Data processing system and method based on SaaS service cloud platform

Abstract

The invention discloses a data processing system and method based on an SaaS service cloud platform, relates to the technical field of SaaS service cloud platforms, and solves the problem of insufficient optimization of interaction processes in the later period due to the fact that feature confirmation is not carried out according to specific data interaction processes of different data interaction parties. According to the method, the interaction rates, the proportion utilization rates and the data cache characteristics of different data interaction parties are comprehensively analyzed, and the comprehensive proportion is accurately calculated; the restriction feature determining end reasonably allocates and adjusts the computing power resources of the cloud service platform on the basis of the restriction feature determining end; allocating resources according to the comprehensive proportion; when the total evaluation value is greater than the computing power resource, carrying out equalization through ratio processing to ensure the scientificity and rationality of resource allocation; and the execution center accurately limits the data interaction rate according to the information provided by the limiting feature determination end, so that the interaction efficiency of each data interaction process is guaranteed, and a stable interaction effect is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of SaaS service cloud platforms, and specifically to a data processing system and method based on a SaaS service cloud platform. Background Art

[0002] In the current rapid development of information technology, the wave of enterprise digital transformation is sweeping across all industries. The traditional software deployment model faces many dilemmas and has become a bottleneck restricting the efficient development of enterprises.

[0003] On the one hand, for enterprises to build and maintain software systems by themselves, they need to invest huge amounts of money in purchasing hardware equipment, obtaining software licenses, and forming a professional technical operation and maintenance team. The hardware equipment not only has a high procurement cost, but also the hidden costs brought by its rapid depreciation cannot be underestimated with the technological iteration and update. The software license fees often increase significantly with the increase in enterprise scale and functional requirements, making many enterprises overwhelmed.

[0004] On the other hand, the traditional software deployment takes a long time. From the early planning, installation and debugging to the final launch, it often takes several months or even years, which makes it difficult for enterprises to quickly respond to market changes and miss development opportunities. Moreover, the system maintenance work is extremely complex, and issues such as software vulnerability repair, version upgrade, and hardware fault troubleshooting need to be constantly monitored. A slight oversight may lead to system crashes, seriously affecting the enterprise's business.

[0005] The SaaS service cloud platform emerged as the times require. Relying on the powerful computing, storage, and network resources of cloud computing and using the Internet as a medium, it provides software as a service to enterprise users. Enterprises no longer need to worry about the procurement of hardware equipment, software installation and maintenance. They can easily access and use various software applications with rich functions through a browser or a lightweight client. This innovative service model enables enterprises to achieve digital transformation at a low cost and in a very short time, quickly deploy the required business systems, flexibly adjust the scale of software use, and fully focus on the development of core businesses, gaining the upper hand in the fierce market competition.

[0006] The SaaS service cloud platform does not perform feature confirmation according to the specific data interaction processes of different data interaction parties, resulting in insufficient optimization of the interaction processes in the later stage and overloading of some interaction processes. Summary of the Invention

[0007] Aiming at the deficiencies of the prior art, the present invention provides a data processing system and method based on a SaaS service cloud platform, which solves the problem of insufficient optimization of the interaction processes in the later stage due to the lack of feature confirmation according to the specific data interaction processes of different data interaction parties.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: A data processing system based on a SaaS service cloud platform, including:

[0009] A platform monitoring center that confirms different data interaction parties associated with the data interaction process of the service cloud platform and simultaneously monitors the interaction rate associated with the corresponding data interaction processes of different data interaction parties in real time;

[0010] An interaction feature analysis terminal that, according to the data transmission protocols associated with different data interaction parties and the service cloud platform, confirms the data formats associated with different data interaction parties, and from historical cloud data, confirms the conversion rate associated with the service cloud platform in different data format conversion processes. The specific method is as follows:

[0011] S11. Mark the data formats associated with different data interaction parties as G i , where i represents different data interaction parties, randomly combine two groups of data formats, confirm several sets of combined data formats, and from historical cloud data, confirm the logical swapping time of different sets of combined data formats. Process the confirmed several logical swapping times by taking the mean value, lock the combined set features, and sequentially confirm the combined set features associated with different sets of combined data formats;

