CN114140152A

CN114140152A - Cloud platform customer management system and method

Info

Publication number: CN114140152A
Application number: CN202111259755.XA
Authority: CN
Inventors: 刘坤
Original assignee: Beijing Yindun Tai'an Network Technology Co ltd
Current assignee: Beijing Yindun Tai'an Network Technology Co ltd
Priority date: 2021-10-28
Filing date: 2021-10-28
Publication date: 2022-03-04

Abstract

The invention provides a cloud platform customer management system and a method, wherein the system comprises: the determining module is used for acquiring order information of the first customer and determining an evaluation standard based on the order information; the evaluation module is used for acquiring a first usage record of the first customer when the time length of the first customer using the cloud platform reaches a preset time length threshold value, and evaluating the first usage record based on an evaluation standard; and the recommending module is used for generating recommended content based on the evaluation result and recommending the recommended content to the first client. According to the cloud platform customer management system and method, the first use record of the first customer can be evaluated, and based on the evaluation result, how to select the payment function on the cloud platform is accurately recommended to the customer, so that the customer experience is improved, and the system and method are very humanized.

Description

Cloud platform customer management system and method

Technical Field

The invention relates to the technical field of cloud platforms, in particular to a cloud platform customer management system and a cloud platform customer management method.

Background

At present, a client of a cloud platform cannot select payment functions (such as a cloud server cpu, a mirror image, a memory and the like) on the cloud platform at first, and selects a short ordering duration, tries the payment functions first, and then selects an applicable function, but the client selects the applicable function by self, so that the accuracy is not high enough, and the humanization is not enough;

therefore, a solution is needed.

Disclosure of Invention

One of the purposes of the invention is to provide a cloud platform customer management system and method, which can evaluate a first usage record of a first customer, accurately recommend to the customer how to select a payment function on a cloud platform based on an evaluation result, improve customer experience, and are very humanized.

The embodiment of the invention provides a cloud platform customer management system, which comprises:

the determining module is used for acquiring order information of a first customer and determining an evaluation standard based on the order information;

the evaluation module is used for acquiring a first usage record of the first customer when the time length of the first customer using the cloud platform reaches a preset time length threshold value, and evaluating the first usage record based on the evaluation standard;

and the recommending module is used for generating recommended content based on the evaluation result and recommending the recommended content to the first client.

Preferably, the determining module performs the following operations:

extracting a plurality of ordering items in the order information, wherein the ordering items comprise: the type and duration of the order;

determining an evaluation standard corresponding to the order type and the order duration together based on a preset order type-order duration-evaluation standard library;

and integrating each evaluation standard to finish the determination.

Preferably, the evaluation module performs the following operations:

extracting a target record corresponding to the subscription type in the first usage record;

evaluating the corresponding target record based on the evaluation standard to obtain an evaluation result;

and integrating the evaluation results to finish evaluation.

Preferably, the recommending module performs the following operations:

extracting a plurality of evaluation items in the evaluation result, wherein the evaluation items comprise: type of assessment and score;

determining recommended content corresponding to the evaluation type and the score together based on a preset evaluation type-score-recommended content library;

and integrating the recommended contents, completing the generation and recommending to the first customer.

Preferably, the cloud platform customer management system further includes:

the compensation module is used for correspondingly compensating the first client based on the evaluation result;

the compensation module performs the following operations:

based on a preset evaluation type-score-compensation mode library, trying to determine a compensation mode corresponding to the evaluation type and the score together;

and compensating the first client based on the compensation mode.

