CN111581389B

CN111581389B - Regional data analysis method and device and cloud server

Info

Publication number: CN111581389B
Application number: CN202010416262.1A
Authority: CN
Inventors: 秦佩; 倪向东; 胡建敏; 费红琳; 胡幼华
Original assignee: Guangzhou Doctor Information Technology Research Institute Co ltd
Current assignee: Guangzhou Doctor Information Technology Research Institute Co ltd
Priority date: 2020-05-17
Filing date: 2020-05-17
Publication date: 2020-12-29
Anticipated expiration: 2040-05-17
Also published as: CN111581389A

Abstract

The invention relates to the technical field of data analysis, in particular to a regional data analysis method, a device and a cloud server, which can acquire first address information and first information reporting information of each group of enterprise information, create a plurality of region division strategies and determine second address information and second information reporting time corresponding to each region division strategy. The method and the device determine a plurality of policy information demand categories corresponding to the region division strategy set according to the second address information corresponding to each region division strategy and the reporting time of the second information, and realize the determination of the weighted sum value corresponding to each region division strategy set. The region division strategy set corresponding to the maximum weighted sum value is used as a target region division strategy set, multiple groups of enterprise information are subjected to region division according to the target region division strategy set to obtain multiple division regions, then regional policy information release is achieved, corresponding release of policy information can be carried out aiming at enterprise servers in different regions, and accurate release of policy information is guaranteed.

Description

Regional data analysis method and device and cloud server

Technical Field

The invention relates to the technical field of data analysis, in particular to a regional data analysis method and device and a cloud server.

Background

With the development of science and technology, the way for enterprises to obtain relevant policy information of project declaration becomes more and more convenient.

At the present stage, the cloud server can put the policy information into the enterprise server, and an enterprise does not need to actively search the policy information. However, the existing cloud server does not consider the region difference of the enterprise server when releasing policy information to the enterprise server, so that the policy information received by the enterprise servers in different regions is not different, which affects the releasing accuracy of the policy information.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, an object of the present invention is to provide a method and an apparatus for analyzing regional data, and a cloud server.

In a first aspect of the embodiments of the present invention, a regional data analysis method is provided, which is applied to a cloud server, and the method includes:

acquiring a plurality of groups of enterprise information from a pre-stored enterprise information data pool, and acquiring first address information and first information reporting time of each group of enterprise information;

establishing a plurality of region division strategy sets by utilizing the first address information and the first information reporting time of each group of enterprise information; aiming at each created region division strategy set, a region address boundary setting strategy exists in the region division strategy set, and the longitude and latitude distance calculated according to the first address information of the first enterprise information in the region division strategy set and the first address information of the second enterprise information corresponding to the region address boundary setting strategy is smaller than the set distance; the first information reporting moments of all enterprise information in the regional division strategy set are arranged according to the sequence of reporting time;

setting first address information and first information reporting time of second enterprise information corresponding to a strategy or first address information and first information reporting time of the last group of enterprise information in the region division strategy set by aiming at each region division strategy set as second address information and second information reporting time of the region division strategy set; the last group of enterprise information is obtained according to a sorting sequence obtained by sorting the first information reporting time of the enterprise information, and the first information reporting time of the last group of enterprise information is positioned at the last of the sorting sequence;

determining a plurality of policy information demand categories corresponding to each region division strategy set by using the second information reporting time and the second address information of each region division strategy set; determining a weighted sum value of information delivery matching degrees of each divided region in each region division strategy set according to a plurality of corresponding policy information demand categories of each region division strategy set and pre-stored policy information delivery strategies;

taking the region division strategy set corresponding to the maximum weighted sum value as a target region division strategy set; carrying out regional division on multiple groups of enterprise information according to the target regional division strategy set and the first address information of each group of enterprise information to obtain multiple divided regions; and releasing corresponding policy information to an enterprise server corresponding to the enterprise information included in each divided region according to the policy information releasing strategy.