[0012] S12. Randomly select a set of formats as the initial format from the confirmed several sets of data formats G i , execute the first selected process, record the initial format as the format to be determined, and determine the different sets of combined data formats associated with this format to be determined. Then, select the minimum value from the combined set features of different sets of combined data formats, and record the set of combined data formats associated with the minimum value as the subsequent format of this format to be determined;

[0013] S13. Then, process the subsequent format in the same way as the format to be determined, sequentially confirm the subsequent format. The already selected G i does not participate in the subsequent format confirmation process. Based on the specific process of sequential confirmation, sort several sets of data formats into a format data column, sum the combined set features associated with adjacent formats of this format data column, and confirm the comprehensive feature of this format data column;

[0014] S14. Then execute other selected processes: randomly select another set of formats as the initial format. The data format already selected as the initial format cannot be selected again. Use the same method as in steps S12 - S13 to confirm the comprehensive feature of the format data column corresponding to the second selected process;

[0015] S15. Sequentially confirm the comprehensive features of data columns in different formats, select the minimum value from the confirmed several groups of comprehensive features, use the data column in the format associated with the minimum value as the best data interaction logic, and transmit this best data interaction logic to the execution center;

[0016] The interaction process analysis terminal, according to the interaction rates associated with different data interaction processes of different data interaction parties, and confirm the occupancy utilization rate associated with the corresponding interaction nodes, confirm the process characteristics of the corresponding data interaction process, and then based on the data cache characteristics associated with the corresponding data interaction process, reconfirm the comprehensive occupancy ratio. The specific method is as follows:

[0017] Define a set of monitoring periods, and the monitoring period is a preset period. Confirm the interaction rates associated with different data interaction parties at different times within this monitoring period, and generate an interaction rate change line belonging to different data interaction parties within this monitoring period. The horizontal axis of this interaction rate change line is the time line, and its vertical axis is the interaction rate. And there are multiple line segments within the interaction rate change line, and different line segments correspond to different interaction processes;

[0018] Confirm the characteristics of a single group of interaction rate change lines: identify the difference in interaction rates associated with adjacent times, and the interaction rate difference = |interaction rate at the previous time - interaction rate at the next time|, where the interaction rate at the previous time and the interaction rate at the next time are the interaction rates associated with adjacent times. Starting from the starting point of a single group of interaction rate change lines, sequentially label the confirmed interaction rate differences as V k , where k = 1, 2,..., n, where V1 is the first group of interaction rate differences confirmed from front to back, and V n is the last group of interaction rate differences confirmed. Sequentially confirm the variance of the confirmed interaction rate differences V k from front to back. Sequentially select columns of the same characteristic parameters from front to back. Starting from the first group of interaction rate differences, sequentially select other interaction rate differences for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate differences until the variance ≥ Y1 and stop selecting. And label the selected interaction rate differences this time as the first group of values in the next-stage variance processing process, where Y1 is a preset value. Denote the selected several groups of interaction rate differences this time as the same-characteristic differences, and denote the partial curve associated with the same-characteristic differences as the same-characteristic curve segment;

[0019] Sequentially select and confirm the variance of the interaction rate differences from front to back, and lock the same-characteristic curve segment based on the specific process of time confirmation. The same-characteristic curve segment includes at least three groups of interaction rate differences;

[0020] Perform mean processing on several groups of interaction rates associated with each same-feature curve segment, confirm the mean interaction rate of the corresponding same-feature curve segment, and select the maximum value from the confirmed several groups of mean interaction rates as the process feature of this data interaction party in the data interaction process;

[0021] The restriction feature determination end evenly distributes the computing power resources of the cloud service platform according to the different comprehensive occupancy ratios associated with different data interaction processes, and based on the evenly distributed computing power resources, restricts the interaction rate of each data interaction process and executes it through the execution center.

[0022] Preferably, the execution center directly executes this optimal data interaction logic when multiple data interaction parties perform data interaction at the same time according to the confirmed optimal data interaction logic.