Preferably, the cloud platform customer management system further includes:

the building module is used for building an association area when the first customer accesses the functional module of the cloud platform, associating a suitable second customer for the first customer in the association area, and facilitating the communication between the first customer and the second customer in the association area;

the building module performs the following operations:

acquiring a preset online client set, wherein the online client set comprises: a plurality of third customers;

confirming whether the third customer is accessing the functional module;

if so, taking the corresponding third customer as a fourth customer;

respectively acquiring first attribute information of the first client and second attribute information of the fourth client;

determining at least one key attribute corresponding to the functional module based on a preset functional module-key attribute library;

determining a plurality of first attribute items corresponding to the key attribute in the first attribute information;

determining a plurality of second attribute items corresponding to the key attribute in the second attribute information;

performing feature extraction on the first attribute item to obtain a plurality of first features;

performing feature extraction on the second attribute items to obtain a plurality of second features;

matching the first characteristic with the second characteristic to obtain a first matching degree;

summarizing the first matching degree to obtain a first matching degree sum, and corresponding to the key type;

determining a first correlation judgment value which corresponds to the key attribute, the first matching degree and the common key attribute based on a preset key attribute-matching degree and-correlation judgment value library;

summarizing the first correlation judgment values to obtain a sum of correlation judgment values;

if the correlation judgment value is larger than or equal to a preset first threshold value, taking the corresponding fourth customer as a fifth customer;

acquiring a second use record of the fifth client;

determining at least one key record type corresponding to the functional module based on a preset functional module-key record type library;

determining a plurality of first record items in the first usage record that correspond to the key record type;

determining a plurality of second record items in the second usage record that correspond to the key record type;

respectively establishing a first time axis and a second time axis;

setting the first record item on the first timeline based on a preset first setting rule, and simultaneously setting the second record item on the second timeline;

randomly carrying out feature extraction on one first record item to obtain a plurality of third features;

determining a first position of the first entry for feature extraction on the first time axis;

determining the second record items in a first range preset before and/or after a second position corresponding to the first position on the second time axis, and taking the second record items as third record items;

performing feature extraction on the third record item to obtain a plurality of fourth features;

matching the third characteristic with the fourth characteristic to obtain a second matching degree;

summarizing the second matching degree to obtain a second matching degree sum, and corresponding to the key record type;

determining the key record type, the second matching degree and a first value degree which corresponds to the key record type and the second matching degree together based on a preset key record type-matching degree and-value degree;

if the first price degree is larger than or equal to a preset second threshold value, determining a third position of the corresponding third record item on the second time axis;

setting a selection direction, wherein the selection direction comprises: front or back;

selecting the first record item in the selection direction of the first record item subjected to feature extraction on the first time axis, and taking the first record item as a fourth record item;

determining a fourth position of the fourth entry on the first time axis;

determining the second record items in a preset second range before and/or after a fifth position corresponding to the fourth position on the second time axis, and taking the second record items as fifth record items;

performing feature extraction on the fourth record item to obtain a plurality of fifth features;

performing feature extraction on the fifth record item to obtain a plurality of sixth features;

matching the fifth feature with the sixth feature to obtain a third matching degree;

summarizing the third matching degrees to obtain a third matching degree sum;

determining the key record type, the selection direction, the third matching degree and a second value degree which corresponds to the key record type, the selection direction, the third matching degree and the value degree base based on a preset key record type-selection direction-matching degree and-value degree base;

summarizing the first valence degree and the second valence degree to obtain a second correlation judgment value, and corresponding to the key record type;

summarizing the second correlation judgment values to obtain a second correlation judgment value sum;

and if the second correlation judgment value sum is greater than or equal to a preset third threshold value, taking the corresponding fifth customer as the second customer.

Preferably, the cloud platform customer management system further includes:

the monitoring module is used for monitoring whether the first client is in compliance when accessing the cloud platform, and if not, corresponding measures are executed;

the monitoring module performs the following operations:

periodically acquiring a plurality of first action items newly generated by the first client;

establishing a third time axis;

setting the first behavior item on the third timeline based on a preset second setting rule;

performing feature extraction on the first behavior items one by one from a starting point to an end point of the third time axis to obtain a plurality of seventh features;

acquiring a preset trigger feature pair library, and randomly selecting a trigger feature pair from the trigger feature pair library, wherein the trigger feature pair comprises: the first trigger characteristic and the at least one second trigger characteristic correspond to each other;

matching the seventh feature with the first trigger feature;

if the matching is in accordance with the first trigger characteristic, determining the number of the second trigger characteristics corresponding to the first trigger characteristics in accordance with the matching;