In an alternative embodiment, the creating multiple regional division policy sets by using the first address information and the first information reporting time of each group of enterprise information includes:

acquiring information acquisition records of interaction between the enterprise server corresponding to each group of enterprise information and the cloud server according to the first address information and the first information reporting time of each group of enterprise information;

when the cloud server is in an information updating state, analyzing the service request in the information acquisition record corresponding to the enterprise server corresponding to each group of acquired enterprise information to obtain an address analysis result; carrying out consistency matching on the home address and the address resolution result which are set in the metadata management form corresponding to the cloud server; if the matching is successful, judging the information acquisition record corresponding to the enterprise server corresponding to each group of enterprise information as an effective record; if the matching fails, judging the information acquisition record corresponding to the enterprise server corresponding to each group of enterprise information as an invalid record;

when the cloud server is not in an information updating state, detecting whether an attribution address in a metadata management form corresponding to the cloud server is set; when the home address in the metadata management form corresponding to the cloud server is set, aiming at each information acquisition record, determining the activation address of the information acquisition record according to the message flow direction in the information acquisition record; when the home address in the metadata management form corresponding to the cloud server is not set, determining an activation address of each information acquisition record according to the protocol address of the enterprise server corresponding to the information acquisition record for each information acquisition record; for each activation address, whether the activation address is matched with a pre-stored registration address of the enterprise server corresponding to the activation address or not is judged, and if the matching is successful, the information acquisition record corresponding to the activation address is determined to be an effective record; if the matching is unsuccessful, determining the information acquisition record corresponding to the activation address as an invalid record;

determining the enterprise information recorded as the effective record as target enterprise information;

determining enterprise information characteristic vectors of each group of target enterprise information aiming at each group of target enterprise information; acquiring a feature extraction logic table of each group of enterprise feature vectors, and adding each feature extraction logic table to a reference feature list of each group of target enterprise information to obtain a feature information update table corresponding to each group of target enterprise information; adjusting each group of enterprise feature vectors according to each feature information update table to obtain a first enterprise feature vector; establishing a plurality of feature vector sequence tables for sequencing the first enterprise feature vectors by taking each group of first enterprise feature vectors as reference enterprise feature vectors; each feature vector sequence table is sorted according to the size of a feature value in each reference enterprise feature vector;

and classifying the equipment addresses of the enterprise server corresponding to each group of target enterprise information according to each group of feature vector sequence list to obtain multiple classes of equipment addresses, and generating a region division strategy set corresponding to the feature vector sequence list according to the multiple classes of equipment addresses.

In an alternative embodiment, the determining, by using the second information reporting time and the second address information of each region partition policy set, multiple policy information requirement categories corresponding to the region partition policy set includes:

acquiring a label of longitude and latitude information corresponding to second address information in each regional division strategy set and an association relation between the second address information and preset policy information release frequency according to the second information reporting time of each regional division strategy set;

determining target longitude and latitude information corresponding to the association relation in each region division strategy set according to the label and the association relation; determining an area identifier corresponding to a policy delivery area where the target longitude and latitude information is located;

when the region identifier is determined to meet policy information release conditions, counting historical policy information corresponding to second address information in each region division strategy set according to the region identifier; determining a plurality of keywords in historical policy information;

under the condition that each region division strategy set is determined to contain the information rejection behavior according to the historical policy information, determining the Hamming distance between each keyword of each region division strategy set under the information rejection behavior and each keyword of each region division strategy set under the information rejection behavior according to the keyword of each region division strategy set under the information rejection behavior and the word vector thereof, and transferring the keywords of each region division strategy set under the information reception behavior and the keywords under the information rejection behavior, wherein the Hamming distance between each keyword of the region division strategy sets under the information reception behavior and the keywords under the information rejection behavior is smaller than the preset distance, to the corresponding information rejection behavior;

under the condition that a plurality of keywords are contained in the current information receiving behavior of each region division strategy set, determining the Hamming distance between the keywords of each region division strategy set in the current information receiving behavior according to the keywords of each region division strategy set in the information rejecting behavior and word vectors thereof, and classifying the keywords in the current information receiving behavior according to the Hamming distance between the keywords;

and setting a keyword signature for each category of keywords obtained by classification according to the keywords of each region division strategy set under the information rejection behavior and the word vectors thereof, and determining a plurality of policy information demand categories corresponding to each region division strategy set according to the keyword signatures.

