CN114722034A

CN114722034A - Big data analysis method and system for cloud resource sharing

Info

Publication number: CN114722034A
Application number: CN202210352927.6A
Authority: CN
Inventors: 陆小东
Original assignee: Individual
Current assignee: Individual
Priority date: 2022-04-06
Filing date: 2022-04-06
Publication date: 2022-07-08

Abstract

The embodiment of the disclosure discloses a big data analysis method and a big data analysis system for cloud resource sharing, which can accurately and reliably realize the mining of interested operation events, the continuous analysis of the interested operation events and the description extraction of the significant events as far as possible by utilizing the idea of the mining of resource sharing operation data, and then accurately and reliably determine the resource sharing thermodynamic information under the resource sharing conversation to be analyzed as far as possible by means of the extracted description content of the significant events and the request response delay value of the target interested operation events. Compared with the traditional resource sharing thermodynamic analysis through topic analysis of interested operation events and the like, the method can avoid event mining errors caused by topic deletion or similarity to a certain extent through the description of the significant events, comprehensively considers the request response delay value of the target interested operation event on the premise of describing the content of the significant events, and can ensure the accuracy and the reliability of the resource sharing thermodynamic information of the resource sharing session to be analyzed.

Description

Big data analysis method and system for cloud resource sharing

Technical Field

The disclosure relates to the technical field of big data, in particular to a big data analysis method and system for cloud resource sharing.

Background

Cloud resources (Cloud resources) refer to online resources formed after Cloud computing is integrated with a related resource platform, and one of the characteristics of the Cloud resources is that the Cloud resources can be dynamically deployed in the Cloud computing platform, so that resource sharing processing across regions and time is realized, and the utilization rate of the Cloud resources is improved to the maximum extent. At present, with the popularization of cloud resource sharing, more and more industries start to realize the sharing of cloud/online resources. In order to better share resources, sharing heat analysis on a resource sharing process is one of important links, however, a sharing event identification error exists in a related sharing heat analysis technology, so that the precision and the reliability of the sharing heat analysis are difficult to guarantee.

Disclosure of Invention

One object of the present disclosure is to provide a big data analysis method and system for cloud resource sharing.

The technical scheme of the disclosure is realized by at least some of the following embodiments.

A big data analytics method for cloud resource sharing, wherein the method is implemented by a big data sharing system, the method comprising at least: determining a sharing operation data set and an analysis constraint condition, wherein the sharing operation data set is obtained by carrying out session big data analysis on a resource sharing session to be analyzed; the shared operation data set carries multiple groups of target resource shared operation data; the target resource sharing operation data covers the resource interaction node operation data of the resource sharing session to be analyzed; sequentially carrying out interested operation event mining on each group of target resource sharing operation data in the multiple groups of target resource sharing operation data, and sequentially carrying out continuous analysis on a plurality of interested operation events existing in the sharing operation data set to obtain a plurality of target interested operation events, the significant event description content of the target interested operation events in the corresponding target resource sharing operation data and the request response delay data of the target interested operation events in the corresponding target resource sharing operation data queue; and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining the significant event description content, the request response delay data and the analysis constraint condition pointed by the target interesting operation events respectively.

The method and the device are applied to the embodiment, the mining of the interested operation events, the analysis of the persistence of the interested operation events and the extraction of the description of the significant events can be realized as accurately and reliably as possible by utilizing the idea of the resource sharing operation data mining, and then the resource sharing thermodynamic information under the resource sharing conversation to be analyzed can be determined as accurately and reliably as possible by means of the extracted description content of the significant events and the request response delay value of the target interested operation events. Compared with the traditional resource sharing thermodynamic analysis through topic analysis of interested operation events and the like, the method can avoid event mining errors caused by topic deletion or similarity to a certain extent through the description of the significant events, comprehensively considers the request response delay value of the target interested operation event on the premise of describing the content of the significant events, and can ensure the accuracy and the reliability of the resource sharing thermodynamic information of the resource sharing session to be analyzed.

For some embodiments, the significant event description covers a first distributed expression of M significant event descriptions in N sets of target resource sharing operation data for the target operation event of interest to which the significant event description points; the analysis constraints include thermal hit conditions; the determining, in combination with the significant event description content, the request response delay data, and the analysis constraint condition, to which each of the target interesting operation events respectively points, resource sharing thermodynamic information of the resource sharing session to be analyzed includes: for each target operation event of interest, determining a thermodynamic analysis tag pointed to by the target operation event of interest in combination with a first distribution expression of the M significant event descriptions pointed to by the target operation event of interest and a second distribution expression of the thermodynamic hit conditions; the thermal analysis tag is intended to reflect whether the target operational event of interest hits the thermal hit condition; and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining a plurality of thermodynamic analysis tags pointed by the target interesting operation events and the request response delay data.

When the method is applied to the embodiment, through the first distribution expression of the significant event description pointed by the target interested operation event and the second distribution expression of the thermal hit condition, whether the target interested operation event hits the thermal hit condition or not can be accurately and credibly determined as far as possible, that is, the thermal analysis tag with guaranteed accuracy can be obtained, and then the resource sharing thermal analysis can be performed on the resource sharing session to be analyzed as accurately and credibly as possible by combining the thermal analysis tag and the request response delay data of the target interested operation event.

For some embodiments, the significant event description content includes K significant event description content duplets of the target operation event of interest, where a significant event description content duplet includes one or more of a first significant event description content of the target operation event activation link, a second significant event description content of the target operation event execution link, and a third significant event description content of the target operation event verification link.

With this embodiment, the distribution of the significant event description, such as the first significant event description, the second significant event description, or the third significant event description, may reflect the distribution of the target operation event of interest as accurately and reliably as possible, so that it may be determined whether the target operation event of interest hits a thermal hit condition based on the significant event description as accurately and reliably as possible.

For some embodiments that may be independent, the determining, in combination with the first distribution expression of the M significant event descriptions to which the target operation event of interest points and the second distribution expression of the thermal hit conditions, a thermal analysis tag to which the target operation event of interest points includes: and determining that the thermal analysis label covers the label of the target interesting operation event hitting the thermal hit condition on the basis that the correlation characteristics of two significant event descriptions pointed by K significant event description content binary groups are determined to exist by combining the first distribution expression and the second distribution expression and the thermal hit condition meets a set relation.

When the method is applied to the embodiment, the correlation characteristics described by the individual significant events and the thermal hit conditions meet the set relationship, the target interesting operation event hits the thermal hit conditions, and in this case, the thermal analysis tag needs to be changed into a tag including the target interesting operation event hit thermal hit conditions.