selecting the number of first behavior items after the first behavior item for feature extraction on the third timeline as a second behavior item;

performing feature extraction on the second behavior item to obtain a plurality of eighth features;

matching the eighth feature with the second trigger feature corresponding to the first trigger feature matched with the eighth feature;

if the matching is matched, taking the matched second trigger feature as a target feature, and meanwhile, determining a sixth position of the second behavior item corresponding to the matched eighth feature on the third time axis;

inquiring a preset trigger characteristic-acquisition direction-selection range-malicious behavior library, and determining an acquisition direction, a selection range and a malicious behavior corresponding to the target characteristic;

selecting the first behavior item in the selection range in the acquisition direction of the sixth position on the third time axis, and taking the first behavior item as a third behavior item;

if the third behavior item contains the malicious behavior, determining that the first client is unqualified in access;

determining measures corresponding to the malicious behaviors based on a preset malicious behavior-measure library;

the measures are performed.

The cloud platform customer management method provided by the embodiment of the invention comprises the following steps:

step S1: acquiring order information of a first customer, and determining an evaluation standard based on the order information;

step S2: when the time length of the first customer using the cloud platform reaches a preset time length threshold value, acquiring a first usage record of the first customer, and evaluating the first usage record based on the evaluation standard;

step S3: and generating recommended content based on the evaluation result, and recommending to the first client.

Preferably, in step S1, the determining an evaluation criterion based on the order information includes:

and integrating each evaluation standard to finish the determination.

Preferably, the evaluating the first usage record based on the evaluation criteria includes:

and integrating the evaluation results to finish evaluation.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

fig. 1 is a schematic diagram of a cloud platform customer management system according to an embodiment of the present invention;

fig. 2 is a flowchart of a cloud platform customer management method according to an embodiment of the present invention;

fig. 3 is a flowchart of another cloud platform customer management method according to an embodiment of the present invention.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

An embodiment of the present invention provides a cloud platform customer management system, as shown in fig. 1, including:

the system comprises a determining module 1, a judging module and a judging module, wherein the determining module is used for acquiring order information of a first customer and determining an evaluation standard based on the order information;

the evaluation module 2 is configured to obtain a first usage record of the first customer when a duration that the first customer uses the cloud platform reaches a preset duration threshold, and evaluate the first usage record based on the evaluation criterion;

and the recommending module 3 is used for generating recommended content based on the evaluation result and recommending the recommended content to the first client.

The working principle and the beneficial effects of the technical scheme are as follows:

acquiring order information of a first customer (for example, ordering 8-core CPUs for 1 month); determining an evaluation criterion (for example, whether the computing power of the 8-core CPU can meet the use requirement of the first customer) based on the order information; when the using time of the first customer using the cloud platform reaches a preset time threshold (for example, 20 days), obtaining the using record of the first customer, and evaluating the using record based on an evaluation standard; generating recommended content (recommending upgrading the CPU into 12 cores) based on the evaluation result (for example: the computing power of the 8-core CPU cannot meet the use requirement of the first client);

the embodiment of the invention can evaluate the first use record of the first customer, accurately recommend to the customer how to select the payment function on the cloud platform based on the evaluation result, improve the customer experience and is very humanized.

The embodiment of the invention provides a cloud platform customer management system, wherein a determining module 1 executes the following operations:

and integrating each evaluation standard to finish the determination.

the preset order type-order duration-evaluation standard library specifically comprises the following steps: a database containing evaluation criteria corresponding to different subscription types and different subscription durations, for example: the ordering type is 8-core CPU ordering, the ordering time is 15 days, and the corresponding evaluation standard is whether the average level of the 8-core CPU satisfying the business processing of the client within 15 days reaches the standard or not;

and determining the evaluation standard based on the order type-order duration-evaluation standard library, and integrating to finish the determination.

The embodiment of the invention provides a cloud platform customer management system, wherein an evaluation module 2 executes the following operations:

and integrating the evaluation results to finish evaluation.

for example: the order type is 8-core CPU order, and a target record corresponding to the order type in the first usage record is extracted, namely the load condition (such as overload) of the 8-core CPU when a customer performs service processing, and the like;

and evaluating the target record based on the evaluation standard, namely finishing the evaluation.