[0023] Preferably, the specific way for the interaction process analysis end to confirm the comprehensive occupancy ratio is:

[0024] Confirm the occupancy ratio utilization rate associated with this data interaction process, using: process feature ÷ occupancy ratio utilization rate = single-frequency feature;

[0025] Then, from the data cache rate associated with the data interaction process within a unit time, perform mean processing on several groups of data cache rates to confirm the mean cache rate, using: (single-frequency feature × mean cache rate) + occupancy ratio utilization rate = comprehensive occupancy ratio, confirm the comprehensive occupancy ratio associated with this data interaction process, and transmit it to the restriction feature determination end.

[0026] Preferably, the specific way for the restriction feature determination end to evenly distribute the computing power resources is:

[0027] Calibrate the different comprehensive occupancy ratios associated with different data interaction processes as Z q , where q represents different data interaction processes, sum several groups of comprehensive occupancy ratios Z q to confirm the total evaluation value ZP;

[0028] Calibrate the computing power resources of the cloud service platform as ZL. If ZP ≤ ZL, then allocate resources from the computing power resources according to the calibrated Z q If ZP > ZL, then perform ratio processing on several groups of Z q to confirm the ratio sequence regarding several groups of Z q , and then evenly distribute the computing power resources ZL according to this ratio sequence to confirm the evenly distributed resources;

[0029] The specific way to restrict the interaction rate is:

[0030] When the computing power resources associated with the corresponding data interaction process are ZL, use: ZL × single-frequency feature = restricted rate to confirm the restricted rate of the corresponding data interaction process;

[0031] When the computing power resources associated with the corresponding data interaction process are evenly divided resources, use: evenly divided resources × single-frequency feature = restricted rate to confirm the restricted rate of the corresponding data interaction process;

[0032] Transmit the confirmed restricted rate and computing power resources of the corresponding data interaction process to the execution center.

[0033] Preferably, the execution center adjusts the occupancy utilization rate of the corresponding data interaction process to ZL or evenly divided resources, and restricts the data interaction rate, and the restricted rate value is the restricted rate.

[0034] Preferably, a data processing method based on the SaaS service cloud platform includes the following steps:

[0035] Step 1: Confirm different data interaction parties associated with the data interaction process of the service cloud platform, and simultaneously monitor the interaction rates associated with the corresponding data interaction processes of different data interaction parties in real time;

[0036] Step 2: According to the data transmission protocols associated with different data interaction parties and the service cloud platform, confirm the data formats associated with different data interaction parties, and from the historical cloud data, confirm the conversion rates associated with the service cloud platform in different data format conversion processes, select the best data interaction logic, and directly execute this best data interaction logic when multiple data interaction parties perform data interaction at the same time;

[0037] Step 3: According to the interaction rates associated with different data interaction processes of different data interaction parties, and confirm the occupancy utilization rate associated with the corresponding interaction nodes, confirm the process characteristics of the corresponding data interaction process, and then reconfirm the comprehensive occupancy ratio based on the data cache characteristics associated with the corresponding data interaction process;

[0038] Step 4: Divide the computing power resources of the cloud service platform evenly according to the different comprehensive occupancy ratios associated with the corresponding different data interaction processes, and based on the evenly divided computing power resources, restrict the interaction rate of each data interaction process and execute it through the execution center.

[0039] The present invention provides a data processing system and method based on the SaaS service cloud platform. Compared with the prior art, it has the following beneficial effects:

[0040] The present invention effectively avoids data congestion by confirming the interaction logic according to different data transmission protocols. The interaction feature analysis terminal selects the optimal data interaction logic from historical cloud data through a unique algorithm, greatly improving the data interaction efficiency. For example, in the face of multiple data formats, it can accurately find the optimal processing sequence, reduce the burden of multiple interaction processes, ensure rapid response when different data interaction parties interact at the same time, significantly improve the overall interaction effect, and ensure the efficiency and smoothness of data interaction;

[0041] By comprehensively analyzing the interaction rate, occupancy utilization rate, and data cache characteristics of different data interaction parties, the comprehensive occupancy ratio is accurately calculated. Based on this, the limit feature determination terminal reasonably allocates and adjusts the computing power resources of the cloud service platform. When the total evaluation value is less than or equal to the computing power resources, the resources are directly allocated according to the comprehensive occupancy ratio; when the total evaluation value is greater than the computing power resources, equal distribution is performed through ratio processing to ensure the scientificity and rationality of resource allocation. At the same time, the execution center accurately limits the data interaction rate according to the information provided by the limit feature determination terminal, which not only ensures the interaction efficiency of each data interaction process and maintains a stable interaction effect, but also effectively prevents process overload, realizes the refined management of data interaction processes, greatly improves the operation stability and reliability of the SaaS service cloud platform under high load conditions, and provides users with a better and more stable service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 of 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.

