CN114840583A

CN114840583A - Panoramic index data analysis processing method and system based on block data construction

Info

Publication number: CN114840583A
Application number: CN202210724060.2A
Authority: CN
Inventors: 王伟福; 郭大琦; 韩荣杰; 黄翔; 杨培培; 楼丹; 唐文升; 赵志明
Original assignee: State Grid Zhejiang Electric Power Co Ltd Tonglu County Power Supply Co; Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Current assignee: State Grid Zhejiang Electric Power Co Ltd Tonglu County Power Supply Co; Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date: 2022-06-24
Filing date: 2022-06-24
Publication date: 2022-08-02
Anticipated expiration: 2042-06-24
Also published as: CN114840583B

Abstract

The invention provides a panoramic index data analysis processing method and a panoramic index data analysis processing system based on block data construction, wherein the panoramic index data analysis processing method comprises the following steps: acquiring block data of a previous moment corresponding to a first unit grid, and classifying a plurality of first index information into a first associated index set according to the index classification information; acquiring a second associated index set generated by a second unit grid at the current moment and a corresponding second scene label, and determining a first associated index set corresponding to the corresponding first scene label; calculating the index similarity of the second associated index set and the corresponding first associated index set, and taking the first associated index set with the index similarity larger than the preset similarity as a third associated index set; analyzing and calculating the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label; and if the index analysis coefficient is smaller than an index preset coefficient, generating corresponding processing data based on the second scene label.

Description

Panoramic index data analysis processing method and system based on block data construction

Technical Field

The invention relates to the technical field of data processing, in particular to a panoramic index data analysis processing method and a panoramic index data analysis processing system based on block data construction.

Background

The digital power supply station converges various types of perception terminals and control terminals, and specifically comprises terminals such as a power supply station distribution working terminal, internal equipment and mobile equipment, wherein the terminals are accessed into a unified intelligent Internet of things management platform through an MQTT or HTTP protocol, and then the intelligent Internet of things management platform provides a standardized data service interface for the power supply station integrated service digital platform, so that remote control, strategy issuing and load query between the power supply station integrated service digital platform and the power supply station terminal equipment are realized, and the power supply station side service scene area autonomy and local decision making are realized.

The intelligent Internet of things system of the digital power supply station can realize accurate sensing, unified management and control and work order data distribution processing of terminal equipment through the Internet of things management platform, creates a regulation and control mode with reproducibility and low limit configuration, promotes resource and data sharing, and provides big data support for service application of the power supply station.

In the prior art, a power minimum service unit is a station area, the station area refers to a power supply range of a transformer, a plurality of station areas form grids, at least 2 grids form a digital power supply station, a large area has a plurality of grids, namely a plurality of digital power supply stations, each digital power supply station collects data in respective responsible grids to form panoramic data required for constructing the digital power supply station, the panoramic data is a data base required for analyzing service indexes, and an integrated service digital platform can judge whether the service indexes in the panoramic data are qualified according to preset service rules. And the panoramic index data of each digital power supply station is stored in a database corresponding to the respective digital power supply station to form corresponding block data.

The block data is the synthesis of various data related to people, affairs, objects and the like formed in a physical space or an administrative region, and is equivalent to deconstructing, intersecting and fusing various 'data'. In the process of data block aggregation, filling of a data space, reconstruction of spatial data, organization in the aggregation process, aggregation in the organization process, and collection of new data and derivative data combined with original data are included. By applying the block data, the data can be mined to have higher and more values. However, in the prior art, it is still impossible to perform overall cross-block analysis on a plurality of blocks according to the difference of the partial relevance indexes of the panoramic indexes in the block data, so as to promote the purpose of internal quality improvement and efficiency improvement from the technical viewpoint.

Disclosure of Invention

The embodiment of the invention provides a block data construction-based panoramic index data analysis processing method and system, which can carry out cross-block overall analysis on a plurality of blocks according to different partial relevance indexes in a panoramic index, and pointedly improve the work order processing efficiency in a certain scene in different administrative regions.

In a first aspect of the embodiments of the present invention, a method for analyzing and processing panoramic index data constructed based on block data is provided, where the method includes:

acquiring block data of a previous moment corresponding to a first unit grid, dividing the block data into a plurality of first index information, classifying the plurality of first index information into first associated index sets according to index classification information, wherein each first associated index set corresponds to a first scene label;

acquiring a second associated index set generated by a second unit grid at the current moment and a corresponding second scene label, and determining a first associated index set corresponding to a corresponding first scene label according to the second scene label;

calculating the index similarity of the second associated index set and the corresponding first associated index set, and taking the first associated index set with the index similarity larger than the preset similarity as a third associated index set;

respectively extracting second target information and third target information corresponding to the second correlation index set and the third correlation index set, and analyzing and calculating the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label;

and if the index analysis coefficient is smaller than an index preset coefficient, generating corresponding processing data based on the second scene label.