The embodiment of the invention provides a cloud platform customer management system, wherein a recommending module 3 executes the following operations:

the preset evaluation type-score-recommendation content library specifically comprises the following steps: the recommended contents corresponding to different evaluation types and different scores are contained, for example: the evaluation type is that the CPU can not meet the requirement, the score is 10 (the lower the score is, the more serious the condition that the CPU can not meet the requirement is), and the corresponding recommended content is that the CPU is recommended to be upgraded to the 12 cores.

The embodiment of the invention provides a cloud platform customer management system, which further comprises:

the compensation module performs the following operations:

and compensating the first client based on the compensation mode.

the preset evaluation type-scoring-compensation mode library is specifically as follows: compensation modes corresponding to different evaluation types and different scores are included, for example: the evaluation type is that the CPU can not meet the requirement, the evaluation is 10, and the corresponding compensation mode is that the CPU with 12 cores is tried out freely.

the building module performs the following operations:

confirming whether the third customer is accessing the functional module;

if so, taking the corresponding third customer as a fourth customer;

acquiring a second use record of the fifth client;

respectively establishing a first time axis and a second time axis;

determining a fourth position of the fourth entry on the first time axis;

summarizing the third matching degrees to obtain a third matching degree sum;

the preset online client set specifically comprises: a set comprising a plurality of online customers (third customers); the preset functional module-key attribute library specifically comprises: a database containing key attributes corresponding to different functional modules, for example: the functional module is a storage functional area (providing a cloud storage function), and the key attributes are the use situation of the client (used for storing department information, the number of department people is 10) and the like; the preset key attribute-matching degree and-association judgment value library specifically comprises the following steps: the method includes different key attributes, different matching degrees and corresponding association judgment values, and the greater the matching degree sum is, the greater the association judgment value is, for example: the key attribute is the use situation of the client, the first matching degree sum is 766, and the corresponding association judgment value is 87; the preset first threshold specifically includes: for example, 820; the preset functional module-key record type library is specifically as follows: a database containing key record types corresponding to different functional modules, for example: the functional module is a storage functional area, and the key record type is daily upload volume (the volume of data uploaded to the cloud storage area by a client every day); the preset first setting rule is specifically as follows: the generation time of the record items corresponds to the time nodes on the time axis one by one, and setting is carried out; the preset first range specifically is: range values on the time axis, for example: 0.5 day; the preset key record types-matching degree and-value degree are specifically as follows: a database containing different key record types and different matching degrees and corresponding first value degrees, the larger the matching degree sum, the greater the value degree, for example: the key record type is daily upload amount, the second matching degree sum is 50, and the first value degree is 66; the preset second threshold specifically is: for example, 750; the preset second range is specifically as follows: range values on the time axis, for example: 0.6 day; the preset key record type-selecting direction-matching degree and-value degree library is specifically as follows: the method comprises different key record types, different selection directions, different third matching degrees and corresponding value degrees, and the value degrees are also influenced by the different selection directions, for example: the more the usage record is the daily upload amount, the more the first client and the second client are matched with the newer (after the selection direction is back), which shows that the first client and the second client have almost the same usage condition and higher value degree, while the previous daily upload amount has lower value degree; the preset third threshold specifically is: for example, 800;

when the cloud platform is actually used, even if the selection of the payment function is recommended to the customer, since the customer wants to save cost, how to use the function more reasonably is more important, and since the influence of the customer is involved, the platform cannot acquire the data of the customer, but the customer can communicate with each other, for example: the first client and the second client are financial supervisors of companies with almost the same scale, and the invoices, payroll tables and the like are stored on the cloud platform, but how to compress the invoices and reduce the size of the invoices before uploading on the premise of ensuring data information is reduced as much as possible, the first client and the second client communicate with each other, and the storage function of the cloud platform is used reasonably and better; therefore, the application constructs an association area (a function submodule can be displayed as a floating window when the client accesses the function module), and associates a second client in the association area for the first client, and the first client and the second client can communicate; however, how to ensure the suitability of the second customer and the first customer needs to be further solved; the method matches the attribute information (such as use purpose, company scale and the like) of the two clients, matches key attributes based on a functional module-key attribute library, improves matching efficiency and completes primary screening; then, the use records of the two clients are matched, and the key records are matched based on the functional module-key record type library, so that the matching efficiency is improved; meanwhile, based on the first time axis and the second time axis, the first valence degree and the second valence degree are skillfully determined, the selection directions are different, the sizes of the second valence degrees are also different, the setting is reasonable, and the association efficiency is improved.

