A cloud computing-based data statistics management system and method

Abstract

This invention discloses a cloud computing-based data statistics management system and method, relating to the field of data statistics technology. The system includes a data acquisition module, an error data filtering module, a classification module, a real-time data addition module, and a statistics module. The data acquisition module acquires data stored on each server. The error filtering module filters out garbled data and data containing unconventional words from all data. The classification module classifies data for which the server provides classification criteria, and for data without provided classification criteria, the system learns historical classification criteria to determine the corresponding classification criteria. The real-time data addition module integrates data added from the servers into the system for classification and statistics. The statistics module performs statistics on the already classified data, calculates the data quantity and percentage, generates charts, and sends them back to the server.

Description

Technical Field

[0001] This invention relates to the field of data statistics technology, specifically to a cloud computing-based data statistics management system and method. Background Technology

[0002] Cloud computing refers to a system with extremely high computing power formed through computer networks. It can store and aggregate relevant resources and can be configured on demand to provide personalized services to users.

[0003] Traditional data statistics methods rely on manual data input by staff, followed by computer classification and statistics. This results in very slow data processing when the data to be analyzed is very large. In addition, the following situations may occur during the data statistics process: 1. New data added during the statistics process may affect the results of previously compiled data; 2. Staff need to consider the classification criteria when classifying data, and sometimes the inaccuracy of the classification criteria leads to unsatisfactory final classification results. Summary of the Invention

[0004] The purpose of this invention is to provide a cloud computing-based data statistics management system and method to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a data statistics management method based on cloud computing, comprising the following steps:

[0006] Step S1: Import the server number into the system. After the server saves all the data that needs to be counted, the server displays the complete data identifier. The system determines whether to acquire data based on the total number of data calculated per unit time. When the total number of data calculated is less than the minimum number of data for the system to start data statistics, data acquisition will begin after the system-set time T1.

[0007] Step S2: The system determines data that does not belong to the encoding range as garbled data based on the data's encoding range, and then compares it with the database of imported regular words to filter out non-regular data; the garbled data and non-regular data are returned to the server for modification and then retransmitted to the system; the system filters the transmitted data a second time and writes the unmodified non-regular words into the database;

[0008] Step S3: The system classifies the data for which the server provides classification criteria according to the classification criteria, and saves the new classification criteria provided by the server; for data for which no classification criteria are provided, the system learns the historical classification criteria to determine the corresponding classification criteria for the data, and classifies the data according to the classification criteria.

[0009] Step S4: Count the number of data in each category after classification according to the classification criteria, and calculate the proportion η of each category. k When the system receives new data, it adds the data to the corresponding category, changes the amount and percentage of data in each category in real time, and sends the final results to the server in the form of a chart.

[0010] 10. Further, in step S1, the system retrieves the data stored on the server, including the following steps:

[0011] Step S11: Import the server number into the system. When the server has stored all the data that the system needs to statistically analyze, a complete data identifier will be displayed on the server.

[0012] Step S12: The system calculates the amount of data stored on each server every unit time T, and sets N. i Let N be the number of data stored in the i-th server, j be the total number of servers, and N be the total number of servers. 总 The total amount of data stored across all servers; according to the formula:

[0013]

[0014] Step S12: Set the minimum number of data points for the system to start data statistics to δ; when N 总 When <δ, the system begins retrieving data stored in all servers after T1; when N 总 When the value is greater than δ, the system directly retrieves the data stored in all servers;

[0015] Step S13: The system generates set B i B i This represents the data set of server i; the system transfers data from server i to set B. i middle.

[0016] 11. Further, in step S2, erroneous data is filtered out from all the acquired data, including the following steps:

[0017] Step S21: Convert the data into a string according to the server's number sequence, and judge each character according to the string arrangement order; determine whether any character belongs to the system's encoding range according to the system's encoding method; if it belongs to the system's encoding range, the data is determined to be normal data; if it does not belong to the system's encoding range, the data is determined to be garbled data.