Optionally, in a possible implementation manner of the first aspect, the obtaining block data of a previous time corresponding to the first unit grid, dividing the block data into a plurality of first index information, classifying the plurality of first index information into first associated index sets according to the index classification information, where each first associated index set corresponds to one first scene tag, includes:

dividing each point data and/or piece of data in the first unit grid at the previous moment into corresponding first index information to obtain a plurality of pieces of first index information in each first unit grid;

acquiring index selection information and scene labels in index classification information, determining corresponding first index information based on the index selection information to generate a first associated index set, wherein each first index information at least corresponds to one first keyword;

and adding a corresponding first scene label to the first association index set based on the scene label.

Optionally, in a possible implementation manner of the first aspect, the calculating an index similarity between the second relevant index set and the corresponding first relevant index set, and taking the first relevant index set with the index similarity greater than a preset similarity as a third relevant index set includes:

acquiring a second keyword corresponding to each piece of second index information, counting a first word quantity of the first keyword and a second word quantity of the second keyword in the first association index set, and obtaining quantity similarity based on the first word quantity and the second word quantity;

counting all first keywords and second keywords with the same dimensionality, and if the first keywords and the second keywords are judged to be main keywords, obtaining main body similarity according to the first keywords and the second keywords;

if the first keyword and the second keyword are judged to be numerical keywords, numerical similarity is obtained according to the first keyword and the second keyword;

performing fusion calculation according to the quantity similarity, the main body similarity and the numerical value similarity to obtain index similarity;

and taking the first associated index set with index similarity greater than the preset similarity with the second associated index set as a third associated index set.

Optionally, in a possible implementation manner of the first aspect, the obtaining a second keyword corresponding to each piece of second index information, counting a first word quantity of the first keyword and a second word quantity of the second keyword in the first association index set, and obtaining a quantity similarity based on the first word quantity and the second word quantity includes:

calculating the difference value between the first word quantity and the second word quantity to obtain a word quantity difference value, and calculating the average value of the first word quantity and the second word quantity to obtain an average word quantity value;

and calculating according to the word quantity difference and the average word quantity value to obtain quantity similarity.

Optionally, in a possible implementation manner of the first aspect, the counting all the first keywords and the second keywords with the same dimensionality, and if it is determined that the first keywords and the second keywords are main keywords, obtaining a main similarity according to the first keywords and the second keywords includes:

the method comprises the steps of counting a first keyword and a second keyword which are same in dimension and are main body keywords;

if the first main word of the first keyword is the same as the second main word of the second keyword, obtaining a first main sub-coefficient corresponding to the fixed numerical value;

if the first main word of the first keyword is different from the second main word of the second keyword, determining main similar information of the first main word and the second main word according to a main corresponding table to obtain a second main sub-coefficient;

and counting first main body sub-coefficients and second main body sub-coefficients corresponding to all first keywords and second keywords which are main body keywords, and obtaining main body similarity according to the first main body sub-coefficients and the second main body sub-coefficients.

Optionally, in a possible implementation manner of the first aspect, the counting first subject sub-coefficients and second subject sub-coefficients corresponding to all first keywords and all second keywords which are subject keywords, and obtaining a subject similarity according to the first subject sub-coefficients and the second subject sub-coefficients includes:

counting the number of first coefficients of the first main sub-coefficient and the number of second coefficients of the second main sub-coefficient;

and calculating according to the first coefficient number, the first main coefficient, the second coefficient number and the second main coefficient number to obtain the main similarity.

Optionally, in a possible implementation manner of the first aspect, if it is determined that the first keyword and the second keyword are numerical keywords, obtaining a numerical similarity according to the first keyword and the second keyword includes:

counting a first keyword and a second keyword which are same in dimensionality and are numerical keywords, wherein the numerical keywords are quantity words;

calculating a difference value of the first keyword and the second keyword to obtain a numerical difference value, and calculating an average value of the first keyword and the second keyword to obtain a numerical average value;

and calculating according to the numerical difference and the numerical average to obtain numerical similarity.

Optionally, in a possible implementation manner of the first aspect, the performing fusion calculation according to the number similarity, the subject similarity, and the numerical similarity to obtain an index similarity includes:

the index similarity is obtained by performing fusion calculation through the following formula calculation,

wherein the content of the first and second substances,

in order to indicate the degree of similarity,

in order to be a measure of the similarity of the quantities,

in order to be a number weight value,

in order to determine the similarity of the main body,

is the weight value of the main body,

in order to be a measure of the similarity of values,

is a numerical weight value, and is,

the value is normalized for the quantity,

the number of words that are the first number of words,

to the extent that the second number of words,

the values are normalized for the subject and,

is as follows

First principal sub-coefficients of first and second keywords of the same dimension,

an upper limit value of the number of first main body sub-coefficients of the first keyword and the second keyword which are of the same dimension,

a first coefficient quantity value for a first body sub-coefficient for a first keyword and a second keyword of the same dimension,

is as follows

A second principal sub-coefficient of the first keyword and the second keyword of the same dimension,

an upper limit value of the number of second main body sub-coefficients of the first keyword and the second keyword which are of the same dimension,

a second coefficient quantity value for a second body sub-coefficient for the first keyword and the second keyword of the same dimension,

the value is normalized for the value of the value,

is as follows

The value of each of the first key words,

is as follows

The value of the second key word is determined,

is as follows

The sub-number weight value corresponding to the numerical value of the first keyword,

the upper limit value of the number of the first keywords and the second keywords of the same dimension of the numerical value keywords,

the quantitative values of the first keyword and the second keyword that are the same dimension of the numerical keyword,

is a constant value.

