Data processing system and method based on SaaS service cloud platform

Abstract

The present invention discloses a data processing system and method based on a SaaS service cloud platform. The present invention relates to the technical field of SaaS service cloud platforms, and solves the problem that feature confirmation is not performed according to the specific data interaction processes of different data interaction parties, resulting in insufficient optimization of the interaction processes in the later stage. The present invention comprehensively analyzes the interaction rate, utilization ratio and data cache characteristics of different data interaction parties, and accurately calculates the comprehensive ratio; the restriction feature determination end uses this as a basis to reasonably allocate and adjust the computing power resources of the cloud service platform; allocates resources according to the comprehensive ratio; when the total evaluation value is greater than the computing power resources, it is evenly divided through ratio processing to ensure the scientificity and rationality of resource allocation; the execution center accurately limits the data interaction rate according to the information provided by the restriction feature determination end, thereby ensuring the interaction efficiency of each data interaction process and maintaining a stable interaction effect.

Description

Technical Field

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

[0002] With the rapid development of information technology, the wave of enterprise digital transformation is sweeping across all industries. Traditional software deployment models face numerous difficulties, becoming a bottleneck restricting the efficient development of enterprises.

[0003] On the one hand, enterprises building and maintaining their own software systems require substantial investments in hardware, software licenses, and specialized technical operations and maintenance teams. Not only are hardware purchases expensive, but the hidden costs associated with rapid depreciation as technology evolves are also significant. Software licensing fees often rise dramatically with increasing enterprise scale and functionality requirements, leaving many businesses overwhelmed.

[0004] On the other hand, traditional software deployment is time-consuming, often taking months or even years from initial planning, installation and debugging to final launch. This makes it difficult for companies to quickly respond to market changes and miss out on development opportunities. Furthermore, system maintenance is extremely complex, requiring constant attention to software bug fixes, version upgrades, and hardware troubleshooting. Any slight oversight can lead to system crashes, severely impacting a company's business.

[0005] SaaS service cloud platforms have emerged as a response to this need. Leveraging the powerful computing, storage, and network resources of cloud computing and using the internet as a medium, they deliver software as a service to enterprise users. Enterprises no longer need to worry about hardware procurement, software installation, and maintenance. Instead, they can conveniently access and use a wide range of feature-rich software applications through a browser or lightweight client. This innovative service model enables enterprises to achieve digital transformation at a low cost and in a very short time. They can quickly deploy required business systems, flexibly adjust the scale of software use, and fully focus on core business development, gaining an advantage in the fiercely competitive market.

[0006] The SaaS service cloud platform did not perform feature confirmation based on the specific interaction processes of different data interaction parties, resulting in insufficient optimization of the interaction processes in the later stage and causing overload in some interaction processes. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides a data processing system and method based on the SaaS service cloud platform, which solves the problem of insufficient optimization of the interaction process in the later stage due to failure to confirm the characteristics 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, comprising:

[0009] The platform monitoring center confirms the different data interaction parties associated with the data interaction process of this service cloud platform, and simultaneously monitors the interaction rates associated with the corresponding data interaction processes of different data interaction parties in real time;

[0010] The interaction feature analysis terminal determines the data formats associated with different data interaction parties based on the data transmission protocols associated with the service cloud platform. It also determines the conversion rates associated with different data format conversion processes on the service cloud platform from historical cloud data. The specific methods are 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 the two sets of data formats, confirm several sets of combined data format sets, and confirm the logical swap time of different combined data format sets from historical cloud data. Average the confirmed logical swap time sets, lock the combination set features, and confirm the combination set features associated with different combination data format sets in sequence.

[0012] S12, from the confirmed several groups of data formats G i In the process, a set of formats is randomly selected as the initial format, the first set of selection processes is executed, the initial format is recorded as the pending format, and the different combination data format sets associated with the pending format are determined. Then, the minimum value is selected from the combination set characteristics of the different combination data format sets, and the combination data format set associated with the minimum value is recorded as the subsequent format of the pending format;

[0013] S13, and then treat the subsequent formats in the same way as the pending formats, and confirm the subsequent formats in turn. i Without participating in the subsequent format confirmation process, based on the specific process of sequential confirmation, several groups of data formats are sorted into format data columns, and the combined set features associated with adjacent formats in this format data column are summed to confirm the comprehensive features of this format data column;

[0014] S14. Execute another selection process: randomly select another set of formats as the initial formats. The data formats that have been selected as the initial formats cannot be selected again. The same method as steps S12-S13 is used to confirm the comprehensive characteristics of the format data columns corresponding to the second set of selected processes.