[0018] Step S22: Import the database containing regular phrases. When the normal data is a number, skip the comparison directly. When the normal data contains non-numeric characters, compare it with the database line by line. Split the normal data containing non-numeric characters into several phrases. When all the phrases exist in the database, the normal data is determined to be correct. When a phrase does not exist in the database, the normal data is determined to contain unconventional words.

[0019] Step S23: Create set P based on the server ID. i Data identified as garbled text and data containing unconventional words are placed into corresponding sets, and the sets are sent back to the server.

[0020] Step S24: The server modifies the data that is determined to be garbled data according to the system's encoding method, compares the data containing unconventional words with the original data in the server, and determines whether there is an error and makes corrections accordingly.

[0021] Step S25: After the server has processed all the erroneous data, it resends it back to the system for secondary filtering.

[0022] Step S26: The system re-accepts the modified data and repeats steps S21 and S22; when the data containing unconventional words is modified to be consistent with the original data, the unconventional words in the data are written into the database containing conventional words.

[0023] 12. Further, step S3 involves classifying the filtered data, including the following steps:

[0024] Step S31: Set a threshold τ to represent the number of times the server needs to upload classification criteria; let t be the number of times the server with ID i has uploaded classification criteria. i ;

[0025] When t i When <τ, or t i When a classification standard exists in server i with a value greater than or equal to τ, the system reads the classification standard of server i, decomposes the classification standard into k keywords, and then selects the appropriate keywords from set B. i Generate k subsets b from the given data. k The data is compared one by one. When the data satisfies the k-th keyword, the data is written into subset b. k middle;

[0026] Step S32: The system counts the number of times the j-th classification standard is used in the classification criteria for server i. ij Take t ijThe maximum value of the j-th classification standard is used to mark the j-th classification standard as the default classification standard of the server with number i; by comparing data with the same classification standard, keywords that exist simultaneously are retrieved, and the number of such keywords in set B is calculated. i The proportion θ in the formula is:

[0027]

[0028] Where n is the number of keywords, T i For set B i The number of data points; a threshold μ is set, and when θ > μ, the system matches the keywords with the classification criteria and saves them in the system;

[0029] Step S33: When t i When the value is greater than or equal to τ and no classification standard exists on the server, the system compares the data with the keywords stored in the system.

[0030] When a keyword matches a classification criterion, the classification criterion that matches the keyword is used as the classification criterion for the data.

[0031] When keywords in the data match multiple classification criteria, the system calculates the proportion θ′ of keywords matched by the p-th classification criterion in the data. p Let a pl Let r be the number of keywords matched by the l-th keyword for the p-th classification criterion, and r be the number of keywords matched by a certain classification criterion, according to the formula:

[0032]

[0033] Compare the proportion θ′ of keywords matched by the p-th classification criterion in the data. p The classification standard corresponding to the maximum value is selected as the classification standard of the data; the proportion of keywords matched by the p-th classification standard in the data is θ′. p When there are q identical maximum values, the system will use the classification criteria corresponding to the q identical maximum values ​​as the classification criteria for the data simultaneously, that is, classify the data q times.

[0034] Step S34: If no keyword exists in the data, then the default classification standard of server number i is used as the pair set B. i Classification criteria for Chinese data;

[0035] Step S35: When there is data that does not satisfy all keywords, the system selects set B. i Generate a new subset b0 and write all data that does not meet the keyword into subset b0.

[0036] 13. Further, step S4 involves statistical analysis of the categorized data, including the following steps:

[0037] Step S41: Statistical analysis of set B i k subsets b k The number of data is M k Let s be a set B i The number of subsets that do not include subset b0 is calculated for each subset b. k In set B i The percentage of data in η k If a subset b0 exists, the number of data points in subset b0 is M0; according to the formula:

[0038]

[0039] Wherein, subset b0 is in set B i The proportion of data in

[0040] Step S42: Every unit time T, the system checks whether there is a complete data identifier in each server. If there is no complete data identifier in some servers, the system acquires the new data from the servers and transmits it to the system.