Optionally, in a possible implementation manner of the first aspect, extracting second target information and third target information corresponding to the second relevant index set and the third relevant index set, respectively, and performing analysis calculation on the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label includes:

respectively acquiring a second associated index set and a third associated index set, and second index information and third index information of second work order data and third work order data;

extracting second work order information in the second work order data based on the second target information, wherein the second work order information at least comprises second time information, second route information and second satisfaction degree information;

extracting third work order information in the third work order data based on the third target information, wherein the third work order information at least comprises third time information, third route information and third satisfaction information;

acquiring all third time information, third route information and third satisfaction information corresponding to the block data of all first unit grids to obtain average time information, average route information and average satisfaction information;

and comparing the second time information, the second route information and the second satisfaction information with the average time information, the average route information and the average satisfaction information to obtain an index analysis coefficient.

Optionally, in a possible implementation manner of the first aspect, the comparing the second time information, the second route information, and the second satisfaction information with the average time information, the average route information, and the average satisfaction information to obtain an index analysis coefficient includes:

obtaining a time sub-value according to the difference value of the second time information and the average time information;

obtaining a distance sub-numerical value according to the difference value of the second distance information and the average distance information;

obtaining a satisfaction sub-numerical value according to the difference value of the second satisfaction information and the average satisfaction information;

and performing fusion calculation on the time subsystem numerical value, the distance subsystem numerical value and the satisfaction subsystem numerical value to obtain an index analysis coefficient.

Optionally, in a possible implementation manner of the first aspect, the fusion calculation of the time sub-value, the distance sub-value, and the satisfaction sub-value to obtain an index analysis coefficient includes:

the index analysis coefficient is calculated by the following formula,

wherein the content of the first and second substances,

for the purpose of index analysis of the coefficients,

is a value of the time sub-system,

is a weight value of the time, and is,

for the value of the distance sub-system,

in order to be a weight value for the journey,

in order to satisfy the sub-values of degree,

in order to have a weight value of the satisfaction degree,

in order to average out the time information,

is the second time information that is to be transmitted,

in order to obtain the average distance information,

in order to be the second route information,

in order to average out the satisfaction information,

is the second satisfaction information.

Optionally, in a possible implementation manner of the first aspect, if the index analysis coefficient is smaller than an index preset coefficient, generating corresponding processing data based on the second scene tag includes:

if the index analysis coefficient is smaller than the index preset coefficient, extracting a corresponding second scene label, a corresponding time sub-numerical value and a corresponding satisfaction sub-numerical value;

and comparing the quantity value of the time subsystem numerical value with the satisfaction subsystem numerical value to generate corresponding processing data.

Optionally, in a possible implementation manner of the first aspect, the comparing the time sub-value and the satisfaction sub-value with a quantitative value to generate corresponding processing data includes:

if the time subsystem numerical value is a positive number, the satisfaction subsystem numerical value is a negative number, the time subsystem numerical value is greater than the absolute value of the satisfaction subsystem numerical value, and the difference value between the time subsystem numerical value and the absolute value of the satisfaction subsystem numerical value is greater than a preset difference value, calling an efficiency preset document to generate time processing data;

if the time sub-numerical value is a positive number, the satisfaction sub-numerical value is a negative number, the time sub-numerical value is smaller than the absolute value of the satisfaction sub-numerical value, and the difference value between the absolute value of the satisfaction sub-numerical value and the time sub-numerical value is larger than a preset difference value, calling a satisfaction preset document to generate satisfaction processing data;

if the difference between the absolute value of the time sub-numerical value and the absolute value of the satisfaction sub-numerical value is less than or equal to the preset difference, and the difference between the absolute value of the satisfaction sub-numerical value and the absolute value of the time sub-numerical value is less than or equal to the preset difference, respectively calling an efficiency preset document and a satisfaction preset document to generate time processing data and satisfaction processing data.

In a second aspect of the embodiments of the present invention, there is provided a system for analyzing and processing panoramic index data constructed based on block data, including:

the acquisition module is used for acquiring block data of a previous moment corresponding to a first unit grid, dividing the block data into a plurality of first index information, classifying the plurality of first index information into first associated index sets according to index classification information, wherein each first associated index set corresponds to a first scene label;

the determining module is used for acquiring a second associated index set generated by a second unit grid at the current moment and a corresponding second scene label, and determining a first associated index set corresponding to the corresponding first scene label according to the second scene label;

the calculation module is used for calculating the index similarity between the second associated index set and the corresponding first associated index set, and taking the first associated index set with the index similarity larger than the preset similarity as a third associated index set;

the extraction module is used for respectively extracting second target information and third target information corresponding to the second correlation index set and the third correlation index set, and analyzing and calculating the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label;

and the generating module is used for generating corresponding processing data based on the second scene label if the index analysis coefficient is smaller than an index preset coefficient.