In an alternative embodiment, the issuing, according to the policy information issuing policy, the corresponding policy information to the enterprise server corresponding to the enterprise information included in each of the divided regions includes:

releasing corresponding policy information to an enterprise server corresponding to the enterprise information included in each divided region at regular time according to the policy information releasing strategy;

acquiring the receiving frequency of each enterprise server for receiving the current policy information and the storage frequency for storing the current policy information;

according to the receiving frequency and the storage frequency, sending a throughput adjusting parameter to each enterprise server before delivering corresponding policy information to each enterprise server next time so that each enterprise server adjusts the receiving frequency and the storage frequency of the enterprise server according to the throughput adjusting parameter;

after receiving prompt information fed back by each enterprise server and used for finishing the adjustment of the receiving frequency and the storage frequency of the enterprise server, continuously releasing corresponding policy information to each enterprise server;

wherein, enabling each enterprise server to adjust the receiving frequency and the storage frequency of the enterprise server according to the throughput adjustment parameters comprises:

judging whether the receiving frequency of each enterprise server exceeds a first set frequency and judging whether the storage frequency of each enterprise server is lower than a second set frequency;

when the receiving frequency of each enterprise server exceeds the first set frequency and the storage frequency of each enterprise server is lower than the second set frequency, reducing the receiving frequency and the storage frequency of each enterprise server according to the throughput adjustment parameter;

when the receiving frequency of each enterprise server does not exceed the first set frequency and the storage frequency of each enterprise server is not lower than the second set frequency, increasing the receiving frequency and the storage frequency of each enterprise server according to the throughput adjustment parameter;

when the receiving frequency of each enterprise server exceeds the first set frequency and the storage frequency of each enterprise server is not lower than a second set frequency, reducing the receiving frequency of each enterprise server and increasing the storage frequency of each enterprise server according to the throughput adjusting parameter;

and when the receiving frequency of each enterprise server does not exceed the first set frequency and the storage frequency of each enterprise server is not lower than a second set frequency, increasing the receiving frequency of each enterprise server and reducing the storage frequency of each enterprise server according to the throughput adjustment parameter.

In a second aspect of the embodiments of the present invention, there is provided a regional data analysis apparatus, which is applied to a cloud server, and includes:

the acquisition module is used for acquiring a plurality of groups of enterprise information from a pre-stored enterprise information data pool, and acquiring first address information and first information reporting time of each group of enterprise information;

the system comprises a creating module, a judging module and a judging module, wherein the creating module is used for creating a plurality of region division strategy sets by utilizing first address information and first information reporting time of each group of enterprise information; aiming at each created region division strategy set, a region address boundary setting strategy exists in the region division strategy set, and the longitude and latitude distance calculated according to the first address information of the first enterprise information in the region division strategy set and the first address information of the second enterprise information corresponding to the region address boundary setting strategy is smaller than the set distance; the first information reporting moments of all enterprise information in the regional division strategy set are arranged according to the sequence of reporting time;

a first determining module, configured to set, for each region partition policy set, first address information and first information reporting time of second enterprise information corresponding to a policy for a region address boundary of the region partition policy set, or first address information and first information reporting time of a last group of enterprise information in the region partition policy set, as second address information and second information reporting time of the region partition policy set; the last group of enterprise information is obtained according to a sorting sequence obtained by sorting the first information reporting time of the enterprise information, and the first information reporting time of the last group of enterprise information is positioned at the last of the sorting sequence;

the second determining module is used for determining a plurality of policy information demand categories corresponding to each region division strategy set by using the second information reporting time and the second address information of each region division strategy set; determining a weighted sum value of information delivery matching degrees of each divided region in each region division strategy set according to a plurality of corresponding policy information demand categories of each region division strategy set and pre-stored policy information delivery strategies;

the releasing module is used for taking the region division strategy set corresponding to the maximum weighted sum value as a target region division strategy set; carrying out regional division on multiple groups of enterprise information according to the target regional division strategy set and the first address information of each group of enterprise information to obtain multiple divided regions; and releasing corresponding policy information to an enterprise server corresponding to the enterprise information included in each divided region according to the policy information releasing strategy.