[0041] Step S43: When new data is imported into the system, the system statistically analyzes the new data and classifies it into subset b. k The number in is m k Calculate each subset b k In set B i The percentage of data in η k According to the formula:

[0042]

[0043] Wherein, subset b0 is in set B i The proportion of data in

[0044] Step S44: Obtain the subset b k Data quantity M k and the percentage of data η k The data is compiled in the form of charts and then sent to the server.

[0045] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: When statistically analyzing data on a server, this invention first categorizes the data according to the server's classification criteria; then, it compares the data with historical classification criteria to identify identical keywords and matches these keywords with the classification criteria; in subsequent data classifications, it matches the corresponding classification criteria based on the keywords in the data; the system automatically selects the required classification criteria based on the data, reducing the impact of classification criteria on statistical results; and new data is added in real time during the statistical process, avoiding problems with the statistical results due to new data. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 A flowchart illustrating the steps of a cloud-based data statistics and management method;

[0048] Figure 2 A system module diagram of a cloud-based data statistics and management system;

[0049] Figure 3 This is a statistical result chart of the first embodiment of a cloud-based data statistics management system and method;

[0050] Figure 4 This is a statistical result chart of the second embodiment of a cloud computing-based data statistics management system and method. Detailed Implementation

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] Please see Figure 1 , Figure 2 The present invention provides a technical solution: a data statistics management method based on cloud computing, the method comprising the following steps:

[0053] Step S1: Import the server number into the system. After the server saves all the data that needs to be counted, the server displays the complete data identifier. The system determines whether to acquire data based on the total number of data calculated per unit time. When the total number of data calculated is less than the minimum number of data for the system to start data statistics, data acquisition will begin after the system-set time T1.

[0054] In step S1, the system retrieves the data stored on the server, including the following steps:

[0055] Step S11: The server imports the server number into the system. When the server has stored all the data that the system needs to statistically analyze, a complete data identifier is displayed on the server.

[0056] Step S12: The system calculates the amount of data stored on each server every unit time T, and sets N. i Let N be the number of data stored in the i-th server, j be the total number of servers, and N be the total number of servers. 总 The total amount of data stored across all servers; according to the formula:

[0057]

[0058] Step S12: Set the minimum number of data points for the system to start data statistics to δ; when N 总 When <δ, the system begins retrieving data stored in all servers after T1; when N 总 When the value is greater than δ, the system directly retrieves the data stored in all servers;

[0059] Step S13: The system generates set B i B i This represents the data set of server i; the system transfers data from server i to set B. i middle.

[0060] Step S2: The system determines data that does not belong to the encoding range as garbled data based on the data's encoding range, and then compares it with the database of imported regular words to filter out non-regular data; the garbled data and non-regular data are returned to the server for modification and then retransmitted to the system; the system filters the transmitted data a second time and writes the unmodified non-regular words into the database;

[0061] In step S2, erroneous data is filtered out from all the acquired data, including the following steps:

[0062] 14. In step S2, erroneous data is filtered out from all the acquired data, including the following steps:

[0063] Step S21: Convert the data into a string according to the server's number sequence, and judge each character according to the string arrangement order; determine whether any character belongs to the system's encoding range according to the system's encoding method; if it belongs to the system's encoding range, the data is determined to be normal data; if it does not belong to the system's encoding range, the data is determined to be garbled data.

[0064] Step S22: Import the database containing regular phrases. When the normal data is a number, skip the comparison directly. When the normal data contains non-numeric characters, compare it with the database line by line. Split the normal data containing non-numeric characters into several phrases. When all the phrases exist in the database, the normal data is determined to be correct. When a phrase does not exist in the database, the normal data is determined to contain unconventional words.

[0065] Step S23: Create set P based on the server ID. i Data identified as garbled text and data containing unconventional words are placed into corresponding sets, and the sets are sent back to the server.

[0066] Step S24: The server modifies the data that is determined to be garbled data according to the system's encoding method, compares the data containing unconventional words with the original data in the server, and determines whether there is an error and makes corrections accordingly.