A third aspect of the embodiments of the present invention provides a storage medium, in which a computer program is stored, and the computer program is used for implementing the method according to the first aspect of the present invention and various possible designs of the first aspect when the computer program is executed by a processor.

The invention provides a block data construction-based panoramic index data analysis processing method and system, which can decompose block data in a plurality of unit grids to obtain a plurality of index information respectively, and classify by combining different scenes with different index information, so that all associated indexes in the same scene can form a set.

The invention respectively counts the information in the associated index set which needs to be compared, analyzes the corresponding target information to obtain the index analysis coefficient, further ensures that when each unit grid finishes the processing of a certain service, the index analysis coefficient of the processing can be determined by combining the historical processing behaviors of other unit grids, and carries out targeted guidance processing according to the index analysis coefficient, thereby improving the next service efficiency, service quality and the like.

According to the technical scheme provided by the invention, when the index similarity of the second associated index set and the first associated index set is calculated, firstly, each group of first keywords and second keywords are classified according to different attributes to obtain the numerical keywords and the main body keywords, then, the similarity of the two second associated index sets and the first associated index set is obtained according to the difference between the number of the first keywords and the second keywords, the difference between the numerical keywords and the difference between the main bodies, and further, the third associated index set which is closer to the second associated index sets in the plurality of first associated index sets of other unit grids is determined, so that the associated index sets in the second unit grid are more comparable to the associated index sets in the first unit grid.

When the index of the corresponding scene is analyzed, the second work order data and the third work order data of the corresponding scene are extracted, the time information, the distance information and the satisfaction degree information in the second work order information and the third work order information are compared, and when the index analysis coefficient does not meet the corresponding requirement, the time sub-system numerical value and the satisfaction degree sub-system numerical value are compared to generate corresponding processing data, so that the time processing data or the satisfaction degree processing data can be generated in an asynchronous and synchronous mode according to the time behavior and the satisfaction degree behavior, and workers of corresponding unit grids can be trained effectively.

Drawings

Fig. 1 is a flowchart of a first embodiment of a method for analyzing and processing panoramic index data constructed based on block data;

FIG. 2 is a flowchart of a second embodiment of a panoramic index data analysis processing method constructed based on block data;

fig. 3 is a flowchart of a first embodiment of a panoramic index data analysis processing apparatus constructed based on block data.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.

It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that A, B, C all comprise, "comprises A, B or C" means comprise one of A, B, C, "comprises A, B and/or C" means comprise any 1 or any 2 or 3 of A, B, C.

It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.

As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.

The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

Different administrative regions correspond to different unit grids, different unit grids can correspond to different blocks, main bodies of work order data generated by different blocks have certain differences, the processing behaviors of the blocks in the multiple association indexes can be reflected through comparison of the work order data with the same indexes of the multiple blocks, in the prior art, overall cross-block analysis can not be carried out on the multiple blocks according to the difference of partial association indexes in panoramic indexes, quality improvement and efficiency improvement can not be carried out on the inside, and the work order processing effects in different administrative regions can not be improved.

The invention provides a panoramic index data analysis processing method constructed based on block data, which comprises the following steps of:

step S110, obtaining block data of a previous time corresponding to the first unit grid, dividing the block data into a plurality of first index information, and classifying the plurality of first index information into first associated index sets according to the index classification information, where each first associated index set corresponds to one first scene tag. Each first grid of cells may correspond to an administrative area, and different administrative areas may correspond to different power teams, such as maintenance teams, installation teams, and so forth. The invention divides the block data of each first unit grid into a plurality of first index information, and classifies the plurality of first index information into a first associated index set by combining the index classification information. For example, the first scene label is a maintenance scene of the smart meter, at this time, the first index information may include meter damage of a first main body located at a first position at a first time, the second first index information may include maintenance of the smart meter by a first team after the first time, the third first index information may include a service life of the smart meter, the fourth first index information may include a damage cause of the smart meter, and the fifth first index information may include a service work order obtained after the maintenance is completed. The first scene label at this time may be a smart meter maintenance scene. The associated index set may include the five first index information described above.

In a possible implementation manner of the technical solution provided by the present invention, as shown in fig. 2, step S110 includes:

step S1101, dividing each point data and/or each piece of data in the first unit grid at the previous time into corresponding one piece of first index information, and obtaining a plurality of pieces of first index information in each first unit grid. Generally, each block data is composed of corresponding point data and/or piece of data, so that it can be divided into a plurality of pieces of first index information according to the difference of the point data and/or piece of data.