[0015] S15. Confirm the comprehensive features of data columns of different formats in sequence, select the minimum value from the confirmed sets of comprehensive features, use the format data column associated with the minimum value as the optimal data interaction logic, and transmit the optimal data interaction logic to the execution center;

[0016] The interaction process analysis end determines the interaction rates associated with different data interaction processes of different data interaction parties, confirms the utilization ratio associated with the corresponding interaction nodes, confirms the process characteristics of the corresponding data interaction processes, and then reconfirms the comprehensive utilization ratio based on the data cache characteristics associated with the corresponding data interaction processes. The specific method is as follows:

[0017] A set of monitoring periods is defined, where the monitoring period is a preset period. The interaction rates associated with different data interaction parties at different times within the monitoring period are confirmed, and interaction rate change lines associated with different data interaction parties within the monitoring period are generated. The horizontal axis of the interaction rate change line is the timeline, and the vertical axis is the interaction rate. The interaction rate change line contains multiple line segments, and different line segments correspond to different interaction processes.

[0018] Confirm the characteristics of a single group of interaction rate change lines: identify the interaction rate difference associated with adjacent moments, where the interaction rate difference = |interaction rate at the previous moment - interaction rate at the next moment|, where the interaction rate at the previous moment and the interaction rate at the next moment are the interaction rates associated with adjacent moments. Starting from the starting point of the single group of interaction rate change lines, the interaction rate differences confirmed in sequence are calibrated as V k , where k = , 2, ..., n, where V1 is the first set of interaction rate differences confirmed from the front to the back, V n is the last confirmed set of interaction rate differences, the confirmed interaction rate differences V from front to back k Perform variance confirmation, select the same characteristic parameter columns from front to back, starting from the first group of interaction rate differences, and then select other interaction rate differences for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate differences. Stop selecting when variance ≥ Y1, and calibrate the selected interaction rate differences this time as the first group of values ​​in the next stage of variance processing, where Y1 is a preset value. The selected groups of interaction rate differences are recorded as same-characteristic differences, and the part of the curve associated with the same-characteristic differences is recorded as the same-characteristic curve segment;

[0019] The interaction rate differences are selected from the front to the back and the variance is confirmed. Based on the specific process of the moment confirmation, the same characteristic curve segment is locked. The same characteristic curve segment includes at least three groups of interaction rate differences.

[0020] Performing average processing on several groups of interaction rates associated with each characteristic curve segment to determine the average interaction rate corresponding to the characteristic curve segment, and selecting the maximum value from the determined average interaction rate groups as the process characteristic of this data interaction party in the data interaction process;

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

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

[0023] Preferably, the interaction process analysis terminal confirms the comprehensive proportion in the following specific manner:

[0024] Determine the utilization ratio associated with this data interaction process using the formula: process characteristics ÷ utilization ratio = single-frequency characteristics.

[0025] Then, the data cache rates associated with each unit time in the data interaction process are averaged for several groups of data cache rates to confirm the cache rate average. The following formula is used: (single-frequency feature × cache rate average) + proportion utilization = comprehensive proportion. The comprehensive proportion associated with this data interaction process is confirmed and transmitted to the restriction feature determination end.