For some embodiments that may be independent, the determining, in combination with the first distribution expression of the M significant event descriptions to which the target operation event of interest points and the second distribution expression of the thermal hit conditions, a thermal analysis tag to which the target operation event of interest points includes: and determining that the thermal analysis tag carries a tag of the target interesting operation event hitting the thermal hit condition on the basis that the first distribution expression and the second distribution expression of the M significant event descriptions of the target interesting operation event meet the alignment index.

When one of the significant event descriptions is matched with the thermal hit condition, the target interesting operation event is reflected to hit the thermal hit condition, in this case, the thermal analysis tag needs to be changed into a tag including the target interesting operation event hitting the thermal hit condition, and by adopting the design, the precision and the reliability of the obtained thermal analysis tag can be ensured.

For some independent embodiments, the determining resource sharing thermal information of the resource sharing session to be analyzed in combination with the thermal analysis tag pointed to by the target operation event of interest and the request response delay data includes: determining request response state data of the target interested operation event by combining the shared operation data set; and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining a plurality of thermodynamic analysis tags pointed by the target interesting operation events, the request response delay data and the request response state data.

The method and the device are applied to the embodiment, on the premise of thermal analysis labels and request response delay data, request response state data of the target interested operation event are integrated, whether the target interested operation event is switched to the resource sharing session to be analyzed or switched out of the resource sharing session to be analyzed can be accurately and credibly determined as far as possible, and more accurate and complete resource sharing thermal information can be obtained.

For some embodiments that may be independent, the resource sharing thermal information covers a first running total of target operational events of interest for switching to the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed includes: determining the thermal analysis tag characterization hits the thermal hit condition, the request response state data characterization hits the thermal hit condition, and the request response state data characterization hits the request response state of the first interesting operation event is switched to the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a first specified delay value; determining a running total of a first operational event of interest as the first running total.

By applying the embodiment, the target interested operation event switched to the resource sharing session to be analyzed can be determined as accurately and credibly as possible through the thermal analysis tag, the request response delay data and the request response state data, so that the first accumulated value is determined accurately.

For some embodiments, the resource sharing thermal information includes a second running total of target operational events of interest switched out of the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed includes: determining a second interested operation event that the thermodynamic analysis tag characterization hits the thermodynamic hit condition, the request response state data characterization hits the thermodynamic hit condition, the request response state is switched out of the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a second designated delay value from the target interested operation events; determining the second accumulated value of the second interesting operation event as the second accumulated value.

The method and the device are applied to the embodiment, and the target interested operation event switched out of the resource sharing session to be analyzed can be accurately and credibly determined as far as possible by utilizing the thermal analysis tag, the request response delay data and the request response state data, so that the second accumulated value is accurately determined.

For some embodiments, the resource sharing thermal information includes a third running total of target operational events of interest corresponding to the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed further includes: combining the first running total and the second running total to determine the third running total; wherein the first accumulated value is an accumulated value of a first interesting operation event of which the thermal analysis tag representation hits the thermal hit condition and the request response state data representation hits the thermal hit condition among the target interesting operation events is switched to the resource sharing session to be analyzed, and the delay value of the request response delay data representation is greater than a first specified delay value; the second accumulated value is the accumulated value of the second interesting operation event, among the target interesting operation events, when the thermal analysis tag characterization hits the thermal hit condition, and when the request response state data characterization hits the thermal hit condition, the request response state is switched out of the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a second designated delay value.

With this embodiment, based on the accumulated value of the target operation event of interest switched to the resource sharing session to be analyzed, such as the above first accumulated value, and the accumulated value of the target operation event of interest switched out of the resource sharing session to be analyzed, such as the above second accumulated value, the accumulated value (third accumulated value) corresponding to the target operation event of interest under the resource sharing session to be analyzed can be determined as accurately and reliably as possible.

For some embodiments that may be independent, the performing interested operation event mining on each target resource sharing operation data in the multiple sets of target resource sharing operation data in sequence, and performing continuous analysis on a plurality of interested operation events existing in the sharing operation data set in sequence to obtain a plurality of target interested operation events includes: sequentially carrying out interested operation event mining on a plurality of groups of target resource sharing operation data to obtain a plurality of interested operation event mining results; each interested operation event mining result is bound with one interested operation event; determining quantitative comparison results between the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations, determining the same interesting operation events pointed by the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations on the basis that the quantitative comparison results are larger than a specified quantitative value, and determining the interesting operation events as the target interesting operation events.

By applying the method and the device, whether the operation events of interest pointed by the two operation event mining results of interest are the same operation events of interest can be determined as accurately and reliably as possible according to the quantitative comparison result of the two operation event mining results of interest, so that the continuity analysis of the operation events of interest can be performed as accurately and reliably as possible.

For some embodiments, after obtaining the number of target operational events of interest, the method further comprises: for each target operation event of interest in the plurality of target operation events of interest, determining that a persistent analysis anomaly exists for the target operation event of interest based on the target operation event of interest not being continuously located within a specified period.

Applied to the embodiment, an analysis exception may exist for a target operation event of interest that is not located in some stage, in which case, the target operation event of interest may be changed to an analysis exception in persistence, so as to achieve effective differentiation of analysis of persistence of the operation event of interest.

For some embodiments, which may be independent, the analysis constraint includes specifying a constraint window; the mining of the interested operation events of each group of target resource sharing operation data in the multiple groups of target resource sharing operation data in sequence and the continuous analysis of a plurality of interested operation events existing in the sharing operation data set in sequence to obtain a plurality of target interested operation events comprises the following steps: combining the third distribution expression of the specified constraint window, sequentially mining interesting operation events of resource sharing operation contents corresponding to the specified constraint window in multiple groups of target resource sharing operation data, and sequentially analyzing a plurality of interesting operation events existing in the resource sharing operation contents corresponding to the specified constraint window in the sharing operation data set to obtain a plurality of target interesting operation events; wherein the analysis constraint points to a thermal hit condition that corresponds at least in part to the specified constraint window.

The method and the device are applied to mining and continuity analysis only on the operation events of interest in the specified constraint window, so that the system processing load can be reduced as much as possible, and the resource utilization rate and the thermal analysis timeliness are guaranteed.

For some embodiments, after determining the first operational event of interest and the second operational event of interest, further comprising: and transmitting one or more of a semantic feature of switching the first interested operation event to the resource sharing session to be analyzed, a semantic feature of switching the second interested operation event to the resource sharing session to be analyzed, a first state feature of switching the first interested operation event to the resource sharing session to be analyzed, a second state feature of switching the second interested operation event to the resource sharing session to be analyzed, and a second distribution expression of the thermal hit conditions to the cloud platform system, so that the cloud platform system performs resource sharing thermal analysis on the resource sharing session to be analyzed.