Step S1102, index selection information and scene labels in the index classification information are obtained, a first associated index set is generated by determining corresponding first index information based on the index selection information, and each first index information at least corresponds to one first keyword. According to the method and the device, a first associated index set is obtained according to index selection information and scene labels input by a worker, namely the worker collects associated first index information to obtain the first associated index set, each piece of first index information at the moment corresponds to at least one first keyword, for example, the first index information may include the use duration of an intelligent electric meter, and the first keyword at the moment may be the use duration and the like.

Step S1103, adding a corresponding first scene tag to the first association index set based on the scene tag. According to the method and the device, the corresponding first scene label is added to the first associated index set according to the scene label determined by the staff to the first associated index set. By the method, the staff can actively classify the block data to obtain a plurality of first associated index sets, and each first associated index set has a corresponding first scene label.

Step S120, a second associated index set generated by a second unit grid at the current moment and a corresponding second scene label are obtained, and a first associated index set corresponding to a corresponding first scene label is determined according to the second scene label. The second unit grid may be regarded as a first unit grid for generating a second associated index and a second scene tag at the current time, and after it is determined that one of the first unit grids generates a corresponding second associated index set and a second scene tag at the current time, a first associated index set corresponding to the corresponding first scene tag may be determined according to the second scene tag, different first unit grids may have different numbers of first associated index sets, and in some extreme scenes, the first associated index set corresponding to some of the first unit grids may be 0.

Step S130, calculating index similarity between the second associated index set and the corresponding first associated index set, and taking the first associated index set with the index similarity larger than the preset similarity as a third associated index set. The method calculates the index similarity of the second associated index set and the first associated index sets of different first unit grids, and screens the first associated index sets of the same scene according to the index similarity to obtain a third associated index set. For example, all the first unit grids coexist in a scenario where ten first related index sets correspond to a second related index set, and the smart meter is maintained, but the subject to be maintained, the maintenance type, the distance, and the like may have a certain difference, so that different first related index sets may have a certain difference from corresponding second related index sets at this time. The method determines the first associated index set with larger similarity with the second associated index set as a third associated index set.

In one possible implementation manner, the technical solution provided by the present invention, in step S130, includes:

and acquiring a second keyword corresponding to each piece of second index information, counting the first word quantity of the first keyword and the second word quantity of the second keyword in the first association index set, and obtaining the quantity similarity based on the first word quantity and the second word quantity. The method and the device can count the number of the first words and the number of the second words of the second keywords, and if the number of the first words is closer to that of the second words, the closer the corresponding first relevance index set is to the second relevance index set, the number similarity can be obtained according to the number of the first words and the number of the second words.

In a possible implementation manner, the technical solution provided by the present invention is that, counting a first word quantity of a first keyword and a second word quantity of a second keyword in the first association index set, and obtaining a quantity similarity based on the first word quantity and the second word quantity, including:

and calculating the difference value of the first word quantity and the second word quantity to obtain a word quantity difference value, and calculating the average value of the first word quantity and the second word quantity to obtain an average word quantity value. The invention calculates the word number difference and the average word number value respectively.

And calculating according to the word quantity difference and the average word quantity value to obtain quantity similarity. If the difference in the number of words is larger, the degree of similarity in the number of proofs is smaller. Through the method, the number of the described keywords corresponding to each scene can be obtained, and the similarity analysis of the first relevance index set and the second relevance index set is performed from the number dimension of the keywords.

And counting all first keywords and second keywords with the same dimensionality, and if the first keywords and the second keywords are judged to be main keywords, obtaining main body similarity according to the first keywords and the second keywords. The method and the device can obtain first keywords and second keywords with the same dimensionality, for example, the first keywords and the second keywords are main keywords, the first keywords can be family users at the moment, the first keywords can be business users and the like, for example, the first main body damaged by the intelligent electric meter can be family users, business users and the like, the method and the device can obtain main body similarity according to the first keywords and the second keywords at the moment, and if the keywords with certain dimensionality are the same, the similarity of the first keywords and the second keywords with the corresponding dimensionality can be 1 at the moment. If the keywords in some dimensions are different, the similarity between the first keyword and the second keyword in the corresponding dimension may be 0.5, 0.3, and so on.

In a possible implementation manner, the technical solution provided by the present invention is that, the counting all the first keywords and the second keywords with the same dimension, and if the first keywords and the second keywords are determined to be main keywords, obtaining a main similarity according to the first keywords and the second keywords, including:

the first keyword and the second keyword are counted as the same dimension of the main body keyword. The first keyword and the second keyword of the main body keyword with the same dimension can be obtained respectively, for example, in the dimension of the maintained main body, the first keyword and the second keyword can be the same dimension of a family user, and can also be the dimension of the family user, the dimension of a business user and the like.

And if the first main word of the first keyword is the same as the second main word of the second keyword, obtaining a first main sub-coefficient corresponding to the fixed numerical value. When the first main word and the second main word are the same, the users that may be maintained at this time are the same type of users, so the fixed number may be 1 at this time, and the first main sub-number is 1 at this time.