[0067] Step S25: After the server has processed all the erroneous data, it resends it back to the system for secondary filtering.

[0068] Step S26: The system re-accepts the modified data and repeats steps S21 and S22; when the data containing unconventional words is modified to be consistent with the original data, the unconventional words in the data are written into the database containing conventional words.

[0069] 15. In step S3, classifying the filtered data includes the following steps:

[0070] Step S31: Set a threshold τ to represent the number of times the server needs to upload classification criteria; let t be the number of times the server with ID i has uploaded classification criteria. i ;

[0071] When t i When <τ, or t i When a classification standard exists in server i with a value greater than or equal to τ, the system reads the classification standard of server i, decomposes the classification standard into k keywords, and then selects the appropriate keywords from set B. i Generate k subsets b from the given data. kThe data is compared one by one. When the data satisfies the k-th keyword, the data is written into subset b. k middle;

[0072] Step S32: The system counts the number of times the j-th classification standard is used in the classification criteria for server i. ij Take t ij The maximum value of the j-th classification standard is used to mark the j-th classification standard as the default classification standard of the server with number i; by comparing data with the same classification standard, keywords that exist simultaneously are retrieved, and the number of such keywords in set B is calculated. i The proportion θ in the formula is:

[0073]

[0074] Where n is the number of keywords, T i For set B i The number of data points; a threshold μ is set, and when θ > μ, the system matches the keywords with the classification criteria and saves them in the system;

[0075] Step S33: When t i When the value is greater than or equal to τ and no classification standard exists on the server, the system compares the data with the keywords stored in the system.

[0076] When a keyword matches a classification criterion, the classification criterion that matches the keyword is used as the classification criterion for the data.

[0077] When keywords in the data match multiple classification criteria, the system calculates the proportion θ′ of keywords matched by the p-th classification criterion in the data. p Let a pl Let r be the number of keywords matched by the l-th keyword for the p-th classification criterion, and r be the number of keywords matched by a certain classification criterion, according to the formula:

[0078]

[0079] Compare the proportion θ′ of keywords matched by the p-th classification criterion in the data. p The classification standard corresponding to the maximum value is selected as the classification standard of the data; the proportion of keywords matched by the p-th classification standard in the data is θ′. p When there are q identical maximum values, the system will use the classification criteria corresponding to the q identical maximum values ​​as the classification criteria for the data simultaneously, that is, classify the data q times.

[0080] Step S34: If no keyword exists in the data, then the default classification standard of server number i is used as the pair set B. iClassification criteria for Chinese data;

[0081] Step S35: When there is data that does not satisfy all keywords, the system selects set B. i Generate a new subset b0 and write all data that does not meet the keyword into subset b0.

[0082] Step S4: Count the number of data in each category after classification according to the classification criteria, and calculate the proportion η of each category. k When the system receives new data, it adds the data to the corresponding category, changes the amount and percentage of data in each category in real time, and sends the final results to the server in the form of a chart.

[0083] 16. Step S4 involves statistical analysis of the categorized data, including the following steps:

[0084] Step S41: Statistical analysis of set B i k subsets b k The number of data is M k Let s be a set B i The number of subsets that do not include subset b0 is calculated for each subset b. k In set B i The percentage of data in η k If a subset b0 exists, the number of data points in subset b0 is M0; according to the formula:

[0085]

[0086] Wherein, subset b0 is in set B i The proportion of data in

[0087] Step S42: Every unit time T, the system checks whether there is a complete data identifier in each server. If there is no complete data identifier in some servers, the system acquires the new data from the servers and transmits it to the system.

[0088] Step S43: When new data is imported into the system, the system statistically analyzes the new data and classifies it into subset b. k The number in is m k Calculate each subset b k In set B i The percentage of data in η k According to the formula:

[0089]

[0090] Wherein, subset b0 is in set B i The proportion of data in

[0091] Step S44: Obtain the subset b k Data quantity M k and the percentage of data η k The data is compiled in the form of charts and then sent to the server.