In an alternative embodiment, the creation module is to:

In an alternative embodiment, the second determining module is configured to:

In an alternative embodiment, the delivery module is configured to:

The embodiment of the invention also provides a cloud server, which comprises a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory so as to execute the regional data analysis method.

An embodiment of the present invention further provides a readable storage medium, on which a program is stored, and the program, when executed by a processor, implements the above-mentioned regional data analysis method.

The regional data analysis method, the regional data analysis device and the cloud server provided by the embodiment of the invention can acquire the first address information and the first information reporting information of each group of enterprise information, create a plurality of regional division strategies and further determine the second address information and the second information reporting time corresponding to each regional division strategy. Furthermore, a plurality of policy information requirement categories corresponding to the region division strategy set are determined according to the second address information corresponding to each region division strategy and the reporting time of the second information, and further determination of the weighted sum value corresponding to each region division strategy set is achieved. Since the weighted sum is used for representing the matching degree of the information delivery, the region division strategy set corresponding to the maximum weighted sum is used as a target region division strategy set, multiple groups of enterprise information are subjected to region division according to the target region division strategy set to obtain multiple divided regions, and then regional policy information delivery is realized, so that corresponding delivery of policy information can be performed for enterprise servers in different regions, and the delivery accuracy of policy information is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a flowchart of a localized data analysis method according to an embodiment of the present invention.

Fig. 2 is a functional block diagram of a localized data analysis device according to an embodiment of the present invention.

Fig. 3 is a schematic block diagram of a cloud server according to an embodiment of the present invention.

Icon:

200-a regionalized data analysis device; 201-an acquisition module; 202-a creation module; 203-a first determination module; 204-a second determination module; 205-a delivery module;

300-a cloud server; 301-a processor; 302-a memory; 303-bus.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

Fig. 1 is a flowchart of a regional data analysis method according to an embodiment of the present invention, applied to a cloud server, where the method may include the following steps:

and step S21, acquiring multiple groups of enterprise information from a pre-stored enterprise information data pool, and acquiring first address information and first information reporting time of each group of enterprise information.

And step S22, creating a plurality of regional division strategy sets by using the first address information and the first information reporting time of each group of enterprise information.

Step S23, regarding each area division policy set, using the first address information and the first information reporting time of the second enterprise information corresponding to the area address boundary setting policy of the area division policy set or the first address information and the first information reporting time of the last group of enterprise information in the area division policy set as the second address information and the second information reporting time of the area division policy set.

Step S24, determining a plurality of policy information requirement categories corresponding to each region division strategy set by using the second information reporting time and the second address information of each region division strategy set; and determining the weighted sum value of the information delivery matching degree of each divided region in each region division strategy set according to the corresponding multiple policy information demand categories of each region division strategy set and the pre-stored policy information delivery strategies.

Step S25, taking the region division strategy set corresponding to the maximum weighted sum value as the target region division strategy set; carrying out regional division on multiple groups of enterprise information according to the target regional division strategy set and the first address information of each group of enterprise information to obtain multiple divided regions; and releasing corresponding policy information to an enterprise server corresponding to the enterprise information included in each divided region according to the policy information releasing strategy.

In step S22, for each created regional division policy set, a regional address boundary setting policy exists in the regional division policy set, and a longitude and latitude distance calculated according to first address information of first enterprise information in the regional division policy set and first address information of second enterprise information corresponding to the regional address boundary setting policy is smaller than a set distance; and the first information reporting moments of all the enterprise information in the regional division strategy set are arranged according to the sequence of the reporting time.

In step S24, the last group of enterprise information is obtained according to a sorting sequence obtained by sorting the first information reporting time of the enterprise information, and the first information reporting time of the last group of enterprise information is located at the end of the sorting sequence.