the monitoring module performs the following operations:

establishing a third time axis;

matching the seventh feature with the first trigger feature;

the measures are performed.

the preset second setting rule is specifically as follows: setting the generation time of the behavior item and the time nodes on the time axis in a one-to-one correspondence manner; the preset triggering characteristic pair library specifically comprises the following steps: a database containing a plurality of trigger feature pairs, the trigger feature pairs comprising: a first trigger feature and at least one second trigger feature corresponding to the first trigger feature; the preset trigger characteristics-acquisition direction-selection range-malicious behavior library specifically comprises the following steps: the method comprises the steps of obtaining directions, selecting ranges and malicious behaviors corresponding to different second trigger characteristics;

for example: dividing a certain malicious behavior into a step 1, a step 2 and a step 3 according to a time sequence (such as invasion to a background, a chat area, 35881;, cursing and the like), wherein a first trigger characteristic can be the characteristic of the step 1, a second trigger characteristic can be the characteristic of the step 2, the acquisition direction is selected backwards on a third time axis, the selection range can be determined based on the average required time between the step 2 and the step 3, and if the selected third line is an item comprising the step 3, the malicious behavior is cured; the first trigger feature may also be the feature of step 2, and then the second trigger feature is the feature of step 3, the obtaining direction is selected forward on the third time axis, the selecting range may be determined based on the average required time between step 1 and step 3, and if the selected third behavior item includes step 1, the malicious behavior is seated;

according to the embodiment of the invention, the extracted seventh feature is matched with the first trigger feature based on the trigger feature pair library, and the matching is in accordance with the condition that the first client has the possibility of generating malicious behaviors, and the process is continued, otherwise, the process is not continued, so that the efficiency of monitoring the malicious behaviors is improved; meanwhile, whether the malicious behavior is actually seated or not is further determined by utilizing the second trigger characteristic based on the trigger characteristic-acquisition direction-selection range-malicious behavior library, the acquisition direction and the selection range can be directly determined, and the efficiency of determining the actual seating is improved.

the removing module is used for acquiring a preset malicious record library, determining whether to remove the first client or not based on the malicious record library, and if so, removing the first client;

the culling module performs the following operations:

determining at least one sixth record item corresponding to the first customer from the malicious record library;

extracting a first influence value and a first dynamic influence value in the sixth record item;

calculating a first decision index based on the first impact value and the first dynamic impact value, the calculation formula being as follows:

wherein σ₁Is the first determination index, α_1，tIs the first influence value, beta, in the tth sixth entry corresponding to the first client_1，tThe first dynamic influence value is a first dynamic influence value in the tth sixth record item corresponding to the first customer, and O is the total number of the sixth record items corresponding to the first customer;

determining a plurality of associated clients corresponding to the first client based on a preset client-associated client library;

acquiring an incidence relation between the associated client and the client;

analyzing the incidence relation to obtain a first relation value;

acquiring a preset association prediction model, inputting the association relation into the association prediction model, and acquiring a second relation value;

determining at least one seventh record item corresponding to the associated client from the malicious record library;

extracting a second influence value and a second dynamic influence value in the seventh record item;

based on the first relation value, the second influence value and the second dynamic influence value, and based on a second determination index, a calculation formula is as follows:

wherein σ₂Is the second judgment index, A_γA first relation value, B, corresponding to a gamma-th associated customer corresponding to said first customer_γA second relation value corresponding to a gamma-th associated customer corresponding to the first customer, n is the total number of associated customers corresponding to the first customer, alpha_2，γ，tA second influence value, β, in the tth seventh entry for the jth associated client for the first client_2，γ，tA second dynamic influence value,/, in a tth seventh entry corresponding to a gamma-th associated customer corresponding to the first customer_γA total number of seventh entries corresponding to a γ th associated customer corresponding to the first customer;

and if the first judgment index is greater than or equal to a preset first judgment index threshold value and/or the second judgment index is greater than or equal to a preset second judgment index threshold value, rejecting the corresponding first customer.