[0092] Servers A and B are connected to the system. Server 1 stores data in lemon yellow, rose red, olive green, seaweed green, yellow, sky blue, sapphire blue, coral red, and army green. Server 2 stores data in Huang Jiaqi (female), Zhang San (male), Li Si (female), Wang Yinghong (female), and Li Liu (male). Server 1 stores all the data that needs to be statistically analyzed and displays complete data tags.

[0093] The system counts the amount of data on each server every 30 seconds. Server 1 has 9 data entries, Server 2 has 5 data entries, and the total number of data entries N stored on all servers is calculated. 总 =9 + 5 = 14; Let the minimum number of data points δ at which the system begins data statistics be 30; because N 总 Since the value is less than δ, the system does not process the data. After 1 minute, if the value is still N... 总 When the value is less than δ, the system starts acquiring data directly; the system generates sets B1 and B2; data from server 1 is transferred to set B1, and data from server 2 is transferred to set B2;

[0094] The system checks the encoding range of the data in set B1 and finds no garbled data. It then checks for unconventional words, again finding none. The system checks the encoding range of the data in set B2 and finds no encoded data. It then checks for unconventional words and finds that "military green" is not in the database of regular words. Therefore, the system transmits "military green" back to the server. The server detects that "military green" is a correct word and transmits it back to the system without modification. Since the system detects that the transmitted data is still "military green," it writes "military green" into the database.

[0095] Let the classification criteria be based on the keywords corresponding to colors such as yellow, green, red, and blue, and the classification criteria be based on the keywords corresponding to gender such as male and female.

[0096] If the data in set B1 contains the keywords yellow, green, red, and blue, then color will be used as the classification criterion for set B1.

[0097] The data in set B2 includes the keywords yellow and red, as well as the keywords male and female; these correspond to the classification criteria by color and the classification criteria by gender, respectively.

[0098] The system calculates the percentage of keywords matching the color-based classification criteria in the data. Calculate the percentage of keywords matching the gender classification criteria in the data. Since 100% > 60%, we will use gender as the classification criterion for set B2.

[0099] The system divides set B1 into four subsets: subset b1 contains lemon yellow and yellow, subset b2 contains rose red and coral red, subset b3 contains olive green, seaweed green and army green, and subset b4 contains sky blue and sapphire blue.

[0100] The system divides set B2 into two subsets, where subset b1 contains Huang Jiaqi (female), Li Si (female), and Wang Yinghong (female), and subset b2 contains Zhang San (male) and Li Liu (male);

[0101] In set B1, subset b1 contains 2 data points, subset b2 contains 2 data points, subset b3 contains 3 data points, and subset b4 contains 2 data points; in set B2, subset b1 contains 3 data points, and subset b2 contains 2 data points.

[0102] The proportion of the number of data items in set B1 to the subset b1 in set B1.

[0103] The proportion of subset b2 in set B1 of the total number of data in set B1.

[0104] The proportion of subset b3 in set B1 of the total number of data in set B1.

[0105] The proportion of subset b4 in the total number of data in set B1

[0106] The proportion of the data in set B1 to the subset b1 in set B2.

[0107] The proportion of the data in set B1 to the subset b1 in set B2.

[0108] The statistical results are generated in the form of charts, such as Figure 3 , Figure 4 As shown, the results are returned to the server.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data statistics management method based on cloud computing, characterized in that, The method includes the following steps: Step S1: Import the server number into the system. After the server saves all the data that needs to be counted, the server displays the complete data identifier. The system determines whether to acquire data based on the total number of data calculated per unit time. When the total number of data calculated is less than the minimum number of data for the system to start data statistics, data acquisition will begin after the system-set time T1. Step S2: The system determines data that does not belong to the encoding range as garbled data based on the data's encoding range, and then compares it with the database of imported regular words to filter out non-regular data; the garbled data and non-regular data are returned to the server for modification and then retransmitted to the system; the system filters the transmitted data a second time and writes the unmodified non-regular words into the database; Step S3: The system classifies the data for which the server provides classification criteria according to the classification criteria, and saves the new classification criteria provided by the server; for data for which no classification criteria are provided, the system learns the historical classification criteria to determine the corresponding classification criteria for the data, and classifies the data according to the classification criteria. Step S4: Count the number of data in each category after classification according to the classification criteria, and calculate the proportion of data in each category. When the system receives new data, it adds the data to the corresponding category, changes the amount and percentage of data in each category in real time, and sends the final results to the server in the form of a chart.