[0045] First Embodiment

[0046] Please refer to Figure 1 , this application provides a data processing system based on a SaaS service cloud platform, including a platform monitoring center, an interaction feature analysis terminal, an execution center, an interaction process analysis terminal, and a limit feature determination terminal. The platform monitoring center is electrically connected to the input nodes of the interaction feature analysis terminal or the interaction process analysis terminal respectively, and the interaction feature analysis terminal is electrically connected to the input node of the execution center, and the interaction process analysis terminal, the limit feature determination terminal, and the execution center are electrically connected in sequence from the output node to the input node;

[0047] Among them, the platform monitoring center, based on the monitored data interaction process, will confirm the different data interaction parties associated with the data interaction process of this service cloud platform, and simultaneously monitor the interaction rates associated with the corresponding data interaction processes of different data interaction parties in real time. The data interaction process that interacts with the service platform is associated with different data interaction parties. Different data interaction parties are associated with different data transmission protocols with this service cloud platform. The specific interaction logic can be confirmed according to different data transmission protocols to avoid data congestion when multiple data interaction parties interact at the same time:

[0048] Among them, the interaction feature analysis end confirms the data formats associated with different data interaction parties according to the data transmission protocols associated with different data interaction parties and the service cloud platform, confirms the conversion rates associated with the service cloud platform in different data format conversion processes from historical cloud data, and selects the best data interaction logic. Specifically, when its service platform is aimed at the specific conversion of different data types, there are different replacement logics in the replacement process of the conversion process. Therefore, the fastest replacement process can be selected according to the specific replacement characteristics in the replacement process, so as to achieve a better data interaction logic, ensure that different data interaction parties can achieve fast interaction effects when interacting at the same time, and reduce the interaction burden of multiple interaction processes;

[0049] The specific method of selecting the best interaction logic is:

[0050] S11. Mark the data formats associated with different data interaction parties as G i , where i represents different data interaction parties, randomly combine two sets of data formats, confirm several sets of combined data format sets, and confirm the logical exchange time of different combined data format sets from historical cloud data. The logical exchange time can be understood as: the proposed combined data format sets are A and B, and the interval between the processing of data in type A format and the processing of data in type B format by this data cloud platform is the logical exchange time. The confirmed several sets of logical exchange time are averaged, the combination set features are locked, and the combination set features associated with different combination data format sets are confirmed in turn. In addition, there are no two completely identical sets of data formats in each combination data format set, that is, there can be a single set of the same format;

[0051] S12, from the confirmed several groups of data formats G iAmong them, a group of formats is randomly selected as the initial format, and the first group of selection processes is executed. The initial format is denoted as the format to be determined, and the set of different combined data formats associated with this format to be determined is determined. Then, from the combined set characteristics of the different combined data formats, the minimum value is selected, and the combined data format set associated with the minimum value is denoted as the subsequent format of this format to be determined (in the confirmation process of its subsequent format, if there are subsequent formats with the same combined set characteristics, a group of subsequent formats can be randomly selected as the selected specific format);

[0052] S13. Then, the subsequent format is processed in the same way as the format to be determined, and the subsequent format is sequentially confirmed. The already selected G i does not participate in the confirmation process of the subsequent format. Based on the sequential confirmation specific process, several groups of data formats are sorted into a format data column, and the combined set characteristics associated with adjacent formats in this format data column are summed to confirm the comprehensive characteristics of this format data column;

[0053] S14. Then, other selection processes are executed: Another group of formats is randomly selected as the initial format. The data format that has been selected as the initial format cannot be selected again. Using the same method as in steps S12 - S13, the comprehensive characteristics of the format data column corresponding to the second group of selection processes are confirmed;

[0054] S15. The comprehensive characteristics of different format data columns are sequentially confirmed, and from the several groups of confirmed comprehensive characteristics, the minimum value is selected. The format data column associated with the minimum value is used as the best data interaction logic, and this best data interaction logic is transmitted to the execution center;