It can be understood that, through steps S21 to S24, the first address information and the first information reporting information of each group of enterprise information can be obtained, and multiple area division policies are created, so that the second address information and the second information reporting time corresponding to each area division policy are determined. Furthermore, a plurality of policy information requirement categories corresponding to the region division strategy set are determined according to the second address information corresponding to each region division strategy and the reporting time of the second information, and further determination of the weighted sum value corresponding to each region division strategy set is achieved. Since the weighted sum is used for representing the matching degree of the information delivery, the region division strategy set corresponding to the maximum weighted sum is used as a target region division strategy set, multiple groups of enterprise information are subjected to region division according to the target region division strategy set to obtain multiple divided regions, and then regional policy information delivery is realized, so that corresponding delivery of policy information can be performed for enterprise servers in different regions, and the delivery accuracy of policy information is improved.

When creating the regional division policy sets, it is necessary to comprehensively analyze the enterprise information from multiple dimensions (for example, an enterprise address, an enterprise type, an enterprise research progress, an enterprise product market share, an enterprise product release period, and the like), and to this end, in step S22, the creating multiple regional division policy sets by using the first address information and the first information reporting time of each group of enterprise information may specifically include the following:

and step S221, acquiring information acquisition records of interaction between the enterprise server corresponding to each group of enterprise information and the cloud server according to the first address information and the first information reporting time of each group of enterprise information.

Step S222, when the cloud server is in an information updating state, analyzing the service request in the information acquisition record corresponding to the enterprise server corresponding to each group of acquired enterprise information to obtain an address analysis result; carrying out consistency matching on the home address and the address resolution result which are set in the metadata management form corresponding to the cloud server; if the matching is successful, judging the information acquisition record corresponding to the enterprise server corresponding to each group of enterprise information as an effective record; and if the matching fails, judging the information acquisition record corresponding to the enterprise server corresponding to each group of enterprise information as an invalid record.

Step S223, when the cloud server is not in the information updating state, detecting whether an home address in a metadata management form corresponding to the cloud server is set; when the home address in the metadata management form corresponding to the cloud server is set, aiming at each information acquisition record, determining the activation address of the information acquisition record according to the message flow direction in the information acquisition record; when the home address in the metadata management form corresponding to the cloud server is not set, determining an activation address of each information acquisition record according to the protocol address of the enterprise server corresponding to the information acquisition record for each information acquisition record; for each activation address, whether the activation address is matched with a pre-stored registration address of the enterprise server corresponding to the activation address or not is judged, and if the matching is successful, the information acquisition record corresponding to the activation address is determined to be an effective record; and if the matching is unsuccessful, determining the information acquisition record corresponding to the activation address as an invalid record.

In step S224, the enterprise information recorded as the valid information acquisition record is determined as the target enterprise information.

Step S225, aiming at each group of target enterprise information, determining enterprise information characteristic vectors of each group of target enterprise information; acquiring a feature extraction logic table of each group of enterprise feature vectors, and adding each feature extraction logic table to a reference feature list of each group of target enterprise information to obtain a feature information update table corresponding to each group of target enterprise information; adjusting each group of enterprise feature vectors according to each feature information update table to obtain a first enterprise feature vector; and establishing a plurality of feature vector sequence tables for sequencing the first enterprise feature vectors by taking each group of first enterprise feature vectors as reference enterprise feature vectors.

Step S226, classifying the device addresses of the enterprise servers corresponding to each group of target enterprise information according to each group of feature vector sequence list to obtain multiple classes of device addresses, and generating a region division strategy set corresponding to the feature vector sequence list according to the multiple classes of device addresses.

In step S225, each feature vector sequence table is sorted according to the size of a feature value in each reference enterprise feature vector.

It can be understood that, through steps S221 to S226, the validity of the information acquisition record corresponding to each group of enterprise information can be determined, so as to filter the enterprise information corresponding to the invalid information acquisition record, and avoid the influence of the enterprise information corresponding to the invalid information acquisition record on the accuracy of the subsequently created region partition policy set. Further, the first enterprise feature vectors are sorted according to the size of one feature value in each reference enterprise feature vector, so that the features of enterprise information can be analyzed from multiple dimensions, and further, the region division strategy sets generated aiming at the features of different dimensions are determined, and therefore the multiple region division strategy sets are determined comprehensively.