2. The data statistics management method based on cloud computing according to claim 1, characterized in that: In step S1, the system retrieves data stored on the server, including the following steps: Step S11: Import the server number into the system. When the server has stored all the data that the system needs to statistically analyze, a complete data identifier will be displayed on the server. Step S12: The system every unit of time Calculate the amount of data stored on each server and set... For the The amount of data stored in each server N represents the total number of servers. 总 The total amount of data stored across all servers; according to the formula: Step S12: Set the minimum number of data points required for the system to begin data statistics. ;when At that time, the system passed Then it begins retrieving data stored on all servers; when At that time, the system directly retrieves data stored in all servers; Step S13: System generates set ,in Indicates the number is The system transfers data from the server to the collection. middle.

3. The data statistics management method based on cloud computing according to claim 1, characterized in that: In step S2, erroneous data is filtered out from all the acquired data, including the following steps: Step S21: Convert the data into a string according to the server's number sequence, and judge each character according to the string arrangement order; determine whether any character belongs to the system's encoding range according to the system's encoding method; if it belongs to the system's encoding range, the data is determined to be normal data; if it does not belong to the system's encoding range, the data is determined to be garbled data. Step S22: Import the database containing regular phrases. When the normal data is a number, skip the comparison directly. When the normal data contains non-numeric characters, compare it with the database line by line. Split the normal data containing non-numeric characters into several phrases. When all the phrases exist in the database, the normal data is determined to be correct. When a phrase does not exist in the database, the normal data is determined to contain unconventional words. Step S23: Create a set based on the server ID. Data identified as garbled text and data containing unconventional words are placed into corresponding sets, and the sets are sent back to the server. Step S24: The server modifies the data that is determined to be garbled data according to the system's encoding method, compares the data containing unconventional words with the original data in the server, and determines whether there is an error and makes corrections accordingly. Step S25: After the server has processed all the erroneous data, it resends it back to the system for secondary filtering. Step S26: The system re-accepts the modified data and repeats steps S21 and S22; when the data containing unconventional words is modified to be consistent with the original data, the unconventional words in the data are written into the database containing conventional words.

4. The data statistics management method based on cloud computing according to claim 1, characterized in that: The step S3 involves classifying the filtered data, including the following steps: Step S31: Set a threshold This indicates the number of times the server needs to upload the classification criteria; let the number be... The server has uploaded the classification criteria the number of times. ; when At that time, or And the number is When the classification criteria exist in the server, the system reads the number as The classification criteria for servers are broken down into the following categories: Each keyword is placed in a set based on its number. Generated in Subset Compare the data one by one, and when the data meets the condition... When there are 1 keyword, the data is written into a subset. middle; Step S32: The system statistics number is The server classification criteria Number of classification criteria ;Pick The maximum value of the first The classification criteria are marked with the number . The server's default classification standard; comparing data with the same classification standard, retrieving coexisting keywords, and calculating the number of such keywords in the set. The proportion of According to the formula: Where n is the number of keywords. For set The number of data points; set a threshold. ,when The system matches the keywords with the classification criteria and saves the results in the system. Step S33: When Furthermore, if no classification criteria exist on the server, the system compares the data with keywords stored in the system. When a keyword matches a classification criterion, the classification criterion that matches the keyword is used as the classification criterion for the data. When keywords in the data match multiple classification criteria, the system calculates the percentage of keywords matched by the p-th classification criterion in the data. ,set up The first matching the p-th classification criterion The number of keywords, r, is the number of keywords matched by a certain classification criterion, according to the formula: Compare the proportion θ of keywords matched by the p-th classification criterion in the data. ’ p The classification standard corresponding to the maximum value is selected as the classification standard of the data; the proportion of keywords matched by the p-th classification standard in the comparison results is... When there are q identical maximum values, the system will use the classification criteria corresponding to the q identical maximum values ​​as the classification criteria for the data simultaneously, that is, classify the data q times. Step S34: If the data does not contain keywords, then number it as... The default classification criteria for servers as a set Classification criteria for Chinese data; Step S35: When there is data that does not satisfy all keywords, the system retrieves the data from the set. Generate a new subset Write all data that does not meet the keyword criteria into a subset. .