The method and the device are applied to the embodiment, the information such as the first semantic feature and the first state feature of the interesting operation event switched to the resource sharing session to be analyzed, the information such as the second semantic feature and the second state feature of the interesting operation event switched to the resource sharing session to be analyzed and transmitted to the cloud platform system, the quality of resource sharing thermal analysis performed by the cloud platform system can be guaranteed, and the flexibility of the resource sharing thermal analysis is improved.

A big data sharing system, comprising: a memory for storing an executable computer program, a processor for implementing the above method when executing the executable computer program stored in the memory.

A computer-readable storage medium, on which a computer program is stored which, when executed, performs the above-described method.

Drawings

Fig. 1 is a schematic diagram illustrating one communication configuration of a big data sharing system in which an embodiment of the present disclosure may be implemented.

Fig. 2 is a flowchart illustrating a big data analysis method for cloud resource sharing, where an embodiment of the present disclosure may be implemented.

Fig. 3 is an architectural diagram illustrating an application environment in which a big data analysis method for cloud resource sharing of an embodiment of the present disclosure may be implemented.

Detailed Description

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. For the purpose of making the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be described in further detail with reference to the accompanying drawings, the described embodiments should not be construed as limiting the present disclosure, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present disclosure. In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing embodiments of the disclosure only and is not intended to be limiting of the disclosure.

Fig. 1 is a block diagram illustrating one communication configuration of a big data sharing system 100 that can implement an embodiment of the present disclosure, the big data sharing system 100 including a memory 101 for storing an executable computer program, and a processor 102 for implementing a big data analysis method for cloud resource sharing in the embodiment of the present disclosure when the executable computer program stored in the memory 101 is executed.

Fig. 2 is a flowchart illustrating a big data analysis method for cloud resource sharing, which may implement an embodiment of the present disclosure, where the big data analysis method for cloud resource sharing may be implemented by the big data sharing system 100 shown in fig. 1, and further may include a technical solution described in the following related steps.

Step 110: and determining a shared operation data set obtained by carrying out session big data analysis on the resource sharing session to be analyzed and analysis constraint conditions.

For some examples, the set of shared operational data carries multiple sets of target resource sharing operational data covering resource interaction node operational data of the resource sharing session to be analyzed. Further, the resource sharing session to be analyzed may be understood as a sharing session process or a sharing session scenario for preparing to perform resource sharing thermal information analysis, for example, a file resource sharing session corresponding to a collaborative office scenario. The shared operation data set can be obtained by analyzing a big data acquisition module which is deployed in a resource sharing session to be analyzed and is matched with the operation data of the resource interaction node. The analysis constraint condition may cover a thermal hit condition, and only when the operation event of interest hits the thermal hit condition, the analysis constraint condition may have a certain probability to be used as value information in the resource sharing thermal information. In other cases, the analysis constraint may also cover a specified constraint window (a decision window with a range characteristic for performing range identification and extraction of the operation event of interest), and in the case that the operation event of interest falls within the specified constraint window, the operation event of interest may be regarded as value information in the resource sharing thermodynamic information with a certain probability.

Step 120: and sequentially mining interested operation events of each group of target resource sharing operation data in the multiple groups of target resource sharing operation data, and sequentially analyzing a plurality of interested operation events existing in the sharing operation data set to obtain a plurality of target interested operation events, the significant event description content of the target interested operation events in the corresponding target resource sharing operation data, and the request response delay data of the target interested operation events in the corresponding target resource sharing operation data queue.

In the embodiment of the disclosure, the target resource sharing operation data can be subjected to interesting operation event mining and persistence analysis by means of the debugged AI machine learning model, and the significant event description content of the target interesting operation event is determined. The interested operation event may be a key event or an event with a higher importance degree in the resource sharing process, such as a resource mutual transmission event, a resource verification event, and the like. It can be understood that, when the interesting operation event mining is performed, the interesting operation event mining result of each interesting operation event in each group of target resource sharing operation data is determined. Then, the continuity analysis of the operation event of interest may be performed according to the joint analysis result of the mining result of the operation event of interest (such as the dissimilarity comparison result of the mining result of the operation event of interest), for example, in two sets of target resource sharing operation data having an upstream-downstream relationship or having a small intermittence, two operation events of interest to which the two mining results of the operation event of interest having a joint analysis result larger than a specified quantization value point are determined as the same operation event of interest, in which case, the operation event of interest may be regarded as the completion of the continuity analysis (such as the success of capturing), and thus may be determined as the target operation event of interest.

In the embodiment of the disclosure, the summary of the process of mining the operation events of interest can determine the significant event description content pointed to by each operation event of interest by means of the debugged AI machine learning model. Of course, after the target interesting operation event is determined, the target interesting operation event can be mined by the debugged AI machine learning model, and the description content of the significant event pointed by the target interesting operation event can be determined. Further, the significant event description content may be a data feature of a significant event description capable of reflecting the distribution of the target operation event of interest, and may cover a first distribution expression (such as relative position data) of M significant event descriptions in the N sets of target resource sharing operation data of the pointed (corresponding) target operation event of interest. For example, the data characteristic of the significant event description may be one or more of information pointed to by a first significant event description of the target operation event of interest, information pointed to by a second significant event description of the target operation event of interest, and information pointed to by a third significant event description of the target operation event of interest. In other words, the first significant event description content may correspond to a target operation event activation link, the second significant event description content may correspond to a target operation event execution link, the third significant event description content may correspond to a target operation event verification link, and the activation link, the execution link, and the verification link respectively correspond to different status links of an operation event.

On the basis of the above, based on the analysis of the persistence of the target interesting operation event, multiple sets of target resource sharing operation data pointed by the target interesting operation event can be determined, and the resource sharing operation data are sorted based on the collection sequence, so as to obtain the target resource sharing operation data queue.

It can be understood that, after the target resource sharing operation data queue of a certain target interest operation event is determined, the target resource sharing operation data queue may be combined to determine the operation change characteristics of the target interest operation event in the acquisition order empty window period of the two sets of target resource sharing operation data, and then according to the acquisition order empty window period of the two sets of target resource sharing operation data, the request response delay of the target interest operation event in the acquisition order empty window period of the two sets of target resource sharing operation data can be determined. The request reply latency data may be determined in conjunction with a number of the request reply latencies to which two of the sets of target resource sharing operation data are directed. For example, the request response delay time pointed by two groups of target resource sharing operation data is determined as the request response delay time data at once. The request response delay data may be recorded in units of seconds or milliseconds, but is not limited thereto.

