjust the CPU occupancy utilization rate associated with different storage nodes. Among them, the specific sub-steps for readjustment are as follows:

[0070] S21. Based on the storage feature confirmed for the corresponding storage node, lock the monitoring interval for this storage node, and designate the storage feature as V i , where i represents different storage nodes, and the locked monitoring interval is [V i -Y1, V i +Y1], where Y1 is a preset value, and its specific value is determined by the operator according to experience. If the corresponding storage node is a mechanical storage node with a classification mark A, then there are two groups of monitoring intervals for such storage nodes;

[0071] S22. Based on the monitored interval confirmed by the corresponding storage node, the storage rates associated with different storage nodes during the storage process are monitored in real time, and based on the specific results of the real-time monitoring, the CPU occupancy rate of this storage node is readjusted:

[0072] If the real-time storage rate Vs monitored ∉ [V i -Y1, V i +Y1], then high-low adjustment is performed. When Vs < V i -Y1, the CPU occupancy rate of the corresponding storage node is increased until the real-time storage rate Vs monitored ∈ [V i -Y1, V i +Y1]. If Vs > V i +Y1, the CPU occupancy rate of the corresponding storage node is decreased until the real-time storage rate Vs monitored ∈ [V i -Y1, V i +Y1];

[0073] Specifically, by adopting this processing method, the storage processes of each storage node can be effectively monitored in real time. Based on the specific results of the real-time monitoring, the associated storage rates are controlled to achieve an effective control effect of storage balance, so as to fully reduce the corresponding storage time and achieve a better storage processing effect.

[0074] Step 3. After all the associated groups of project data in this batch are stored, an index sequence table for this batch is established based on the storage duration associated with the corresponding project data. When indexing the data in this batch subsequently, step-by-step indexing is performed according to this sequence table, and a better indexing effect can be achieved;

[0075] Among them, the specific sub-steps for establishing the index sequence table are:

[0076] S31. Confirm the storage durations of several groups of project data in this batch, and label the storage durations associated with different project data as SJ k , where k represents different project data;

[0077] S32. Based on the different specific values of different storage durations SJ k , in the order of sorting from small to large, the different project data associated with the corresponding storage duration SJ k are sorted to establish the index sequence table for this batch;

[0078] S33. When indexing the project data of this batch, index step by step from front to back according to this index sequence list. This indexing method can ensure the gradual output of the corresponding project data, without causing long waiting for the corresponding personnel, facilitating the gradual output of the data of the corresponding batch. This indexing effect is better and can fully reduce the waiting time for indexing.

[0079] Combined with Figure 2 , a project audit management system based on big data, comprising:

[0080] A node feature determination end, according to multiple groups of storage nodes preset in this audit system, first determine the classification to which the corresponding storage node belongs, and then process the historical completed data of storage nodes of different classifications in different ways to initially determine the storage characteristics of different storage nodes;

[0081] A project data allocation end, based on the different storage characteristics determined by different storage nodes, determines the standard process for allocating different types of project data, and associates and allocates different types of project data to different storage nodes according to the standard process, and different storage nodes perform storage processing on different types of project data;

[0082] A CPU occupancy ratio utilization adjustment end, after the specific allocation of different types of project data is completed, monitors the storage processes associated with each storage node in real time, and based on the real-time monitored storage rate, readjusts the CPU occupancy ratio utilization rate associated with different storage nodes;

[0083] An index sequence list construction end, after all groups of project data associated within this batch are stored, based on the storage duration associated with the corresponding project data, establish the index sequence list of this batch in the order from small to large according to the storage duration.

[0084] Some data in the above formula are all numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well known to those skilled in the art.

[0085] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A project audit management method based on big data, characterized in that It includes the following steps: Step 1: Conduct initial management of the storage process for several groups of project data generated within a specified batch. Based on multiple groups of storage nodes preset in this audit system, initially determine the storage characteristics of different storage nodes. Based on the determined storage characteristics, determine the allocation and determination criteria process for different types of project data. According to the criteria process, associate and allocate different types of project data to different storage nodes, and let different storage nodes perform storage processing on different types of project data; Among them, the specific method of associating and allocating different types of project data to different storage nodes is: Identify the number G of data formats within a single group of different types of project data. If G = 1, label this type of project data as single-path data. If G ≥ 2, label this type of project data as multi-path data; Based on the capacity of the labeled single-path data, sort the labeled single-path data in ascending order of capacity value to confirm the single-path data sequence. Simultaneously confirm the capacity of the multi-path data, and also sort the multi-path data in ascending order of capacity value to confirm the multi-path data sequence; For the mechanical storage node with classification mark A, preferentially select data from the end to the front within the single-path data sequence. After the single-path data sequence is selected, then select data from the front to the back within the multi-path data sequence; For the solid-state storage node with classification mark B, preferentially select data from the end to the front within the multi-path data sequence. After the multi-path data sequence is selected, then randomly select the remaining data from the single-path data sequence; For the tape storage node with classification mark C, only select data from the front to the back within the single-path data sequence; Randomly execute several selection processes. Each selection process allocates all the data within the single-path data sequence and the multi-path data sequence to different storage nodes, and confirm the time characteristic value associated with each selection process. The specific method is: Based on the storage characteristics associated with the corresponding storage node, confirm the time characteristic value associated with the corresponding node, and identify whether the corresponding storage node is a mechanical storage node with classification mark A: If so, record the total single-path data capacity and the total multi-path data capacity allocated to this storage node, and label the recorded total single-path data capacity as R 单 , label the recorded total multi-path data capacity as R 多 , label the single-path storage rate characteristic confirmed for this storage node as V 单 , label the confirmed multi-path storage rate characteristic as V 多 , adopt: R 单 ÷V 单 =T 单 and R 多 ÷V 多 =T 多 Determine the time characteristics associated with different types of data, and then sum up T 单 and T 多 to determine the time characteristic value ST of this storage node; If not, record the total capacity Rz of all the data allocated to this storage node, then confirm the storage characteristic Tz associated with this storage node, and use Rz ÷ Tz = JT to confirm the time characteristic value JT belonging to this storage node; Process the different time characteristic values associated with different storage nodes in different selection processes: Perform variance processing on the multiple groups of time characteristic values associated with different storage nodes in a single selection process to confirm the characteristic variance belonging to this selection process, and then use the same processing method for other selection processes to confirm the characteristic variance of this selection process; Select the minimum value from the different characteristic variances associated with multiple different selection processes, mark the selection process corresponding to the minimum value as the standard process, and based on this standard process, associate and allocate different types of project data to different storage nodes; Step 2: After the specific allocation of project data of different types is completed, the storage processes associated with each storage node are monitored in real time, and based on the storage rate monitored in real time, the CPU occupancy utilization rate associated with different storage nodes is readjusted; Step 3: After all the groups of project data associated within this batch are stored, an index sequence list for this batch is established based on the storage duration associated with the corresponding project data.