[0055] Specifically, it is assumed that the data formats associated with different data interaction parties of this service cloud platform are: A, B, C, and D. Among them, the combined data format sets regarding A are A - B, A - C, and A - D, the combined data formats regarding B are B - C, B - D, and the combined data set regarding C is C - D;

[0056] For different combined data sets, there are different combined set characteristics;

[0057] Execute the first group of selection processes, with data format A as the initial format, and then sequentially perform format confirmation. Then, the corresponding format data column can be determined in the same way as in steps S12 - S13;

[0058] Then, with data format B as the initial format, a new group of format data columns is determined, and so on. Then, with data formats C and D as the corresponding initial formats in sequence, and the format data columns associated with the subsequent ones are sequentially confirmed in sequence to complete the specific confirmation of the best data interaction logic.

[0059] Among them, the execution center directly executes this optimal data interaction logic when multiple data interaction parties perform data interaction at the same time according to the confirmed optimal data interaction logic.

[0060] Among them, the interaction process analysis end confirms the process characteristics of the corresponding data interaction process according to the interaction rate associated with different data interaction processes of different data interaction parties and confirms the occupancy utilization rate associated with the corresponding interaction nodes, and then re-confirms the comprehensive occupancy ratio based on the data cache characteristics associated with the corresponding data interaction process. The specific method for re-confirmation is as follows:

[0061] Define a set of monitoring periods, the monitoring period is a preset period, and its specific value is determined by the operator according to experience, generally taking 1h. Confirm the interaction rates associated with different data interaction parties at different times within this monitoring period and generate interaction rate change lines belonging to different data interaction parties within this monitoring period. The horizontal axis of this interaction rate change line is the time line, and its vertical axis is the interaction rate. There are multiple line segments within the interaction rate change line, and different line segments correspond to different interaction processes (because there are different interaction characteristics between the corresponding data interaction parties and the cloud service platform in different interaction processes, so there may be intermittent situations in each interaction process, so the corresponding interaction rate change line is generally multiple change rate lines with time breaks).

[0062] Confirm the characteristics of a single set of interaction rate change lines: Identify the difference in interaction rates associated with adjacent times, and the interaction rate difference = |interaction rate at the previous time - interaction rate at the next time|, where the interaction rate at the previous time and the interaction rate at the next time are the interaction rates associated with adjacent times. Starting from the starting point of a single set of interaction rate change lines, mark the sequentially confirmed interaction rate differences as V k , where k = 1, 2,..., n, where V1 is the first set of interaction rate differences confirmed from front to back, and V n is the last set of interaction rate differences confirmed. Confirm the variance of the confirmed interaction rate differences V k from front to back. Sequentially select columns of the same characteristic parameters from front to back. Starting from the first set of interaction rate differences, select other interaction rate differences in turn for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate differences until the variance ≥ Y1, and then stop selecting. Mark the selected interaction rate differences this time as the first set of values for the next-stage variance processing process, where Y1 is a preset value, and its specific value is determined by the operator according to experience. Denote the selected several sets of interaction rate differences this time as the same characteristic differences, and denote the partial curve associated with the same characteristic differences as the same characteristic curve segment;

[0063] Select the interaction rate differences one by one from front to back and perform variance confirmation. Based on the specific process confirmed by the moment, lock the same characteristic curve segments, and the same characteristic curve segments include at least three groups of interaction rate differences. If in a single confirmation process, the first group of differences and the second group of differences do not meet the variance confirmation process, then start from the second group of differences and perform the specific confirmation of the characteristic curve segments, and so on, to confirm several corresponding curve segments with relatively stable change trends;

[0064] Example: It is assumed that within a single line segment of the corresponding interaction rate change line, the associated interaction rate differences are {V1, V2, V3, V4, ……, V 10}, after variance confirmation, {V1, V2, V3, V4} are the numerical segments associated with the first group of the same characteristic curve segments confirmed. The specific condition of variance determination is not met between V4 and V5. Therefore, for the subsequent variance confirmation process, start from V5 and gradually perform relevant confirmation on the subsequent curve segments, and so on, to confirm the same characteristic curve segments associated within a single group of interaction rate change lines in sequence;