[0026] Preferably, the restriction feature determination terminal divides the computing power resources equally in the following specific manner:

[0027] The different comprehensive ratios associated with different data interaction processes are calibrated as Z q , where q represents different data interaction processes, and the comprehensive proportion of several groups Z q Perform summation and confirm the total evaluation value ZP;

[0028] The computing power resources of the cloud service platform are calibrated as ZL. If ZP≤ZL, then according to the calibrated Z q Allocate resources from computing resources. If ZP>ZL, then allocate several groups of Z q Perform ratio processing to confirm the Z q The ratio sequence of , and then the computing power resources ZL are evenly divided according to this ratio sequence to confirm the evenly divided resources;

[0029] The specific methods for limiting the interaction rate are as follows:

[0030] If the computing power resource associated with the corresponding data interaction process is ZL, the limit rate of the corresponding data interaction process is determined by: ZL × single frequency characteristics = limit rate;

[0031] If the computing power resources associated with the corresponding data interaction process are evenly distributed resources, the following formula is used: evenly distributed resources × single-frequency characteristics = rate limit to determine the rate limit for the corresponding data interaction process.

[0032] The limit rate and computing resources confirmed by the corresponding data interaction process are transmitted to the execution center.

[0033] Preferably, the execution center adjusts the utilization ratio of the corresponding data interaction process to ZL or evenly distributes resources, and limits the data interaction rate, and the limited rate value is the limit rate.

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

[0035] Step 1: 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 data interaction processes of different data interaction parties in real time;

[0036] Step 2: Based on the data transmission protocols associated with different data interacting parties and the service cloud platform, determine the data formats associated with different data interacting parties. From historical cloud data, determine the conversion rates associated with the service cloud platform in different data format conversion processes. Select the optimal data interaction logic. Based on the determined optimal data interaction logic, directly execute this optimal data interaction logic when multiple data interacting parties are interacting with data at the same time.

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

[0038] Step 4: Divide the computing power resources of the cloud service platform according to the different comprehensive proportions associated with different data interaction processes, and based on the divided computing power resources, limit 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 a SaaS service cloud platform. Compared with the existing technology, it has the following advantages:

[0040] This invention effectively avoids data congestion by confirming the interaction logic based on different data transmission protocols. The interaction feature analysis terminal uses a unique algorithm to select the best data interaction logic from historical cloud data, greatly improving the efficiency of data interaction. For example, when faced with multiple data formats, it can accurately find the optimal processing order, reduce the burden of multiple interaction processes, and ensure that different data interaction parties can achieve rapid response when interacting at the same time, significantly improving the overall interaction effect and ensuring the efficiency and smoothness of data interaction.

[0041] By comprehensively analyzing the interaction rates, utilization ratios, and data cache characteristics of different data interaction parties, the comprehensive ratio is accurately calculated. Based on this, the restriction feature determination end reasonably allocates and adjusts the computing resources of the cloud service platform. When the total evaluation value is less than or equal to the computing resources, resources are directly allocated based on the comprehensive ratio; when the total evaluation value is greater than the computing resources, they are evenly divided through ratio processing to ensure the scientific and rational allocation of resources. At the same time, the execution center accurately limits the data interaction rate based on the information provided by the restriction feature determination end, which not only guarantees 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 the data interaction process, and greatly improves the operational stability and reliability of the SaaS service cloud platform under high load conditions, providing 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

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] First embodiment

[0045] See also Figure 1 , the present application provides a data processing system based on a SaaS service cloud platform, including a platform monitoring center, an interactive feature analysis terminal, an execution center, an interactive process analysis terminal, and a restriction feature determination terminal, wherein the platform monitoring center is electrically connected to the interactive feature analysis terminal or the interactive process analysis terminal input node, and the interactive feature analysis terminal is electrically connected to the execution center input node, and the interactive process analysis terminal, the restriction feature determination terminal, and the execution center are electrically connected from the output node to the input node in sequence;

[0046] 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 data with this service platform is associated with different data interaction parties. Different data interaction parties are associated with this service cloud platform using different data transmission protocols. Based on different data transmission protocols, the specific interaction logic can be confirmed to avoid data congestion when multiple data interaction parties interact at the same time:

[0047] Among them, the interaction feature analysis end confirms the data formats associated with different data interaction parties based on the data transmission protocols associated with different data interaction parties and this service cloud platform, confirms the conversion rates associated with this 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 targeted 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 based on 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;

[0048] The specific method of selecting the best interaction logic is:

[0049] 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. The time interval between the processing of data in type A format and the start of 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 characteristics are locked, and the combination set characteristics associated with different combination data format sets are confirmed in turn. In addition, there are no two completely identical data formats in each combination data format set, that is, there can be a single set of identical formats.