Step 130: and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining the significant event description content, the request response delay data and the analysis constraint condition pointed by the target interesting operation events respectively.

For example, whether the target interesting operation event hits the thermal hit condition can be determined through the significant event description and the analysis constraint condition, the hit delay value of the target interesting operation event when the target interesting operation event hits the thermal hit condition can be determined through the request response delay data, and the target interesting operation event of which the hit delay value meets the specified requirement can be used as the value information in the resource sharing thermal information. Based on the method, the resource sharing thermodynamic information of the resource sharing session to be analyzed can be determined as accurately and reliably as possible by combining the description content of the significant event, the request response delay data and the analysis constraint condition. Further, the resource sharing thermodynamic information may be used as quantitative information for measuring the activity degree of the resource sharing session to be analyzed, and the higher the thermodynamic value corresponding to the resource sharing thermodynamic information is, the higher the activity degree of the resource sharing session to be analyzed is, the lower the thermodynamic value corresponding to the resource sharing thermodynamic information is, the lower the activity degree of the resource sharing session to be analyzed is, so that the targeted memory allocation or security protection processing may be performed on the resource sharing session to be analyzed based on the resource sharing thermodynamic information.

For example, the following techniques may be used: for each of the target operation events of interest, determining a thermal analysis tag to which the target operation event of interest points, which is intended to reflect whether the target operation event of interest hits the thermal hit condition, in combination with a first distribution expression of the M significant event descriptions to which the target operation event of interest points and a second distribution expression of the thermal hit condition. And determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining a plurality of thermodynamic analysis tags pointed by the target interesting operation events and the request response delay data.

For some possible examples, the thermal analysis tag pointed to by the target operation event of interest may be identified by K sets of significant event description content duplets pointed to by the relative significant event description, for example, the significant event description content duplet may cover one or more of a first significant event description content of the target operation event activation link, a second significant event description content of the target operation event execution link, and a third significant event description content of the target operation event verification link. The significant event description content binary group at least carries a distribution expression of a corresponding significant event description, for example, the first significant event description content of the target operation event activation link at least carries a first distribution expression of an active activation type event description and a first distribution expression of a passive activation type event description of the target operation event of interest. For another example, the second significant event description content of the target operation event runtime segment carries at least a first distribution expression of the islanding operation event description and a first distribution expression of the non-islanding operation event description of the target operation event of interest. It is understood that the distribution expression of the first significant event description, the second significant event description or the third significant event description can reflect the distribution situation of the target operation event of interest as accurately and reliably as possible, so that whether the target operation event of interest hits the thermal hit condition can be determined as accurately and reliably as possible based on the significant event description.

In actual implementation, the thermal analysis tag pointed to by the target operation event of interest can be determined by the following: and determining that the thermal analysis label covers the label of the target interesting operation event hitting the thermal hit condition on the basis that the correlation characteristics of two significant event descriptions pointed by K significant event description content binary groups are determined to exist by combining the first distribution expression and the second distribution expression and the thermal hit condition meets a set relation.

It can be understood that, the correlation characteristics described by the individual significant events and the thermal hit conditions satisfy the set relationship, reflecting that the target interesting operation event hits the thermal hit conditions, in which case the thermal analysis tag needs to be changed to a tag including the target interesting operation event hit thermal hit conditions, so that the accuracy and reliability of the obtained thermal analysis tag can be ensured.

For some possible examples, the thermal analysis tag to which the target operation event of interest points may also be determined by means of a separate one of the significant event descriptions, for example, on the basis that the first distribution expression and the second distribution expression of the M significant event descriptions of the target operation event of interest meet the alignment index, the thermal analysis tag is determined to carry the tag that the target operation event of interest hits the thermal hit condition. The compliance with the alignment index may exemplarily be understood as that the first distribution expression of the significant event descriptions is consistent with the second distribution expression, in which case one significant event description, which may reflect the target operation event of interest, corresponds to a thermal hit condition, in other words, the target operation event of interest corresponds to a thermal hit condition, in which case the target operation event of interest hit thermal hit condition may be determined. In addition, when one of the significant event descriptions matches the thermal hit condition, it indicates that the target interesting operation event hits the thermal hit condition, in which case the thermal analysis tag needs to be changed into a tag including the target interesting operation event hit thermal hit condition, and thus the accuracy and reliability of the obtained thermal analysis tag can be ensured.

It can be understood that, on the premise of the thermal analysis tag and the request response delay data, the request response state data of the target interested operation event can be further integrated to determine the resource sharing thermal information of the resource sharing session to be analyzed, so that it can be more accurately determined whether the target interested operation event is switched to the resource sharing session to be analyzed or switched out, and thus more accurate and complete resource sharing thermal information can be obtained.

In some cases, the request response state data may be determined according to the shared operation data set, such as determining a target resource sharing operation data queue pointed by the target interested operation event from the shared operation data set, and determining a distribution of the target interested operation event in two sets of target resource sharing operation data corresponding to the target resource sharing operation data queue, and then according to a collection order window period of the two sets of target resource sharing operation data, a request response state of the target interested operation event in the collection order window period of the two sets of target resource sharing operation data may be determined. The two sets of target resource sharing operation data may be two sets of resource sharing operation data in the target resource sharing operation data queue in an upstream and downstream relationship. The request reply status data may be determined in conjunction with a number of the request reply statuses to which two of the sets of target resource sharing operation data are directed. For example, the request response state data pointed to by two groups of target resource sharing operation data is determined as the request response state data at once. By the design, the request response state of the target interested operation event when the thermal hit condition is hit can be accurately determined according to the request response state data.

For some independent examples, the determining resource sharing thermal information of the resource sharing session to be analyzed in combination with the thermal analysis tag pointed to by the target operation event of interest, the request response delay data and the request response state data may be implemented by the following technologies.

Step 210: determining the thermodynamic analysis tag characterization to hit the thermodynamic hit condition, the request response state data characterization to hit the thermodynamic hit condition is a first interesting operation event which is switched to the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a first specified delay value; and determining the accumulated value of the first interesting operation event as the first accumulated value of the target interesting operation event switched to the resource sharing session to be analyzed.

Step 220: determining a second interested operation event that the thermodynamic analysis tag characterization hits the thermodynamic hit condition, the request response state data characterization hits the thermodynamic hit condition, the request response state is switched out of the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a second designated delay value from the target interested operation events; and determining the accumulated value of the second interesting operation event as a second accumulated value of a target interesting operation event switched out of the resource sharing session to be analyzed.

In the embodiment of the present disclosure, switching to the resource sharing session to be analyzed may be understood as establishing a connection with the resource sharing session to be analyzed, and switching out the resource sharing session to be analyzed may be understood as disconnecting the connection with the resource sharing session to be analyzed.