It can be understood that different region partitioning policy sets are created/generated according to dimensional enterprise information, and in order to ensure that policy information can be accurately released, a policy information requirement category corresponding to each region partitioning policy set needs to be determined to ensure a subsequent release matching rate. For this purpose, in step S24, the determining, by using the second information reporting time and the second address information of each area partition policy set, multiple policy information requirement categories corresponding to the area partition policy set may specifically include the following:

step S241, obtaining a label of longitude and latitude information corresponding to the second address information in each region division policy set and an association relationship between the second address information and a preset policy information release frequency according to the second information reporting time of each region division policy set.

Step S242, determining target longitude and latitude information corresponding to the incidence relation in each area division strategy set according to the label and the incidence relation; and determining an area identifier corresponding to a policy delivery area where the target longitude and latitude information is located.

Step S243, when the region identifier is determined to meet policy information releasing conditions, counting historical policy information corresponding to second address information in each region division policy set according to the region identifier; a plurality of keywords in the historical policy information is determined.

Step S244, determining a hamming distance between each keyword of each region division policy set under the information rejection behavior and each keyword of each region division policy set under the information rejection behavior according to the keyword of each region division policy set under the information rejection behavior and the word vector thereof under the information rejection behavior, and transferring the keyword of each region division policy set under the information reception behavior and the keyword under the information rejection behavior whose hamming distance is less than a predetermined distance to the corresponding information rejection behavior.

Step S245, under the condition that a plurality of keywords are contained in the current information receiving behavior of each region division strategy set, determining the Hamming distance between the keywords of each region division strategy set in the current information receiving behavior according to the keywords of each region division strategy set in the information rejecting behavior and the word vectors thereof, and classifying the keywords in the current information receiving behavior according to the Hamming distance between the keywords.

Step S246, setting a keyword signature for each category of keywords obtained by the above classification according to the keywords of each regional division policy set under the information rejection behavior and the word vectors thereof, and determining a plurality of policy information requirement categories corresponding to each regional division policy set according to the keyword signatures.

It can be understood that, through steps S241 to S246, the target longitude and latitude information in each region division policy set can be determined according to the second information reporting time and the second address information of each region division policy set, and the historical policy information in each region division policy set can be counted according to the region identifier determined by the target longitude and latitude information, so as to determine a plurality of keywords in the historical policy information. Further, classifying the plurality of keywords according to the information rejection behavior and the information receiving behavior of each regional division policy set, setting a keyword signature for each class of keywords obtained by classification, and finally determining a plurality of policy information demand classes corresponding to each regional division policy set according to the keyword signatures. Therefore, different policy information requirement categories can be accurately determined according to the keyword classification.

When the corresponding policy information is released to the enterprise server, the throughput of each enterprise server needs to be considered, omission of the enterprise server when the enterprise server receives the policy information released by the cloud server is avoided, and the accuracy of releasing the policy information by the cloud server is further ensured. For this purpose, in step S25, the delivering the policy information corresponding to the enterprise information included in each divided area to the enterprise server corresponding to the enterprise information included in each divided area according to the policy information delivering policy may specifically include the following:

and step S251, regularly releasing the policy information corresponding to the enterprise information included in each divided region to an enterprise server corresponding to the enterprise information included in each divided region according to the policy information release policy.

Step S252, acquiring a receiving frequency of each enterprise server receiving the current policy information and a storing frequency of storing the current policy information.

Step S253, according to the receiving frequency and the storing frequency, sending a throughput adjustment parameter to each enterprise server before delivering corresponding policy information to each enterprise server next time, so that each enterprise server adjusts the receiving frequency and the storing frequency thereof according to the throughput adjustment parameter.

In step S254, after receiving the prompt information for completing the adjustment of the receiving frequency and the storage frequency of each enterprise server, the corresponding policy information is continuously delivered to each enterprise server.