[0050] S12, from the confirmed several groups of data formats G iIn the process, a group of formats is randomly selected as the initial format, and the first group selection process is executed. The initial format is recorded as the pending format, and the different combination data format sets associated with the pending format are determined. Then, the minimum value is selected from the combination set characteristics of the different combination data format sets, and the combination data format set associated with the minimum value is recorded as the subsequent format of the pending format (in the confirmation process, if there is a subsequent format with the same combination set characteristics, a group of subsequent formats can be randomly selected as the selected specific format);

[0051] S13, and then treat the subsequent formats in the same way as the pending formats, and confirm the subsequent formats in turn. i Without participating in the subsequent format confirmation process, based on the specific process of sequential confirmation, several groups of data formats are sorted into format data columns, and the combined set features associated with adjacent formats in this format data column are summed to confirm the comprehensive features of this format data column;

[0052] S14. Execute another selection process: randomly select another set of formats as the initial formats. The data formats that have been selected as the initial formats cannot be selected again. The same method as steps S12-S13 is used to confirm the comprehensive characteristics of the format data columns corresponding to the second set of selected processes.

[0053] S15. Confirm the comprehensive features of data columns of different formats in sequence, select the minimum value from the confirmed sets of comprehensive features, use the format data column associated with the minimum value as the optimal data interaction logic, and transmit the optimal data interaction logic to the execution center;

[0054] Specifically, the data formats associated with different data interaction parties of this service cloud platform are proposed to be: A, B, C and D, among which the combined data format sets for A are AB, AC and AD, the combined data formats for B are BC and BD, and the combined data set for C is CD;

[0055] For different combination data sets, there are different combination set features;

[0056] Execute the first set of selection processes, using data format A as the initial format, and then perform format confirmation in sequence. Then, the corresponding format data column can be determined in the same manner as steps S12-S13;

[0057] Then, using data format B as the initial format, a new set of format data columns is determined. Similarly, data formats C and D are used as the corresponding initial formats, and the subsequent associated format data columns are confirmed in turn to complete the specific confirmation of the optimal data interaction logic.

[0058] The execution center directly executes the optimal data interaction logic according to the confirmed optimal data interaction logic when multiple data interaction parties are interacting with each other at the same time.

[0059] The interaction process analysis end determines the interaction rates associated with different data interaction processes of different data interaction parties, confirms the utilization ratio associated with the corresponding interaction nodes, confirms the process characteristics of the corresponding data interaction processes, and then reconfirms the comprehensive utilization ratio based on the data cache characteristics associated with the corresponding data interaction processes. The specific method for reconfirmation is as follows:

[0060] A set of monitoring periods is defined. The monitoring period is a preset period. The specific value is determined by the operator based on experience, and is generally 1 hour. The interaction rates associated with different data interaction parties at different times within this monitoring period are confirmed, and interaction rate change lines associated with different data interaction parties within this monitoring period are generated. The horizontal axis of this interaction rate change line is the timeline, and the 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 the corresponding data interaction parties and the cloud service platform have different interaction characteristics in different interaction processes, each interaction process may be discontinuous, so the corresponding interaction rate change line is generally multiple change rate lines with discontinuities at multiple times).

[0061] Confirm the characteristics of a single group of interaction rate change lines: identify the interaction rate difference associated with adjacent moments, where the interaction rate difference = |interaction rate at the previous moment - interaction rate at the next moment|, where the interaction rate at the previous moment and the interaction rate at the next moment are the interaction rates associated with adjacent moments. Starting from the starting point of the single group of interaction rate change lines, the interaction rate differences confirmed in sequence are calibrated as V k , where k = 1, 2, ..., n, where V1 is the first set of interaction rate differences confirmed from the beginning to the end, V n is the last confirmed set of interaction rate differences, the confirmed interaction rate differences V from front to back k Perform variance confirmation, select the same characteristic parameter columns from the front to the back, start from the first group of interaction rate difference values, and select other interaction rate difference values ​​in turn for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate difference values. Stop selecting when variance ≥ Y1, and calibrate the interaction rate difference values ​​selected this time as the first group of values ​​in the next stage of variance processing. Y1 is a preset value, and its specific value is determined by the operator based on experience. The several groups of interaction rate difference values ​​selected this time are recorded as same characteristic differences, and the part of the curve associated with the same characteristic difference values ​​is recorded as the same characteristic curve segment;