Step 230: combining the first running total and the second running total to determine the third running total.

For example, a comparison value (difference value) between the first cumulative value and the second cumulative value is determined, and the comparison value is determined as a third cumulative value, where the third cumulative value is a cumulative value corresponding to the target interested operation event of the resource sharing session to be analyzed.

Step 240: and determining the first accumulated value, the second accumulated value and the third accumulated value as the resource sharing thermal information of the resource sharing session to be analyzed.

In some possible examples, according to the thermal analysis tag, the request response delay data and the request response state data, the accumulated value of the target interesting operation events switched out of the resource sharing session to be analyzed, the accumulated value of the target interesting operation events switched to the resource sharing session to be analyzed and the accumulated value of the target interesting operation events corresponding to the resource sharing session to be analyzed can be determined as accurately and reliably as possible, and the accuracy and the reliability of the determined resource sharing thermal information are guaranteed to a certain extent.

For some possible examples, the mining of the operation events of interest is performed on each of the target resource sharing operation data sets in the multiple sets of target resource sharing operation data in sequence, and the continuous analysis is performed on a number of operation events of interest existing in the sharing operation data sets in sequence to obtain a number of target operation events of interest, which may be implemented by the following technologies: sequentially carrying out interested operation event mining on a plurality of groups of target resource sharing operation data to obtain a plurality of interested operation event mining results; each interested operation event mining result is bound with one interested operation event in sequence; determining quantitative comparison results between the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations, determining the same interesting operation events pointed by the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations on the basis that the quantitative comparison results are larger than a specified quantitative value, and determining the interesting operation events as the target interesting operation events.

For example, the target resource sharing operation data having the upstream and downstream relationship may be understood as the adjacent target resource sharing operation data, and the quantitative comparison result may be understood as the repetition rate.

In an actual implementation process, quantitative comparison results of two interested operation events mining results in any two groups of target resource sharing operation data with close acquisition order can be determined, on the basis that the quantitative comparison results are larger than a specified quantitative value, the two interested operation events pointed by the two interested operation event mining results are determined to be the same interested operation event, and the interested operation events are determined to be the target interested operation events.

Therefore, whether the same interested operation event exists in the interested operation events pointed by the two interested operation event mining results can be accurately and reliably determined as much as possible according to the quantitative comparison result of the two interested operation event mining results, and therefore the continuity analysis of the interested operation events can be accurately and reliably performed as much as possible.

It is understood that the quantitative comparison result in the embodiment of the present disclosure may also be understood as a joint analysis result (e.g., a dissimilarity analysis result) of two interested operation event mining results.

For some possible examples, if a target operational event of interest is not consistently located within a specified period, it is determined that a persistent analysis anomaly exists for the target operational event of interest. For target interesting operation events which are not located in certain stages, analysis abnormity may exist, in this case, the part of the target interesting operation events can be changed into the continuous analysis abnormity, so as to realize effective differentiation of the continuous analysis of the interesting operation events.

It can be understood that after a certain target operation event of interest is determined for the first time, an operation event of interest distinguishing word is added to the target operation event of interest, and for a target operation event of interest with an abnormal persistence analysis, the operation event of interest distinguishing word of the target operation event of interest is not added to the remaining target operation events mined after the addition.

For some possible examples, the analysis constraints cover not only thermal hit conditions, but also a specified constraint window. In this case, the mining of the interested operation events is performed on each group of target resource sharing operation data in the multiple groups of target resource sharing operation data in sequence, and the continuous analysis is performed on a plurality of interested operation events existing in the sharing operation data set in sequence to obtain a plurality of target interested operation events, which may be implemented by the following technical contents: in combination with the third distribution expression of the specified constraint window, carrying out interesting operation event mining on resource sharing operation contents corresponding to the specified constraint window in multiple groups of target resource sharing operation data in sequence, and carrying out continuous analysis on a plurality of interesting operation events existing in the resource sharing operation contents corresponding to the specified constraint window in the sharing operation data set in sequence to obtain a plurality of target interesting operation events; wherein the analysis constraint points to a thermal hit condition that corresponds at least in part to the specified constraint window.

In an actual implementation process, the pointed local resource sharing operation data can be determined from the target resource sharing operation data according to the third distribution expression of the designated constraint window, and the interested operation events are mined and analyzed continuously on the local resource sharing operation data queue to obtain a plurality of target interested operation events.

It can be understood that when the method is applied to the scheme, only the mining and the continuity analysis are carried out on the interested operation events corresponding to the specified constraint window, so that the processing load of the system can be reduced as much as possible, and the resource utilization rate and the thermal analysis timeliness can be guaranteed.

In the above embodiment, the specified constraint window and thermal hit condition are obtained by preprocessing the functional module that collects the shared operation data set. The specified constraint windows may be different types of thread windows. In the process of adding the specified constraint window, it is required to ensure that the core event characteristics of the interested operation event which is switched to or out of the resource sharing session to be analyzed correspond to the specified constraint window, so as to ensure the accuracy and the reliability of the mining, the continuity analysis and the resource sharing thermodynamic information analysis of the interested operation event.

In some examples, the resource interaction node operation data of the resource sharing session to be analyzed may be resource interaction node operation data node data _010, a specified constraint window kernel _020 is correspondingly set based on the resource interaction node operation data node data _010, and the method only mines and continuously analyzes an interested operation event in the specified constraint window. The thermal hit condition _030 corresponds at least in part to within the specified constraint window kernel _ 020. On the basis of the above example, when determining the resource-sharing thermal information, it is necessary to perform determination based on whether the target operation event of interest obtained by the persistence analysis hits the thermal hit condition _ 030.

For example, the association characteristic charcteristic _070 of the individual significant event description of the target operation event of interest case _050 (e.g., the activation authority authentication characteristic is determined as a significant event description, then the set of significant event descriptions may be the dynamic event activation characteristic of the target operation event of interest case _ 050) and the thermal hit condition _030 satisfy a set relationship, in which case, it may be determined that the target operation event of interest case _050 hits the thermal hit condition _030, and a certain significant event description of another target operation event of interest case _060 corresponds to the thermal hit condition _030, in which case, it may also be determined that the thermal hit condition _030 in another target operation event of interest case _ 060.