And if the first main word of the first keyword is different from the second main word of the second keyword, determining the main similar information of the first main word and the second main word according to a main corresponding table to obtain a second main sub-coefficient. At this time, the present invention may obtain corresponding main body similar information according to the difference between the first main body word and the second main body word, for example, if the first main body word is a family user and the second main body word is a business user, the main body similar information of the first main body word and the second main body word may be obtained at this time, and the second main body sub-coefficient may be obtained according to the main body similar information.

For example, the main body correspondence table has three types of corresponding home users, business users, and government users, where the home user corresponds to the home coefficient, the business user corresponds to the business coefficient, the government user corresponds to the government coefficient, and the home coefficient, the business coefficient, and the government coefficient are different coefficients. And the main body corresponds to the coefficients corresponding to the main bodies with different dimensions in the main body corresponding table. If the first subject word is the same as the second subject word, the number of the first subject sub-words corresponding to the fixed number at the moment is 1. If the first subject word and the second subject word are different, the subject similarity information may be a difference between two coefficients, for example, the first subject word is a family user, the corresponding family coefficient is 0.3, the second subject word is a business user, the corresponding family coefficient is 0.5, at this time, the subject similarity information may be 0.3/0.5=0.6, and at this time, the subject similarity information is 0.6. Coefficient values for home coefficients, business coefficients, and government coefficients may be set by the staff according to the actual scenario. It will be appreciated that the immediacy of repair required for home users, commercial users and government users is escalating, so the corresponding coefficients may be escalating. When different subjects are repaired, the adopted repair actions and repair strategies may be different to some extent.

And counting first main body sub-coefficients and second main body sub-coefficients corresponding to all first keywords and second keywords which are main body keywords, and obtaining main body similarity according to the first main body sub-coefficients and the second main body sub-coefficients. The method can calculate according to the first main body sub-coefficient and the second main body sub-coefficient to obtain the main body similarity. If the main bodies of the first key word and the second key word are more similar, the corresponding main body similarity is larger.

In a possible embodiment, the calculating first and second principal sub-coefficients corresponding to first and second keywords of all the main keywords, and obtaining the main similarity according to the first and second principal sub-coefficients includes:

and counting the number of the first coefficients of the first main sub-coefficient and the number of the second coefficients of the first main sub-coefficient. If the number of the first coefficients is larger, the more the first keywords and the second keywords which prove that the subjects are identical, the larger the similarity of the subjects at this time. If the number of the second coefficients is larger, the number of the first keywords and the second keywords which prove that the subjects are identical is smaller, and the similarity of the subjects is smaller.

And calculating according to the first coefficient number, the first main coefficient, the second coefficient number and the second main coefficient number to obtain the main similarity. The invention can carry out comprehensive calculation to obtain the difference of corresponding subjects and determine the similarity of the corresponding subjects.

And if the first keyword and the second keyword are judged to be numerical keywords, obtaining numerical similarity according to the first keyword and the second keyword. In a practical scenario, the first keyword and the second keyword may be numerical keywords, such as maintenance distance of 10 km, 20 km, and so on. At this time, the invention calculates the numerical similarity of the first keyword and the second keyword of the numerical type.

In a possible implementation manner, the obtaining a numerical similarity according to the first keyword and the second keyword if the first keyword and the second keyword are judged to be numerical keywords includes:

and counting a first keyword and a second keyword which are same in dimensionality and are numerical keywords, wherein the numerical keywords are quantity words. The method can count the first keyword and the second keyword which are numerical values and have the same dimension, for example, the dimension is a kilometer dimension, the maintenance distance of the first keyword is 10 kilometers, and the maintenance distance of the second keyword is 12 kilometers. 10 km and 12 km are number words.

And calculating the difference value of the first keyword and the second keyword to obtain a numerical difference value, and obtaining a numerical average value by the average value of the first keyword and the second keyword. The present invention obtains a difference between the first keyword and the second keyword to obtain a value difference, where the value difference may be 2, and the value average may be 11.

And calculating according to the numerical difference and the numerical average to obtain numerical similarity. If the numerical difference is larger, the lower the degree of similarity is proved, and if the numerical difference is smaller, the higher the corresponding phase velocity is proved.

And performing fusion calculation according to the quantity similarity, the main body similarity and the numerical value similarity to obtain index similarity. According to the method, the quantity similarity, the body similarity and the numerical value similarity are synthesized for comprehensive calculation, and the index similarity of the first correlation index and the second correlation index set considering the similarity of multiple dimensions is obtained.

In a possible embodiment, the performing fusion calculation according to the number similarity, the subject similarity, and the numerical similarity to obtain the index similarity includes:

wherein the content of the first and second substances,

in order to indicate the degree of similarity,

to be of similar quantityThe degree of the magnetic field is measured,

in order to be a number weight value,

in order to determine the similarity of the main body,

is the weight value of the main body,

in order to be a measure of the similarity of values,

is a numerical weight value, and is,

the value is normalized for the quantity,

the number of words that are the first number of words,

to the extent that the second number of words,

the values are normalized for the subject and,

is as follows

an upper limit value of the number of first subject sub-coefficients of the first keyword and the second keyword which are of the same dimension,

is as follows

is a normalized value of the numerical value,

is the value of the first keyword,

is the value of the first second keyword,

is as follows

the upper limit values of the first keyword and the second keyword which are the same dimension of the numeric keyword,

is a constant value.