[0062] The interaction rate differences are selected from the front to the back and the variance is confirmed. Based on the specific process of moment confirmation, the same characteristic curve segment is locked. The same characteristic curve segment includes at least three groups of interaction rate differences. If the first group of differences and the second group of differences do not meet the variance confirmation process during a single confirmation process, then the second group of differences is restarted to confirm the specific characteristic curve segment. Similarly, several corresponding curve segments with relatively stable change trends are confirmed.

[0063] Example: It is proposed that within a single segment of the corresponding interaction rate change line, the associated interaction rate difference is {V1, V2, V3, V4, ..., V 10}, after variance confirmation, {V1, V2, V3, V4} are the numerical segments associated with the first group of confirmed characteristic curve segments, and V4 and V5 do not meet the specific conditions for variance determination. Therefore, in the subsequent variance confirmation process, starting from V5, the subsequent curve segments are gradually confirmed. Similarly, the same characteristic curve segments associated with a single group of interaction rate change lines are confirmed in turn;

[0064] Performing average processing on several groups of interaction rates associated with each characteristic curve segment to determine the average interaction rate corresponding to the characteristic curve segment, and selecting the maximum value from the determined average interaction rate groups as the process characteristic of this data interaction party in the data interaction process;

[0065] Then confirm the utilization ratio associated with this data interaction process (allocated by the system in advance) using the formula: process characteristics ÷ utilization ratio = single frequency characteristics.

[0066] Then, the data cache rates associated with each unit time in the data interaction process are averaged for several groups of data cache rates to confirm the cache rate average. The following formula is used: (single-frequency feature × cache rate average) + proportion utilization = comprehensive proportion. The comprehensive proportion associated with this data interaction process is confirmed and transmitted to the restriction feature determination end.

[0067] The restriction feature determination end evenly distributes the computing power resources of the cloud service platform according to the different comprehensive proportions 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 of evenly distributing computing power resources is as follows:

[0068] The different comprehensive ratios associated with different data interaction processes are calibrated as Z q , where q represents different data interaction processes, and the comprehensive proportion of several groups Z q Perform summation and confirm the total evaluation value ZP;

[0069] The computing power resources of the cloud service platform are calibrated as ZL. If ZP≤ZL, then according to the calibrated Z q Allocate resources from computing resources. If ZP>ZL, then allocate several groups of Z q Perform ratio processing to confirm the Z q The ratio sequence of , and then the computing power resources ZL are evenly divided according to this ratio sequence to confirm the evenly divided resources;

[0070] The specific methods for limiting the interaction rate are as follows:

[0071] If the computing power resource associated with the corresponding data interaction process is ZL, the limit rate of the corresponding data interaction process is determined by: ZL × single frequency characteristics = limit rate;

[0072] If the computing power resources associated with the corresponding data interaction process are evenly distributed resources, the following formula is used: evenly distributed resources × single-frequency characteristics = rate limit to determine the rate limit for the corresponding data interaction process.

[0073] The limit rate and computing resources confirmed by the corresponding data interaction process are transmitted to the execution center, and the execution center adjusts the utilization ratio of the corresponding data interaction process to ZL or evenly distributes resources, and limits the data interaction rate. The limited rate value is the limit rate.

[0074] This approach not only effectively guarantees the interaction efficiency of each data interaction process, thereby achieving a more stable interaction effect, but also ensures that each data interaction process will not be overloaded. By limiting the data interaction rate, better data interaction process management effects can be achieved.

[0075] Second embodiment

[0076] The data processing method based on the SaaS service cloud platform includes the following steps:

[0077] Step 1: 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 data interaction processes of different data interaction parties in real time;

[0078] Step 2: Based on the data transmission protocols associated with different data interacting parties and the service cloud platform, determine the data formats associated with different data interacting parties. From historical cloud data, determine the conversion rates associated with the service cloud platform in different data format conversion processes. Select the optimal data interaction logic. Based on the determined optimal data interaction logic, directly execute this optimal data interaction logic when multiple data interacting parties are interacting with data at the same time.