In step S253, each enterprise server is enabled to adjust its own receiving frequency and storage frequency according to the throughput adjustment parameter, which may specifically include the following:

step S2531, it is determined whether the reception frequency of each enterprise server exceeds a first set frequency and whether the storage frequency of each enterprise server is lower than a second set frequency.

And S2532, when the receiving frequency of each enterprise server exceeds the first set frequency and the storage frequency of each enterprise server is lower than the second set frequency, reducing the receiving frequency and the storage frequency of each enterprise server according to the throughput adjusting parameter.

And step S2533, when the receiving frequency of each enterprise server does not exceed the first set frequency and the storage frequency of each enterprise server is not lower than the second set frequency, increasing the receiving frequency and the storage frequency of each enterprise server according to the throughput adjusting parameter.

And S2534, when the receiving frequency of each enterprise server exceeds the first set frequency and the storage frequency of each enterprise server is not lower than a second set frequency, reducing the receiving frequency of each enterprise server and increasing the storage frequency of each enterprise server according to the throughput adjusting parameter.

Step S2535, and when the receiving frequency of each enterprise server does not exceed the first set frequency and the storage frequency of each enterprise server is not lower than a second set frequency, increasing the receiving frequency of each enterprise server and reducing the storage frequency of each enterprise server according to the throughput adjusting parameter.

It is understood that through steps S251 to S254, the receiving frequency and the storing frequency of each enterprise server can be adjusted based on the comparison relationship between the receiving frequency and the first set frequency and the comparison relationship between the storing frequency and the second set frequency of each enterprise server, so as to adjust the throughput of each enterprise server. Further, after receiving the prompt information fed back by each enterprise server to complete the adjustment of the receiving frequency and the storage frequency of the enterprise server, the corresponding policy information is continuously released to each enterprise server. Therefore, the change of the throughput of each enterprise server can be taken into consideration, omission caused by mismatching of the throughputs when the enterprise servers receive the policy information released by the cloud server is avoided, and the accuracy of releasing the policy information by the cloud server is further ensured.

In a specific implementation, in order to ensure the integrity of the multiple sets of enterprise information obtained from the enterprise information data pool, in step S21, the obtaining of the multiple sets of enterprise information from the pre-stored enterprise information data pool may further include the following steps:

and step S211, acquiring data updating thread information of the enterprise information data pool as an information integrity check index.

Step S212, comparing the information integrity check index with a pre-stored verification index template to obtain a comparison result.

And step S213, when the comparison result represents that the information integrity check index passes the check, judging that the data updating thread of the enterprise information data pool is in a normal state, and determining the finishing time of the data updating thread according to the updating thread progress, the updating thread speed and the updating thread delay.

And step S214, acquiring a plurality of groups of enterprise information from the enterprise information data pool when the completion time is reached.

In step S211, the data update thread information of the enterprise information data pool includes a plurality of kinds of the following information: updating thread progress, updating thread rate, and updating thread latency.

In step S214, each set of business information is information for which the periodic update is completed.

It can be appreciated that through steps S211 to S214, the completion time of the data updating thread can be determined when the data updating thread is determined to be in a normal state, and then multiple sets of enterprise information are obtained from the enterprise information data pool at the completion time, so as to ensure that the obtained enterprise information is updated periodically, and thus ensure the integrity of the multiple sets of enterprise information obtained from the enterprise information data pool.

On the basis of the above, the embodiment of the present invention provides a localized data analysis device 200. Fig. 2 is a functional block diagram of a localized data analysis device 200 according to an embodiment of the present invention, where the localized data analysis device 200 includes:

an obtaining module 201, configured to obtain multiple sets of enterprise information from a pre-stored enterprise information data pool, and obtain first address information and first information reporting time of each set of enterprise information;

a creating module 202, configured to create a plurality of region partitioning policy sets by using the first address information and the first information reporting time of each group of enterprise information; aiming at each created region division strategy set, a region address boundary setting strategy exists in the region division strategy set, and the longitude and latitude distance calculated according to the first address information of the first enterprise information in the region division strategy set and the first address information of the second enterprise information corresponding to the region address boundary setting strategy is smaller than the set distance; the first information reporting moments of all enterprise information in the regional division strategy set are arranged according to the sequence of reporting time;