By passing

Can respectively count the similarity

Similarity of subjects

And numerical similarity

And respectively carrying out weighted summation and average processing to obtain the final index similarity. And calculating the number similarity

The invention will pass

Obtaining the relation between the difference value of the first word quantity and the second word quantity and the average value if

The larger the difference between the first word quantity and the second word quantity is, the greater the quantity similarity is

The smaller. The invention calculates the similarity of the main body

When it is going to pass

And obtaining an average value of the sum of the first principal sub-coefficient and the second principal sub-coefficient, and if the first principal sub-coefficient and the second principal sub-coefficient are larger, proving that the similarity between corresponding subjects is larger, so that the correlation between the first correlation index set and the second correlation index set is larger. When the method calculates the numerical similarity, the numerical similarity can be calculated through

Obtaining the difference value of the number values of the first keyword and the second keyword of each dimension, wherein if the difference value of the number values of all the dimensions is larger, the numerical similarity at the moment is higher

The smaller. Through the technical scheme, the index similarity between the first associated index set and the second associated index set can be obtained through comprehensive calculation.

And taking the first associated index set with index similarity greater than the preset similarity with the second associated index set as a third associated index set. The method extracts all first associated index sets with index similarity greater than preset similarity with the second associated index set, takes the first associated index sets as third associated index sets, and analyzes all index information in the second associated index sets through the third associated index sets.

Step S140, respectively extracting second target information and third target information corresponding to the second relevant index set and the third relevant index set, and analyzing and calculating the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label. The invention can extract second target information and third target information corresponding to the second correlation index set and the third correlation index set, wherein the second target information and the third target information can be processing time information, satisfaction degree information and the like.

In one possible implementation manner, the technical solution provided by the present invention, in step S140, includes:

and respectively acquiring second index information and third index information of the second work order data and the third work order data in the second correlation index set and the third correlation index set. It can be understood that, when different teams perform operations and behaviors such as maintenance, corresponding work order data may be provided, and the work order data may include maintenance team information, maintenance target information, and the like, where the maintenance team information and the maintenance target information may be the second index information and the third index information.

And extracting second work order information in the second work order data based on the second target information, wherein the second work order information at least comprises second time information, second route information and second satisfaction information.

And extracting third work order information in the third work order data based on the third target information, wherein the third work order information at least comprises third time information, third route information and third satisfaction information.

The second time information, the second route information, and the second satisfaction information may be regarded as second time information, second route information, and second satisfaction information corresponding to the maintenance behavior of the second unit grid at the current time. The time information can be maintenance time corresponding to damage of the intelligent electric meter, the route information can be regarded as the distance between a main body of the damaged intelligent electric meter and the position of the maintenance team, and the satisfaction degree information can be regarded as satisfaction degree evaluation of the user on the maintenance team, and can be 5, 4 and the like.

And acquiring all third time information, third route information and third satisfaction information corresponding to the block data of all the first unit grids to obtain average time information, average route information and average satisfaction information.

The average time information, the average distance information, and the average satisfaction information at this time may be average values of all different third work order data and third work order information, that is, the average time information is an average value of time information corresponding to all the third work order information, the average distance information is an average value of distance information corresponding to all the third work order information, and the average satisfaction information is an average value of satisfaction information corresponding to all the third satisfaction information.

And comparing the second time information, the second route information and the second satisfaction information with the average time information, the average route information and the average satisfaction information to obtain an index analysis coefficient. Through the above manner, the purpose of comparing the current behavior with the average behavior is achieved, and index analysis is achieved.

In a possible implementation manner, the comparing the second time information, the second route information, and the second satisfaction information with the average time information, the average route information, and the average satisfaction information to obtain an index analysis coefficient includes:

and obtaining a time subsystem value according to the difference value of the second time information and the average time information. The average time information may be subtracted from the second time information to obtain a time sub-system value, and if the time sub-system value is larger, the response time for maintenance is proved, the time spent is longer, and the time sub-system value is larger at the moment.

And obtaining a distance subsystem value according to the difference value of the second distance information and the average distance information. The average distance information may be subtracted from the second distance information to obtain the distance subsystem value, and if the distance subsystem value is larger, it is proved that the maintained distance is longer, and the distance subsystem value is larger at this time.

And obtaining a satisfaction sub-value according to the difference value of the second satisfaction information and the average satisfaction information. The second satisfaction information is subtracted from the average satisfaction information to obtain the satisfaction sub-system value, and if the satisfaction sub-system value is larger, the more satisfied the behavior user is, the larger the satisfaction sub-system value is.