[0065] Perform mean processing on several groups of interaction rates associated with each same characteristic curve segment, confirm the interaction rate mean of the corresponding same characteristic curve segment, and select the maximum value from the confirmed several groups of interaction rate means as the process characteristic of this data interaction party in the data interaction process;

[0066] Then confirm the occupancy utilization rate associated with this data interaction process (which has been allocated by the system in advance), and use: process characteristic ÷ occupancy utilization rate = single-frequency characteristic;

[0067] Then, from the data cache rate associated within the unit time in the data interaction process, perform mean processing on several groups of data cache rates to confirm the cache rate mean, and use: (single-frequency characteristic × cache rate mean) + occupancy utilization rate = comprehensive occupancy ratio, confirm the comprehensive occupancy ratio associated with this data interaction process, and transmit it to the limit characteristic determination end;

[0068] Among them, the limit characteristic determination end evenly distributes the computing power resources of the cloud service platform according to the different comprehensive occupancy ratios associated with different data interaction processes, and based on the evenly distributed computing power resources, limits the interaction rate of each data interaction process and executes it through the execution center. The specific method for evenly distributing the computing power resources is as follows:

[0069] Calibrate the different comprehensive occupancy ratios associated with different data interaction processes as Z q where q represents different data interaction processes, sum several groups of comprehensive occupancy ratios Z q to confirm the total evaluation value ZP;

[0070] Calibrate the computing power resources of the cloud service platform as ZL. If ZP ≤ ZL, then based on the calibrated Z q Perform resource allocation from the computing power resources. If ZP > ZL, then a number of groups of Z q Perform ratio processing to confirm the ratio sequence for a number of groups of Z q Then evenly divide the computing power resource ZL according to this ratio sequence to confirm the evenly divided resources;

[0071] The specific method for restricting the interaction rate is as follows:

[0072] When the computing power resource associated with the corresponding data interaction process is ZL, use: ZL × single - frequency feature = restricted rate to confirm the restricted rate of the corresponding data interaction process;

[0073] When the computing power resource associated with the corresponding data interaction process is the evenly divided resource, use: evenly divided resource × single - frequency feature = restricted rate to confirm the restricted rate of the corresponding data interaction process;

[0074] Transmit the restricted rate and the computing power resource confirmed for the corresponding data interaction process to the execution center. The execution center adjusts the occupancy utilization rate of the corresponding data interaction process to ZL or the evenly divided resource and restricts the data interaction rate, and the restricted rate value is the restricted rate.

[0075] Using this method can not only effectively guarantee the interaction efficiency of each data interaction process to achieve a more stable interaction effect, but also ensure that each data interaction process will not be overloaded. By restricting the data interaction rate, a better data interaction process management effect can be achieved.

[0076] Second Embodiment

[0077] A data processing method based on the SaaS service cloud platform includes the following steps:

[0078] Step 1: Confirm the different data interaction parties associated with the data interaction processes of the service cloud platform, and simultaneously monitor the interaction rates associated with the corresponding data interaction processes of different data interaction parties in real - time;

[0079] Step 2: According to the data transmission protocols associated with different data interaction parties and the service cloud platform, confirm the data formats associated with different data interaction parties. From the historical cloud data, confirm the conversion rates associated with the service cloud platform in different data format conversion processes, select the best data interaction logic, and directly execute this best data interaction logic when multiple data interaction parties perform data interaction at the same time;

[0080] Step 3: Based on the interaction rates associated with different data interaction processes of different data interaction parties, and confirm the occupancy utilization rate associated with the corresponding interaction nodes, confirm the process characteristics of the corresponding data interaction processes, and then reconfirm the comprehensive occupancy ratio based on the data cache characteristics associated with the corresponding data interaction processes;

[0081] Step 4: Divide the computing power resources of the cloud service platform evenly according to the different comprehensive occupancy ratios associated with the corresponding different data interaction processes, and based on the evenly divided computing power resources, limit the interaction rate of each data interaction process, and execute through the execution center.

[0082] Some of the data in the above formula are used for numerical calculation 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.