On the basis of the above, if the continuous analysis report outputs the same target operation event of interest at different timing stages, namely, the target operation event of interest _050 and the target operation event of interest _060, the request response status data of the target operation event of interest may be determined according to the semantic features of the resource sharing operation data corresponding to the mining target operation event of interest _050 and the semantic features of the resource sharing operation data corresponding to the mining target operation event of interest _ 060. For example, if the semantic feature of the resource sharing operation data corresponding to the mining target interested operation event case _050 is prior to the semantic feature of the resource sharing operation data corresponding to the mining target interested operation event case _060, it is determined that the request response state data of the target interested operation event reflects the request response state when the target interested operation event hits the thermal power hit condition, and the request response state is switched to the resource sharing session to be analyzed. And if the delay value of the request response delay data representation of the target interesting operation event, which is further determined according to the semantic features of the resource sharing operation data corresponding to the mining target interesting operation event case _050 and the semantic features of the resource sharing operation data corresponding to the mining target interesting operation event case _060, is higher than the first specified delay value, determining that the target interesting operation event is determined as the interesting operation event switched to the resource sharing session to be analyzed. And determining the first cumulative value according to the determined cumulative value of the type of the target interesting operation event, for example, the cumulative value of the interesting operation event switched to the resource sharing session to be analyzed.

On the basis of the above contents, if the semantic feature of the resource sharing operation data corresponding to the mining target interested operation event case _050 is later than the semantic feature of the resource sharing operation data corresponding to the mining target interested operation event case _060, determining that the request response state when the target interested operation event request response state data reflects that the target interested operation event hits the thermal hit condition is a request response state when the target interested operation event is switched out of the resource sharing session to be analyzed. And if the delay value represented by the request response delay data of the target interesting operation event is further determined according to the semantic features of the resource sharing operation data corresponding to the mining target interesting operation event case _050 and the semantic features of the resource sharing operation data corresponding to the mining target interesting operation event case _060 is greater than the second designated delay value, determining that the target interesting operation event is determined as the interesting operation event for switching out the resource sharing session to be analyzed. And determining the second accumulated value according to the determined accumulated value of the type of target interesting operation events, for example, the accumulated value of the interesting operation events switched out of the resource sharing session to be analyzed.

It can be understood that, before performing the resource sharing thermal information analysis, it is further determined whether to switch to the first state of the resource sharing session to be analyzed and to switch out the second state of the resource sharing session to be analyzed, where the first state and the second state are used to determine whether the request response state of the target operation event of interest when the thermal hit condition is hit is to switch to the resource sharing session to be analyzed or to switch out the resource sharing session to be analyzed.

In some independent embodiments, after determining the first interesting operation event and the second interesting operation event, the first interesting operation event, such as a target interesting operation event switched to the resource sharing session to be analyzed, switched to a semantic feature of the resource sharing session to be analyzed, the second interesting operation event, such as a target interesting operation event switched out of the resource sharing session to be analyzed, switched out of a semantic feature of the resource sharing session to be analyzed, switched to a first state feature of the resource sharing session to be analyzed, switched out of a second state feature of the resource sharing session to be analyzed, a second distribution expression of the thermal hit conditions, a partition word of the thermal hit conditions, a shared operation data set, a first state feature of the resource sharing session to be analyzed, and a second state feature of the resource sharing session to be analyzed, can be further switched to, One or more target resource sharing operation data queues pointed by each target interested operation event are transmitted to the cloud platform system, so that the cloud platform system performs resource sharing thermal analysis on the resource sharing session to be analyzed.

By means of the design, the second semantic feature, the second state feature and other features of the interesting operation event of the resource sharing session to be analyzed are switched out and transmitted to the cloud platform system through the first semantic feature, the first state feature and other data of the interesting operation event switched to the resource sharing session to be analyzed, the quality of resource sharing thermal analysis of the cloud platform system can be guaranteed, and the flexibility of the resource sharing thermal analysis is improved.

Based on the above, in some independent embodiments, after obtaining the resource sharing thermal information of the resource sharing session to be analyzed, the method may further include: responding to the fact that the resource sharing thermal value corresponding to the resource sharing thermal information exceeds a thermal threshold, and collecting sharing request behavior data aiming at the target sharing resource from the resource sharing session to be analyzed; and determining a sharing requirement description through the sharing request behavior data, and pushing a resource sharing service based on the sharing requirement description.

In the embodiment of the present disclosure, the thermal threshold may be adjusted according to an actual situation, for example, the thermal threshold may be set to a numerical value between 0 and 10, and for example, the thermal value may be 6, and the resource sharing thermal value may be obtained by performing text recognition on the resource sharing thermal information, for example, if the recognized resource sharing thermal value is 7, 7>6, so that sharing request behavior data for a target sharing resource may be collected from a resource sharing session to be analyzed, where the target sharing resource may be a file resource, a video resource, or another type of resource, and the sharing request behavior data may be initiated by a sharing requester. Based on this, the sharing requirement description of the sharing requester can be determined through the sharing request behavior data, so that targeted resource sharing service pushing is performed according to the sharing requirement description, for example, the sharing requirement description is that "a YYY file is expected to be used in a XXX period", the resource sharing service pushing can be "real-time occupation condition display for the YYY file", so that a corresponding resource sharing service can be provided for the sharing requester, for example, the sharing requester knows that the YYY file is in a non-occupation state in a ZZZ period prior to the XXX period through "real-time occupation condition display for the yy file", and then the yy file can be used in advance, so that the efficiency and the intelligence degree of resource sharing are improved.

In other independent embodiments, determining the sharing requirement description according to the sharing request behavior data may be implemented by the following technical solutions: determining a target user behavior event set meeting a requirement analysis condition through the shared request behavior data; respectively carrying out real-time type sharing demand analysis and delayed type sharing demand analysis on a plurality of user behavior event sets in the target user behavior event set to obtain a real-time type sharing demand analysis report set and a delayed type sharing demand analysis report set; performing first noise requirement cleaning processing on the real-time type sharing requirement analysis report set through a first noise requirement cleaning strategy to obtain a first user behavior event cluster comprising real-time type sharing requirements; performing second noise requirement cleaning processing on the delayed sharing requirement analysis report set through a second noise requirement cleaning strategy to obtain a second user behavior event cluster comprising the delayed sharing requirement; performing fusion processing based on the first user behavior event cluster and the second user behavior event cluster to obtain a target user behavior event cluster corresponding to a target sharing requirement in the target user behavior event set; the target sharing requirement comprises one or more of a real-time sharing requirement and a delay sharing requirement, and the target user behavior event cluster is used for mining the sharing requirement of the target user behavior event set; and mining the sharing requirement of the target user behavior event set through the target user behavior event cluster to obtain the sharing requirement description.