[0079] Step 3: Based on the interaction rates associated with different data interaction processes of different data interaction parties, confirm the utilization ratio associated with the corresponding interaction nodes, confirm the process characteristics of the corresponding data interaction processes, and then reconfirm the comprehensive utilization ratio based on the data cache characteristics associated with the corresponding data interaction processes;

[0080] Step 4: Divide the computing power resources of the cloud service platform according to the different comprehensive proportions associated with different data interaction processes, and based on the divided computing power resources, limit the interaction rate of each data interaction process and execute it through the execution center.

[0081] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0082] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents 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 by: include: The platform monitoring center confirms the different data interaction parties associated with the data interaction process of this service cloud platform, and simultaneously monitors the interaction rates associated with the corresponding data interaction processes of different data interaction parties in real time; The interaction feature analysis end determines the data formats associated with different data interaction parties based on the data transmission protocols associated with the service cloud platform. It also determines the conversion rates associated with different data format conversion processes on the service cloud platform from historical cloud data and selects the optimal data interaction logic. The interaction process analysis end determines the interaction rates associated with different data interaction processes of different data interaction parties, confirms the utilization ratio associated with the corresponding interaction nodes, confirms the process characteristics of the corresponding data interaction processes, and then reconfirms the comprehensive utilization ratio based on the data cache characteristics associated with the corresponding data interaction processes. The restriction feature determination end divides the computing power resources of the cloud service platform evenly according to the different comprehensive proportions associated with different data interaction processes, and based on the divided computing power resources, limits the interaction rate of each data interaction process and executes it through the execution center.

2. The data processing system based on the SaaS service cloud platform according to claim 1, characterized in that: The specific method for selecting the optimal data interaction logic at the interaction feature analysis end 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 the two sets of data formats, confirm several sets of combined data format sets, and confirm the logical swap time of different combined data format sets from historical cloud data. Average the confirmed logical swap time sets, lock the combination set features, and confirm the combination set features associated with different combination data format sets in sequence. S12, from the confirmed several groups of data formats G i In the process, a set of formats is randomly selected as the initial format, the first set of selection processes is executed, the initial format is recorded as the pending format, and the different combination data format sets associated with the pending format are determined. Then, the minimum value is selected from the combination set characteristics of the different combination data format sets, and the combination data format set associated with the minimum value is recorded as the subsequent format of the pending format; S13, and then treat the subsequent formats in the same way as the pending formats, and confirm the subsequent formats in turn. i Without participating in the subsequent format confirmation process, based on the specific process of sequential confirmation, several groups of data formats are sorted into format data columns, and the combined set features associated with adjacent formats in this format data column are summed to confirm the comprehensive features of this format data column; S14. Execute another selection process: randomly select another set of formats as the initial formats. The data formats that have been selected as the initial formats cannot be selected again. The same method as steps S12-S13 is used to confirm the comprehensive characteristics of the format data columns corresponding to the second set of selected processes. S15. Confirm the comprehensive features of data columns of different formats in sequence, select the minimum value from the confirmed sets of comprehensive features, use the format data column associated with the minimum value as the optimal data interaction logic, and transmit the optimal data interaction logic to the execution center.