[0083] 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 data processing system based on a SaaS service cloud platform, characterized in that, Including: A platform monitoring center that confirms different data interaction parties associated with the data interaction process of the service cloud platform and simultaneously monitors the interaction rate associated with the data interaction process of different data interaction parties in real time; An interaction feature analysis terminal that confirms the data formats associated with different data interaction parties according to the data transmission protocols associated with different data interaction parties and the service cloud platform, confirms the conversion rate associated with the service cloud platform in different data format conversion processes from historical cloud data, and selects the optimal data interaction logic; An interaction process analysis terminal that confirms the process characteristics of the corresponding data interaction process according to the interaction rate associated with different data interaction processes of different data interaction parties and confirms the occupancy utilization rate associated with the corresponding interaction node, and then reconfirms the comprehensive occupancy ratio based on the data cache characteristics associated with the corresponding data interaction process; A limit feature determination terminal that divides the computing power resources of the cloud service platform according to the different comprehensive occupancy ratios associated with the corresponding different data interaction processes, limits the interaction rate of each data interaction process based on the divided computing power resources, and executes through the execution center.

2. The data processing system based on the SaaS service cloud platform according to claim 1, wherein The specific method for the interaction feature analysis terminal to select the optimal data interaction logic is as follows: S11. Mark the data formats associated with different data interaction parties as G i , where i represents different data interaction parties, randomly combine two sets of data formats, confirm several sets of combined data format sets, and confirm the logical exchange time of different combined data format sets from historical cloud data, average the confirmed several sets of logical exchange time, lock the combination set features, and confirm the combination set features associated with different combined data format sets in turn; S12. From the several confirmed data format groups G i randomly select a group of formats as the initial format, execute the first group of selection processes, record the initial format as the format to be determined, determine the different combined data format sets associated with this format to be determined, then select the minimum value from the combined set features of the different combined data format sets, and record the combined data format set associated with the minimum value as the subsequent format of this format to be determined; S13. Then, process the subsequent formats in the same way as the to-be-determined formats, and sequentially confirm the subsequent formats. The already selected G i does not participate in the subsequent format confirmation process. Based on the specific process of sequential confirmation, sort several groups of data formats into a format data column, sum up the combined set features associated with adjacent formats in this format data column, and confirm the comprehensive features of this format data column. S14. Then execute other selection processes: randomly select another group of formats as the initial format, and the data format that has been selected as the initial format cannot be selected again. Use the same method as in steps S12 - S13 to confirm the comprehensive characteristics of the format data columns corresponding to the second group of selection processes; S15. Sequentially confirm the comprehensive characteristics of different format data columns, select the minimum value from the confirmed several groups of comprehensive characteristics, use the format data column associated with the minimum value as the optimal data interaction logic, and transmit this optimal data interaction logic to the execution center.

3. The data processing system based on the SaaS service cloud platform according to claim 2, wherein The execution center directly executes this optimal data interaction logic when multiple data interaction parties perform data interaction at the same time according to the confirmed optimal data interaction logic.

4. The data processing system based on the SaaS service cloud platform according to claim 1, wherein The specific method for the interaction process analysis terminal to confirm the process characteristics of the corresponding data interaction process is as follows: Define a set of monitoring periods, and the monitoring period is a preset period. Confirm the interaction rates associated with different data interaction parties at different times within this monitoring period and generate an interaction rate change line belonging to different data interaction parties within this monitoring period. The horizontal axis of this interaction rate change line is the time line, and its vertical axis is the interaction rate, and there are multiple line segments within the interaction rate change line, and different line segments correspond to different interaction processes; Perform feature confirmation on the single-group interaction rate change line: Identify the difference in interaction rates associated with adjacent time moments, where the interaction rate difference = |interaction rate at the previous time moment - interaction rate at the next time moment|. Here, the interaction rate at the previous time moment and the interaction rate at the next time moment are the interaction rates associated with adjacent time moments. Starting from the starting point of the single-group interaction rate change line, label the successively confirmed interaction rate differences as V k , where k = 1, 2, ……, n, where V1 is the first group of interaction rate differences confirmed from front to back, V n is the last group of interaction rate differences confirmed. Confirm the variance of the confirmed interaction rate differences V k from front to back. Select the same feature parameter columns successively from front to back. Starting from the first group of interaction rate differences, select other interaction rate differences successively for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate differences until the variance ≥ Y1, and then stop selecting. Label the selected interaction rate differences as the first group of values in the next-stage variance processing process. Here, Y1 is a preset value. Denote the selected several groups of interaction rate differences as the same feature differences, and denote the partial curve associated with the same feature differences as the same feature curve segment; Select and confirm the variance of the interaction rate differences in sequence from front to back, and lock the same - characteristic curve segments based on the specific process of time confirmation. The same - characteristic curve segments include at least three groups of interaction rate differences; Perform a mean processing on the several groups of interaction rates associated with each same - characteristic curve segment, confirm the interaction rate mean of the corresponding same - characteristic curve segment, and select the maximum value from the confirmed several groups of interaction rate means as the process characteristic of this data interaction party in the data interaction process.