the preset malicious record library specifically comprises the following steps: a database containing historical malicious records (e.g., malicious intrusion, chat \35881;, curse, etc.) of different customers; the preset client-associated client library specifically comprises: the database comprises related clients corresponding to different clients, and the associated clients can be set when the clients register; the preset associated prediction model specifically comprises: the model is generated after a large number of records of the incidence relation tightness degree are manually predicted by using a machine learning algorithm, the model can predict the future tightness degree of the incidence relation and obtain a second relation value, and the larger the second relation value is, the higher the tightness degree is; the preset first judgment index threshold specifically comprises: for example, 75; the preset second determination index threshold specifically comprises: for example, 80;

when a first client generates a malicious record, the influence generated at the time of the generated malicious record is immediately determined manually to obtain a first influence value, meanwhile, the influence generated later by the malicious record is continuously determined, and the influence is continuously accumulated to obtain a first dynamic influence value; the second influence value and the second dynamic influence value are the same;

calculating a first judgment index, wherein when the first judgment index is greater than or equal to a first judgment index threshold value, the self operation of the first client is not good; in the corresponding formula, α_1，tThe larger, beta_1，tThe larger the first judgment index is, β_1，t-α_1，tThe larger the first judgment index is, the larger the later influence is; calculating a second judgment index, wherein when the second judgment index is greater than or equal to a second judgment index threshold value, the relevant customer of the first customer is not good; in the corresponding formula, α_2，γ，tThe larger, beta_2，γ，tThe larger the second determination index is, the larger β_2，γ，t-α_2，γ，tThe larger the value is, the larger the influence of the later period is, the larger the second judgment index is, and A_γ+B_γThe larger, B_γ-A_γThe larger the relationship is, the tighter the relationship is, the larger the guarantee relationship is, the larger the second determination index is, and the liftable range of the second influence value and the second dynamic influence value is reduced; removing the first client, and not allowing the first client to continue using the cloud platform;

a guarantee relationship is formed between the first client and the associated clients, namely one client generates malicious records, the other client is also influenced, the cost of generating the malicious records is increased, the generation of malicious behaviors is inhibited to a certain degree, the use stability of the platform is improved, and the order of the platform is maintained.

An embodiment of the present invention provides a cloud platform customer management method, as shown in fig. 2, including:

An embodiment of the present invention provides a cloud platform customer management method, as shown in fig. 3, in step S1, determining an evaluation criterion based on the order information includes:

step S101: extracting a plurality of ordering items in the order information, wherein the ordering items comprise: the type and duration of the order;

step S102: determining an evaluation standard corresponding to the order type and the order duration together based on a preset order type-order duration-evaluation standard library;

step S103: and integrating each evaluation standard to finish the determination.

The embodiment of the present invention provides a cloud platform customer management method, in step S2, based on the evaluation criterion, the evaluating the first usage record includes:

and integrating the evaluation results to finish evaluation.

The embodiment of the present invention provides a cloud platform customer management method, in step S3, generating recommendation content based on an evaluation result, and recommending to the first customer, where the method includes:

The embodiment of the invention provides a cloud platform customer management method, which further comprises the following steps:

based on the evaluation result, correspondingly compensating the first customer;

wherein, based on the evaluation result, correspondingly compensating the first customer comprises:

and compensating the first client based on the compensation mode.