In the embodiment of the disclosure, the target user behavior event cluster can be accurately positioned by performing classification analysis on the urgency (time requirement) of the sharing requirement, and thus, in the process of obtaining the sharing requirement description, the sharing requirement mining is performed on the target user behavior event set through the target user behavior event cluster, so that not only the data analysis processing amount can be reduced, but also the noise interference can be reduced, and the determination accuracy and the reliability of the sharing requirement description can be improved.

In another independent embodiment, the performing real-time sharing demand analysis and delayed sharing demand analysis on the plurality of user behavior event sets in the target user behavior event set respectively to obtain a real-time sharing demand analysis report set and a delayed sharing demand analysis report set includes: respectively carrying out real-time sharing demand analysis on a plurality of user behavior event sets in the target user behavior event set to obtain real-time sharing demand analysis results in each user behavior event set and basic sharing demand semantics corresponding to each real-time sharing demand analysis result; determining a real-time sharing demand analysis report set based on real-time sharing demand analysis results and corresponding basic sharing demand semantics in each user behavior event set; and respectively carrying out time-delay type sharing demand analysis on a plurality of user behavior event sets in the target user behavior event set to obtain a time-delay type sharing demand analysis report set. By the design, the integrity of the real-time type sharing demand analysis report set and the delayed type sharing demand analysis report set can be guaranteed.

In other independent embodiments, the performing delayed sharing requirement analysis on the plurality of user behavior event sets in the target user behavior event set respectively to obtain a delayed sharing requirement analysis report set includes: respectively carrying out sharing preference analysis on a plurality of user behavior event sets in the target user behavior event set to obtain sharing preference analysis reports corresponding to the user behavior event sets; respectively analyzing the behavior habits of a plurality of user behavior event sets in the target user behavior event set to obtain behavior habit analysis reports respectively corresponding to the user behavior event sets; binding a sharing preference analysis report and a behavior habit analysis report corresponding to the same event; and performing delayed sharing demand analysis processing on the basis of the behavior habit analysis report bound with the target sharing preference analysis report in the target user behavior event set to obtain a delayed sharing demand analysis report set.

In other independent embodiments, the performing, by using a first noise requirement cleaning policy, a first noise requirement cleaning process on the real-time type sharing requirement analysis report set to obtain a first user behavior event cluster including a real-time type sharing requirement includes: respectively carrying out sharing demand semantic screening on each user behavior event set in the real-time type sharing demand analysis report set to obtain personalized sharing demand semantics corresponding to each user behavior event set; respectively simplifying analysis results based on the characteristic dimension of the real-time type sharing demand analysis result corresponding to the corresponding personalized sharing demand semantics in each user behavior event set to obtain a simplified real-time type sharing demand analysis report set; dynamically simplifying the simplified real-time sharing requirement analysis report set to obtain a plurality of first transition user behavior event clusters comprising the real-time sharing requirement; and according to the word vectors corresponding to the first transition user behavior event clusters, performing event cluster optimization on the first transition user behavior event clusters belonging to the same word vector to obtain the first user behavior event cluster comprising the real-time sharing requirement. By the design, the compactness and the completeness of the first user behavior event cluster can be balanced.

In other independent embodiments, the performing sharing requirement semantic screening on each user behavior event set in the real-time type sharing requirement analysis report set to obtain an individualized sharing requirement semantic corresponding to each user behavior event set includes: for each user behavior event set in the real-time type sharing demand analysis report set, when the quantitative statistical result of the basic sharing demand semantics of the user behavior event set is not less than two, determining a sharing demand semantics credibility factor of each basic sharing demand semantics; when the basic sharing requirement semantics with the largest sharing requirement semantic credibility factor is one, taking the basic sharing requirement semantics with the largest sharing requirement semantic credibility factor as the personalized sharing requirement semantics of the corresponding user behavior event set; when the number of the basic sharing requirement semantics with the largest sharing requirement semantic credibility factors is not less than two, determining an analysis result credibility factor of a corresponding real-time type sharing requirement analysis result for the basic sharing requirement semantics with the largest sharing requirement semantic credibility factors; and determining the personalized sharing requirement semantics corresponding to the corresponding user behavior event set according to the basic sharing requirement semantics corresponding to the maximum analysis result credible factor. By the design, the pertinence and the credibility of the personalized sharing requirement semantics can be ensured, and disorder of the personalized sharing requirement semantics is avoided.

Fig. 3 is an architecture diagram illustrating an application environment of a big data analysis method for cloud resource sharing in which a big data sharing system 100 and a data sharing participant 200 that communicate with each other may be included, in which an embodiment of the present disclosure may be implemented. Based on this, the big data sharing system 100 and the data sharing participating end 200 implement or partially implement the big data analysis method for cloud resource sharing of the embodiment of the present disclosure at runtime.

The embodiments of the present disclosure have been described above with reference to the accompanying drawings, and have at least the following advantages: the method can accurately and reliably realize the mining of the interested operation events, the analysis of the persistence of the interested operation events and the extraction of the description of the significant events as far as possible by utilizing the idea of the mining of the resource sharing operation data, and then can accurately and credibly determine the resource sharing thermodynamic information under the resource sharing conversation to be analyzed as far as possible by means of the extracted description content of the significant events and the request response delay value of the target interested operation events. Compared with the traditional resource sharing thermodynamic analysis through topic analysis of interested operation events and the like, the method can avoid event mining errors caused by topic deletion or similarity to a certain extent through the description of the significant events, comprehensively considers the request response delay value of the target interested operation event on the premise of describing the content of the significant events, and can ensure the accuracy and the reliability of the resource sharing thermodynamic information of the resource sharing session to be analyzed.

The above description is only for the preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure.

Claims

1. A big data analytics method for cloud resource sharing, wherein the method is implemented by a big data sharing system, the method comprising at least:

determining a sharing operation data set and an analysis constraint condition, wherein the sharing operation data set is obtained by carrying out session big data analysis on a resource sharing session to be analyzed; the sharing operation data set carries a plurality of groups of target resource sharing operation data, and the target resource sharing operation data covers the resource interaction node operation data of the resource sharing session to be analyzed;

sequentially carrying out interested operation event mining on each group of target resource sharing operation data in the multiple groups of target resource sharing operation data, and sequentially carrying out continuous analysis on a plurality of interested operation events existing in the sharing operation data set to obtain a plurality of target interested operation events, the significant event description content of the target interested operation events in the corresponding target resource sharing operation data and the request response delay data of the target interested operation events in the corresponding target resource sharing operation data queue;

and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining the significant event description content, the request response delay data and the analysis constraint condition pointed by the target interesting operation events respectively.