2. The method for project audit management based on big data according to claim 1, wherein, In the said Step 1, the specific method for initially determining the storage characteristics of different storage nodes is as follows: S11: Confirm multiple groups of storage nodes preset in this audit system, and determine the classification to which each group of storage nodes belongs. The classifications include mechanical storage nodes, solid-state storage nodes, and tape storage nodes. After the classification of the corresponding storage nodes is determined, classification marks are made for the specified storage nodes. The mechanical storage nodes correspond to the classification mark A, the solid-state storage nodes correspond to the classification mark B, and the tape storage nodes correspond to the classification mark C; S12: If the corresponding storage node is a mechanical storage node with the classification mark A: Determine a group of traceability periods, where the traceability period is a preset period. Process several groups of storage rates generated within the traceability period. Take the average of several groups of storage rates generated during the single-path data storage process to confirm the single-path storage rate characteristics. Then take the average of several groups of storage rates generated during the multi-path data storage process to confirm the multi-path storage rate characteristics. Take the confirmed single-path storage rate characteristics and multi-path storage rate characteristics as the storage characteristics of this storage node; If the corresponding storage node is a solid-state storage node with the classification mark B: Determine a group of traceability periods, where the traceability period is a preset period. Take the average of several groups of storage rates generated by this storage node within the traceability period to confirm the rate average. Take the confirmed rate average as the storage characteristics of this storage node; If the corresponding storage node is a tape storage node with the classification mark C: Similarly determine a group of traceability periods, where the traceability period is a preset period. Confirm several groups of storage rates generated by this storage node for single-path data during the storage process within the traceability period, and take the average of the confirmed several groups of storage rates to confirm the rate average. Take the confirmed rate average as the storage characteristics of this storage node.

3. The method for project audit management based on big data according to claim 1, wherein In the said Step 2, the specific sub-steps for readjusting the CPU occupancy utilization rate associated with different storage nodes are as follows: S21. Based on the storage features confirmed by the corresponding storage node, lock the monitoring interval for this storage node, and formulate the storage feature as V i , where i represents different storage nodes, and the locked monitoring interval is [V i -Y1, V i +Y1], where Y1 is a preset value. If the corresponding storage node is a mechanical storage node with a classification mark A, there are two sets of monitoring intervals for such storage nodes; S22: Based on the monitored intervals confirmed for the corresponding storage nodes, monitor the storage rates associated with different storage nodes during the storage process in real time, and based on the specific results of the real-time monitoring, readjust the CPU occupancy utilization rate of this storage node: If the monitored real-time storage rate Vs ∉ [V i - Y1, V i + Y1], then height adjustment is performed. When Vs < V i - Y1, the CPU occupancy rate of the corresponding storage node is increased until the real-time storage rate Vs of the real-time monitoring ∈ [V i - Y1, V i + Y1].

4. The method for project audit management based on big data according to claim 3, wherein In the step S22, if Vs > V i + Y1, the CPU occupancy rate of the corresponding storage node is reduced until the real-time storage rate Vs ∈ [V i - Y1, V i + Y1] during real-time monitoring.

5. A project audit management method based on big data according to claim 1, characterized in that In the said Step 3, the specific sub-steps for establishing the index sequence list are as follows: S31. Confirm the storage duration of several groups of project data in this batch, and label the storage duration associated with different project data as SJ k , where k represents different project data; S32. Based on different specific values of the storage duration SJ k and in the order of sorting from small to large according to the values, sort the different project data associated with the corresponding storage duration SJ k to establish an index sequence list for this batch; S33: When indexing the project data of this batch, index step by step from front to back according to this index sequence list.

6. A project audit management system based on big data, which operates according to the method for project audit management based on big data described in any one of claims 1-5, characterized in that, Including: Node feature determination end, according to multiple groups of storage nodes preset in this audit system, first determine the category to which the corresponding storage node belongs, and then process the historical completed data of storage nodes in different categories in different ways to initially determine the storage characteristics of different storage nodes; Project data allocation end, based on the different storage characteristics determined by different storage nodes, determine the standard process for allocating different types of project data, and according to the standard process, associate and allocate different types of project data to different storage nodes, and different storage nodes perform storage processing on different types of project data; CPU occupancy utilization adjustment end, after the specific allocation of different types of project data is completed, monitor the storage processes associated with each storage node in real time, and based on the real-time monitored storage rate, readjust the CPU occupancy utilization rate associated with different storage nodes; Index sequence table construction end, after all the groups of project data associated in this batch are stored, based on the storage duration associated with the corresponding project data, establish the index sequence table of this batch in the order of storage duration from small to large.

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