when the first customer accesses the functional module of the cloud platform, constructing an association area, and associating a suitable second customer for the first customer in the association area, so that the first customer and the second customer can conveniently communicate in the association area;

wherein associating a suitable second customer for the first customer in the association zone comprises:

confirming whether the third customer is accessing the functional module;

if so, taking the corresponding third customer as a fourth customer;

acquiring a second use record of the fifth client;

respectively establishing a first time axis and a second time axis;

determining a fourth position of the fourth entry on the first time axis;

summarizing the third matching degrees to obtain a third matching degree sum;

monitoring whether the first client is in compliance when accessing the cloud platform, and if not, executing corresponding measures;

monitoring whether the first client is in compliance when accessing the cloud platform, and if not, executing corresponding measures, wherein the steps comprise:

establishing a third time axis;

matching the seventh feature with the first trigger feature;

the measures are performed.

acquiring a preset malicious record library, determining whether to reject the first client or not based on the malicious record library, and if so, rejecting;

determining whether to reject the first client based on the malicious record library, and if so, rejecting the first client, wherein the removing comprises the following steps:

acquiring an incidence relation between the associated client and the client;

analyzing the incidence relation to obtain a first relation value;

wherein σ₂Is the second judgment index, A_γA first relation value, B, corresponding to a gamma-th associated customer corresponding to said first customer_γIs the firstA second relation value corresponding to a gamma associated customer corresponding to a customer, n is the total number of associated customers corresponding to the first customer, alpha_2，γ，tA second influence value, β, in the tth seventh entry for the jth associated client for the first client_2，γ，tA second dynamic influence value,/, in a tth seventh entry corresponding to a gamma-th associated customer corresponding to the first customer_γA total number of seventh entries corresponding to a γ th associated customer corresponding to the first customer;

calculating a first judgment index, wherein when the first judgment index is greater than or equal to a first judgment index threshold value, the self operation of the first client is not good; in the corresponding formula, α_1，tThe larger the size of the tube is,β_1，tthe larger the first judgment index is, β_1，t-α_1，tThe larger the first judgment index is, the larger the later influence is; calculating a second judgment index, wherein when the second judgment index is greater than or equal to a second judgment index threshold value, the relevant customer of the first customer is not good; in the corresponding formula, α_2，γ，tThe larger, beta_2，γ，tThe larger the second determination index is, the larger β_2，γ，t-α_2，γ，tThe larger the value is, the larger the influence of the later period is, the larger the second judgment index is, and A_γ+B_γThe larger, B_γ-A_γThe larger the relationship is, the tighter the relationship is, the larger the guarantee relationship is, the larger the second determination index is, and the liftable range of the second influence value and the second dynamic influence value is reduced; removing the first client, and not allowing the first client to continue using the cloud platform;

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims

1. A cloud platform customer management system, comprising:

2. The cloud platform customer management system of claim 1, wherein the determination module performs the following operations:

and integrating each evaluation standard to finish the determination.

3. The cloud platform customer management system of claim 1, wherein the evaluation module performs the following operations:

and integrating the evaluation results to finish evaluation.

4. The cloud platform customer management system of claim 1, wherein the recommendation module performs the following operations:

5. The cloud platform customer management system of claim 1, further comprising:

the compensation module performs the following operations:

and compensating the first client based on the compensation mode.

6. The cloud platform customer management system of claim 1, further comprising:

the building module performs the following operations:

confirming whether the third customer is accessing the functional module;

if so, taking the corresponding third customer as a fourth customer;

acquiring a second use record of the fifth client;

respectively establishing a first time axis and a second time axis;

determining a fourth position of the fourth entry on the first time axis;

summarizing the third matching degrees to obtain a third matching degree sum;

7. The cloud platform customer management system of claim 1, further comprising:

the monitoring module performs the following operations:

establishing a third time axis;

matching the seventh feature with the first trigger feature;

the measures are performed.

8. A cloud platform customer management method is characterized by comprising the following steps:

9. The cloud platform customer management method according to claim 8, wherein in the step S1, determining an evaluation criterion based on the order information includes:

and integrating each evaluation standard to finish the determination.

10. The cloud platform customer management method according to claim 8, wherein in the step S2, evaluating the first usage record based on the evaluation criterion includes:

and integrating the evaluation results to finish evaluation.