2. The method of claim 1, wherein the significant event description covers a first distributed expression of M significant event descriptions in N sets of target resource sharing operation data of the target operation event of interest to which the significant event description points, N, M being a positive integer; the analysis constraints include thermal hit conditions; the determining, in combination with the significant event description content, the request response delay data, and the analysis constraint condition, to which each of the target interesting operation events respectively points, resource sharing thermodynamic information of the resource sharing session to be analyzed includes:

for each target operation event of interest, determining a thermodynamic analysis tag pointed to by the target operation event of interest in combination with a first distribution expression of the M significant event descriptions pointed to by the target operation event of interest and a second distribution expression of the thermodynamic hit conditions; the thermal analysis tag is intended to reflect whether the target operational event of interest hits the thermal hit condition;

and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining a plurality of thermodynamic analysis tags pointed by the target interesting operation events and the request response delay data.

3. The method of claim 2, wherein the significant event description comprises K significant event description duplets of the target operation event of interest, K being a positive integer, wherein a significant event description duplet comprises one or more of a first significant event description of the target operation event activation link, a second significant event description of the target operation event execution link, and a third significant event description of the target operation event verification link.

4. The method of claim 3, wherein the determining the thermodynamic analysis tag pointed to by the target operational event of interest in combination with the first distributed representation of the M significant event descriptions pointed to by the target operational event of interest and the second distributed representation of the thermodynamic hit conditions comprises:

determining that the thermal analysis label covers a label of the target interesting operation event hitting the thermal hit condition on the basis that the correlation characteristics of two significant event descriptions pointed by K significant event description content binary groups are determined to exist by combining the first distribution expression and the second distribution expression and the thermal hit condition meets a set relation;

or, the determining, in combination with the first distribution expression of the M significant event descriptions pointed to by the target operation event of interest and the second distribution expression of the thermal hit condition, a thermal analysis tag pointed to by the target operation event of interest includes:

and determining that the thermal analysis tag carries a tag of the target interesting operation event hitting the thermal hit condition on the basis that the first distribution expression and the second distribution expression of the M significant event descriptions of the target interesting operation event meet the alignment index.

5. The method of claim 2, wherein the determining resource sharing thermal information of the resource sharing session to be analyzed in combination with the thermal analysis tag pointed to by the target operation event of interest and the request response delay data comprises:

determining request response state data of the target interested operation event by combining the shared operation data set;

and determining resource sharing thermodynamic information of the resource sharing session to be analyzed by combining a plurality of thermodynamic analysis tags pointed by the target interesting operation events, the request response delay data and the request response state data.

6. The method of claim 5, wherein the resource sharing thermal information encompasses a first running total of target operational events of interest for switching to the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed includes:

determining the thermal analysis tag characterization hits the thermal hit condition, the request response state data characterization hits the thermal hit condition, and the request response state data characterization hits the request response state of the first interesting operation event is switched to the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a first specified delay value; determining a running total of the first operational event of interest as the first running total;

wherein the resource sharing thermal information comprises a second accumulated value of target interesting operation events switched out of the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed includes: determining a second interested operation event that the thermodynamic analysis tag characterization hits the thermodynamic hit condition, the request response state data characterization hits the thermodynamic hit condition, the request response state is switched out of the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a second designated delay value from the target interested operation events; determining the second accumulated value of the second interesting operation event as the second accumulated value.

7. The method of claim 5, wherein the resource sharing thermal information comprises a third running total corresponding to a target operational event of interest of the resource sharing session to be analyzed; the determining, by combining the thermal analysis tags, the request response delay data, and the request response state data, which are pointed by the target interesting operation events, resource sharing thermal information of the resource sharing session to be analyzed further includes: combining the first running total and the second running total to determine the third running total;

wherein the first accumulated value is an accumulated value of a first interesting operation event of which the thermal analysis tag characterization hits the thermal hit condition, the request response state data characterization hits the thermal hit condition and the request response state data characterization switches to the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a first specified delay value, among the target interesting operation events; the second accumulated value is an accumulated value of a second interesting operation event of which the thermal analysis tag characterization hits the thermal hit condition and the request response state data characterization hits the thermal hit condition is switched out of the resource sharing session to be analyzed, and the delay value of the request response delay data characterization is greater than a second specified delay value;

wherein after determining the first operational event of interest, the second operational event of interest, the method further comprises: and transmitting one or more of a semantic feature of switching the first interested operation event to the resource sharing session to be analyzed, a semantic feature of switching the second interested operation event to the resource sharing session to be analyzed, a first state feature of switching the first interested operation event to the resource sharing session to be analyzed, a second state feature of switching the second interested operation event to the resource sharing session to be analyzed, and a second distribution expression of the thermal hit conditions to the cloud platform system, so that the cloud platform system performs resource sharing thermal analysis on the resource sharing session to be analyzed.

8. The method of claim 1, wherein the sequentially performing interesting operation event mining on each of the target resource sharing operation data sets in the plurality of sets of target resource sharing operation data, and sequentially performing persistence analysis on a plurality of interesting operation events existing in the sharing operation data sets to obtain a plurality of target interesting operation events comprises:

sequentially carrying out interested operation event mining on a plurality of groups of target resource sharing operation data to obtain a plurality of interested operation event mining results; each interested operation event mining result is bound with one interested operation event;

determining quantitative comparison results between the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations, determining the same interesting operation events pointed by the mining results of the interesting operation events in the target resource sharing operation data respectively corresponding to the two groups with the upstream and downstream relations on the basis that the quantitative comparison results are larger than a specified quantitative value, and determining the interesting operation events as target interesting operation events;

wherein after obtaining the number of target operational events of interest, the method further comprises: for each target operation event of interest in the plurality of target operation events of interest, determining that a persistent analysis anomaly exists for the target operation event of interest based on the target operation event of interest not being continuously located within a specified period.

9. The method of claim 1, wherein the analyzing constraints comprises specifying a constraint window; the mining of the interested operation events of each group of target resource sharing operation data in the multiple groups of target resource sharing operation data in sequence and the continuous analysis of a plurality of interested operation events existing in the sharing operation data set in sequence to obtain a plurality of target interested operation events comprises the following steps:

in combination with the third distribution expression of the specified constraint window, carrying out interesting operation event mining on resource sharing operation contents corresponding to the specified constraint window in multiple groups of target resource sharing operation data in sequence, and carrying out continuous analysis on a plurality of interesting operation events existing in the resource sharing operation contents corresponding to the specified constraint window in the sharing operation data set in sequence to obtain a plurality of target interesting operation events; wherein the analysis constraint points to a thermal hit condition that corresponds at least in part to the specified constraint window.

10. A big data sharing system, comprising:

a memory for storing an executable computer program, a processor for implementing the method of any one of claims 1-9 when executing the executable computer program stored in the memory.