5. The data processing system based on the SaaS service cloud platform according to claim 4, wherein, The specific method for the interaction process analysis terminal to confirm the comprehensive occupancy ratio is as follows: Confirm the proportion utilization rate associated with this data interaction process, using: Process feature ÷ Proportion utilization rate = Single-frequency feature; Then, from the data cache rate associated with the data interaction process within a unit time, perform a mean processing on several groups of data cache rates to confirm the mean cache rate. Use: (Single-frequency feature × Mean cache rate) + Proportion utilization rate = Comprehensive proportion rate. Confirm the comprehensive proportion rate associated with this data interaction process and transmit it to the limit feature determination end.

6. The data processing system based on the SaaS service cloud platform according to claim 5, characterized in that, The specific method for the limit feature determination end to equally divide the computing power resources is: Calibrate the different comprehensive occupancy ratios associated with different data interaction processes as Z q , where q represents different data interaction processes, and sum up several groups of comprehensive occupancy ratios Z q to confirm the total evaluation value ZP; Calibrate the computing power resources of the cloud service platform as ZL. If ZP ≤ ZL, then allocate resources from the computing power resources according to the calibrated Z q If ZP > ZL, then perform ratio processing on several groups of Z q to confirm the ratio sequence for several groups of Z q and then evenly divide the computing power resources ZL according to this ratio sequence to confirm the evenly divided resources; The specific method for restricting the interaction rate is: If the computing power resources associated with the corresponding data interaction process are ZL, use: ZL × Single-frequency feature = Restriction rate to confirm the restriction rate of the corresponding data interaction process; If the computing power resources associated with the corresponding data interaction process are evenly divided resources, use: Evenly divided resources × Single-frequency feature = Restriction rate to confirm the restriction rate of the corresponding data interaction process; Transmit the confirmed restriction rate and computing power resources of the corresponding data interaction process to the execution center.

7. The data processing system based on the SaaS service cloud platform according to claim 6, characterized in that, The execution center adjusts the proportion utilization rate of the corresponding data interaction process to ZL or evenly divided resources and restricts the data interaction rate, and the restricted rate value is the restriction rate.

8. A data processing method based on a SaaS service cloud platform, which is used to execute the data processing system based on the SaaS service cloud platform described in any one of claims 1-7, characterized in that, It includes the following steps: Step 1: Confirm different data interaction parties associated with the data interaction process of this service cloud platform, and simultaneously monitor the interaction rates associated with the data interaction processes of different data interaction parties in real time; Step 2: According to the data transmission protocols associated with different data interaction parties and this service cloud platform, confirm the data formats associated with different data interaction parties. From the historical cloud data, confirm the conversion rates associated with this service cloud platform in different data format conversion processes, select the best data interaction logic, and directly execute this best data interaction logic when multiple data interaction parties perform data interaction at the same time; Step 3: According to the interaction rates associated with different data interaction processes of different data interaction parties, and confirm the proportion utilization rate associated with the corresponding interaction node, confirm the process feature of the corresponding data interaction process, and then re-confirm the comprehensive proportion rate based on the data cache feature associated with the corresponding data interaction process; Step 4: According to the different comprehensive proportion rates associated with the corresponding different data interaction processes, evenly divide the computing power resources of the cloud service platform, and based on the evenly divided computing power resources, restrict the interaction rate of each data interaction process and execute it through the execution center.

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