a first determining module 203, configured to set, for each region partition policy set, first address information and first information reporting time of second enterprise information corresponding to a policy for a region address boundary of the region partition policy set, or first address information and first information reporting time of a last group of enterprise information in the region partition policy set, as second address information and second information reporting time of the region partition policy set; the last group of enterprise information is obtained according to a sorting sequence obtained by sorting the first information reporting time of the enterprise information, and the first information reporting time of the last group of enterprise information is positioned at the last of the sorting sequence;

a second determining module 204, configured to determine, by using the second information reporting time and the second address information of each region partition policy set, multiple policy information requirement categories corresponding to the region partition policy set; determining a weighted sum value of information delivery matching degrees of each divided region in each region division strategy set according to a plurality of corresponding policy information demand categories of each region division strategy set and pre-stored policy information delivery strategies;

a releasing module 205, configured to use the region division policy set corresponding to the maximum weighted sum value as a target region division policy set; carrying out regional division on multiple groups of enterprise information according to the target regional division strategy set and the first address information of each group of enterprise information to obtain multiple divided regions; and releasing corresponding policy information to an enterprise server corresponding to the enterprise information included in each divided region according to the policy information releasing strategy.

In an alternative embodiment, the creating module 202 is configured to:

In an alternative embodiment, the second determining module 204 is configured to:

In an alternative embodiment, the delivery module 205 is configured to:

The cloud server 300 includes a processor and a memory, the acquiring module 201, the creating module 202, the first determining module 203, the second determining module 204, the delivering module 205, and the like are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. One or more kernels can be set, and the delivery accuracy of the policy information is improved by adjusting kernel parameters.

An embodiment of the present invention provides a readable storage medium, on which a program is stored, which, when executed by a processor, implements the localized data analysis method.

The embodiment of the invention provides a processor, which is used for running a program, wherein the regional data analysis method is executed when the program runs.

In the embodiment of the present invention, as shown in fig. 3, the cloud server 300 includes at least one processor 301, and at least one memory 302 and a bus connected to the processor 301; wherein, the processor 301 and the memory 302 complete the communication with each other through the bus 303; the processor 301 is used to call program instructions in the memory 302 to perform the above-described localized data analysis method. The cloud server 300 herein may be a cloud server, a PC, a PAD, a mobile phone, etc.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, cloud servers (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing cloud server to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing cloud server, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a cloud server includes one or more processors (CPUs), memory, and a bus. The cloud server may also include input/output interfaces, network interfaces, and the like.

The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage cloud servers, or any other non-transmission medium that can be used to store information that can be accessed by a computing cloud server. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or cloud server that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or cloud server. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article, or cloud server comprising the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A regional data analysis method is applied to a cloud server, and comprises the following steps:

2. The method of claim 1, wherein creating a plurality of regional division policy sets using the first address information and the first information reporting time of each group of enterprise information comprises:

3. The method according to claim 1 or 2, wherein the determining the policy information requirement categories corresponding to each region partition policy set by using the second information reporting time and the second address information of each region partition policy set includes:

4. The method according to claim 3, wherein the delivering the corresponding policy information to the enterprise server corresponding to the enterprise information included in each divided region according to the policy information delivery policy comprises:

5. A regional data analysis device, which is applied to a cloud server, the device comprising:

6. The apparatus of claim 5, wherein the creation module is configured to:

7. The apparatus of claim 5 or 6, wherein the second determining module is configured to:

8. The apparatus of claim 7, wherein the delivery module is configured to:

9. A cloud server, comprising a processor, and a memory and a bus connected to the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is configured to call program instructions in the memory to perform the regionalized data analysis method of any of the above claims 1-4.

10. A readable storage medium, characterized in that a program is stored thereon, which when executed by a processor implements the regionalized data analysis method of any one of the preceding claims 1 to 4.