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

4. The data processing system based on the SaaS service cloud platform according to claim 1, characterized in that: The specific method for the interaction process analysis end to confirm the process characteristics of the corresponding data interaction process is: A set of monitoring periods is defined, where the monitoring period is a preset period. The interaction rates associated with different data interaction parties at different times within the monitoring period are confirmed, and interaction rate change lines associated with different data interaction parties within the monitoring period are generated. The horizontal axis of the interaction rate change line is the timeline, and the vertical axis is the interaction rate. The interaction rate change line contains multiple line segments, and different line segments correspond to different interaction processes. Confirm the characteristics of a single group of interaction rate change lines: identify the interaction rate difference associated with adjacent moments, where the interaction rate difference = |interaction rate at the previous moment - interaction rate at the next moment|, where the interaction rate at the previous moment and the interaction rate at the next moment are the interaction rates associated with adjacent moments. Starting from the starting point of the single group of interaction rate change lines, the interaction rate differences confirmed in sequence are calibrated as V k , where k = 1, 2, ..., n, where V1 is the first set of interaction rate differences confirmed from the beginning to the end, V n is the last confirmed set of interaction rate differences, the confirmed interaction rate differences V from front to back k Perform variance confirmation, select the same characteristic parameter columns from front to back, starting from the first group of interaction rate differences, and then select other interaction rate differences for variance confirmation. If the confirmed variance satisfies: variance < Y1, then continue to select interaction rate differences. Stop selecting when variance ≥ Y1, and calibrate the selected interaction rate differences this time as the first group of values ​​in the next stage of variance processing, where Y1 is a preset value. The selected groups of interaction rate differences are recorded as same-characteristic differences, and the part of the curve associated with the same-characteristic differences is recorded as the same-characteristic curve segment; The interaction rate differences are selected from the front to the back and the variance is confirmed. Based on the specific process of the moment confirmation, the same characteristic curve segment is locked. The same characteristic curve segment includes at least three groups of interaction rate differences. Perform average processing on several groups of interaction rates associated with each characteristic curve segment to confirm the average of the interaction rates corresponding to the characteristic curve segment, and select the maximum value from the confirmed several groups of interaction rate averages as the process feature 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, characterized in that: The interaction process analysis terminal determines the comprehensive ratio in the following manner: Determine the utilization ratio associated with this data interaction process using the formula: process characteristics ÷ utilization ratio = single-frequency characteristics. Then, the data cache rates associated with each unit time in the data interaction process are averaged for several groups of data cache rates to confirm the cache rate average. The following formula is used: (single-frequency feature × cache rate average) + proportion utilization = comprehensive proportion. The comprehensive proportion associated with this data interaction process is confirmed and transmitted to the restriction 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 evenly dividing computing power resources at the restriction feature determination end is as follows: The different comprehensive ratios associated with different data interaction processes are calibrated as Z q , where q represents different data interaction processes, and the comprehensive proportion of several groups Z q Perform summation and confirm the total evaluation value ZP; The computing power resources of the cloud service platform are calibrated as ZL. If ZP≤ZL, then according to the calibrated Z q Allocate resources from computing resources. If ZP>ZL, then allocate several groups of Z q Perform ratio processing to confirm the Z q The ratio sequence of , and then the computing power resources ZL are evenly divided according to this ratio sequence to confirm the evenly divided resources; The specific methods for limiting the interaction rate are as follows: If the computing power resource associated with the corresponding data interaction process is ZL, the limit rate of the corresponding data interaction process is determined by: ZL × single frequency characteristics = limit rate; If the computing power resources associated with the corresponding data interaction process are evenly distributed resources, the following formula is used: evenly distributed resources × single-frequency characteristics = rate limit to determine the rate limit for the corresponding data interaction process. The limit rate and computing resources confirmed by the corresponding data interaction process are transmitted 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 utilization ratio of the corresponding data interaction process to ZL or evenly distributes resources, and limits the data interaction rate, where the limited rate value is the limit rate.

8. A data processing method based on a SaaS service cloud platform, the method being used to execute the data processing system based on a SaaS service cloud platform according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: 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 data interaction processes of different data interaction parties in real time; Step 2: Based on the data transmission protocols associated with different data interacting parties and the service cloud platform, determine the data formats associated with different data interacting parties. From historical cloud data, determine the conversion rates associated with the service cloud platform in different data format conversion processes. Select the optimal data interaction logic. Based on the determined optimal data interaction logic, directly execute this optimal data interaction logic when multiple data interacting parties are interacting with data at the same time. Step 3: Based on the interaction rates associated with different data interaction processes of different data interaction parties, confirm the utilization ratio associated with the corresponding interaction nodes, confirm the process characteristics of the corresponding data interaction processes, and then reconfirm the comprehensive utilization ratio based on the data cache characteristics associated with the corresponding data interaction processes; Step 4: Divide the computing power resources of the cloud service platform according to the different comprehensive proportions associated with different data interaction processes, and based on the divided computing power resources, limit the interaction rate of each data interaction process and execute it through the execution center.

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