5. The data statistics management method based on cloud computing according to claim 1, characterized in that: Step S4 involves statistical analysis of the categorized data, including the following steps: Step S41: Statistical set middle Subset The number of data is Let s be a set It does not contain subsets The number of subsets, calculate the number of each subset. In the set The proportion of data in When a subset exists Statistical subsets The number of data is According to the formula: Among them, subset In the set The proportion of data in ; Step S42: The system operates every unit of time. The system checks whether a complete data identifier exists in each server. If a complete data identifier does not exist in some servers, the system acquires the additional data from those servers and transmits it to the system. Step S43: When new data is imported into the system, the system classifies the new data into subsets. The number in Calculate each subset In the set The proportion of data in According to the formula: Among them, subset In the set The proportion of data in ; Step S44: Obtain the subset Data volume and the proportion of data quantity The data is compiled in the form of charts and then sent to the server.

6. A data statistics management system applied to the cloud computing-based data statistics management method according to any one of claims 1-5, characterized in that, The system includes a data acquisition module, an error data filtering module, a classification module, a real-time data addition module, and a statistics module; The data acquisition module is used to determine the total amount of data stored in all servers. Or after a unit period Retrieve the data stored in each server; The error filtering module is used to filter out garbled data and data containing unconventional words from all data, return them to the server for inspection and correction, and then transmit them back to the system. The system writes the correctly matched unconventional words into the database. The classification module is used to read the classification criteria from the server when the number of times the classification criteria are uploaded to the server is less than a set threshold or when the server provides classification criteria. The system then decomposes the classification criteria into several keywords and compares and classifies the data based on the keywords. When the number of times the system uploads classification criteria to the server is greater than the set threshold, the system matches historical classification criteria with corresponding keywords, compares the data with the keywords, and determines the classification criteria of the data. The real-time data addition module is used by the system to check the server every unit period during the data statistics process to determine whether there is a complete data identifier. If there is no complete data identifier, the data added from the server is imported into the system for classification and statistics. The statistics module is used to perform statistics on the already classified data, calculate the number of data and the proportion of data, generate charts and send them back to the server.

7. The data statistics management system according to claim 6, characterized in that: The data acquisition module includes a statistical data quantity unit and a data acquisition unit; The statistical data quantity unit is used to determine whether to acquire data based on the total amount of data in all servers; the data acquisition unit is used to directly acquire data when the total amount of data in all servers is greater than the minimum amount of data required for the system to start data statistics, or to acquire data after a set time.

8. The data statistics management system according to claim 6, characterized in that: The error filtering module includes garbled data units and data units containing unconventional words; The garbled data unit is used to determine the encoding range of the data. When the data does not meet the system's encoding range, the data is determined to be garbled data. The non-standard word data unit is used to compare the data with the standard words in the database. If the comparison fails, the data is transmitted back to the server for modification and then transmitted back to the system. The system checks whether the data has been modified. If it has not been modified, the non-standard words in the data are written into the database.

9. The data statistics management system according to claim 6, characterized in that: The classification module includes standard classification units and adaptive classification units; The standard classification unit is used to classify data according to the classification criteria provided by the server, and the adaptive classification unit is used by the system to determine the required classification criteria for the data provided by the server based on the server's historical classification criteria, and to save the new classification criteria provided by the server.

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