And performing fusion calculation on the time subsystem numerical value, the distance subsystem numerical value and the satisfaction subsystem numerical value to obtain an index analysis coefficient. Through the mode, the index analysis coefficient calculated by the method comprehensively considers a plurality of dimensions such as time, distance, satisfaction degree and the like, so that the calculated index analysis coefficient is more multidimensional and accurate, and the corresponding maintenance behavior can be objectively judged.

In a possible implementation manner, the fusion calculation of the time sub-system value, the distance sub-system value, and the satisfaction sub-system value to obtain an index analysis coefficient includes:

the index analysis coefficient is calculated by the following formula,

wherein the content of the first and second substances,

for the purpose of index analysis of the coefficients,

is a value of the time sub-system,

is a weight value of the time, and is,

for the value of the distance sub-system,

in order to be a weight value for the journey,

in order to satisfy the sub-values of degree,

in order to have a weight value of the satisfaction degree,

in order to average out the time information,

as the second time information, it is,

in order to obtain the average distance information,

as the information on the second route, it is,

in order to average out the satisfaction information,

is the second satisfaction information. By passing

The time subsystem value can be obtained, if the time subsystem value is larger, the longer the maintenance time is proved, the slower the response and maintenance time is, and the time subsystem value is inversely proportional to the index analysis coefficient. By passing

The distance subsystem value can be obtained, if the distance subsystem value is larger, the maintenance time can be increased by objective reasons such as distance, so that the distance subsystem value is in direct proportion to the index analysis coefficient, but the distance influence factor is smaller, so that the distance weight value can be weighted

Is set to be less than the time weight value

And satisfaction weight value

. By passing

A satisfaction sub-value may be obtained, the greater the satisfaction sub-value, the more satisfied the user is with the maintenance action, so the satisfaction sub-value is directly proportional to the index analysis coefficient at that time.

And S150, if the index analysis coefficient is smaller than an index preset coefficient, generating corresponding processing data based on the second scene label. When the index analysis coefficient is smaller than the index preset coefficient, the scene behavior of the second relevant index set corresponding to the second unit grid is proved to be lower than the average level, so that corresponding processing data is generated according to the second scene label, and the processing data at the moment can be processing data for damage maintenance of the intelligent electric meter.

In one possible implementation manner of the technical solution provided by the present invention, step S150 includes:

and if the index analysis coefficient is smaller than the index preset coefficient, extracting a corresponding second scene label, a corresponding time sub-numerical value and a corresponding satisfaction sub-numerical value. At this time, the present invention will obtain the time sub-coefficient value and the satisfaction sub-coefficient value, because when the index analysis coefficient is smaller than the index preset coefficient, at least one of the time sub-coefficient value and the satisfaction sub-coefficient value is lower than the average coefficient value, which may be the result that the index analysis coefficient is smaller than the index preset coefficient due to the longer maintenance time and the worse satisfaction of the maintenance team.

And comparing the quantity value of the time subsystem numerical value with the satisfaction subsystem numerical value to generate corresponding processing data. The invention compares the quantity value of the time sub-quantity and the satisfaction sub-quantity to determine the corresponding processing data, and the processing data has multiple possibilities, so that the processing data can be correspondingly processed and made up for the analyzed defects of the behavior.

In a possible embodiment, the comparing the time sub-value and the satisfaction sub-value to generate corresponding processing data includes:

if the time sub-numerical value is a positive number, the satisfaction sub-numerical value is a negative number, the time sub-numerical value is greater than the absolute value of the satisfaction sub-numerical value, and the difference value between the time sub-numerical value and the absolute value of the satisfaction sub-numerical value is greater than a preset difference value, an efficiency preset document is called, and time processing data are generated. At the moment, the time length that the time sub-value is greater than the average time length and the satisfaction sub-value is less than the average coefficient value proves that the processing time is longer and the user is not satisfied, so the difference value of the absolute values of the time sub-value and the satisfaction sub-value is calculated at the moment, if the difference value of the absolute values is greater than the preset difference value, the reason that the index analysis coefficient is smaller at the moment is proved to be mainly caused by the fact that the processing time of the maintenance team is longer, so the preset efficiency document is called at the moment, the time processing data is generated, and the corresponding efficiency is improved. The efficiency preset document may be a text, an image, a video, etc., and the content in the efficiency preset document may be preset knowledge having an effect of improving processing efficiency and maintenance efficiency.

If the time sub-numerical value is a positive number, the satisfaction sub-numerical value is a negative number, the time sub-numerical value is smaller than the absolute value of the satisfaction sub-numerical value, and the difference value between the absolute value of the satisfaction sub-numerical value and the time sub-numerical value is larger than a preset difference value, a satisfaction preset document is called to generate satisfaction processing data. At the moment, the time length that the time sub-numerical value is greater than the average time length and the satisfaction sub-numerical value is less than the average coefficient value proves that the processing time is longer and the user is not satisfied, so that the difference value between the absolute value of the satisfaction sub-numerical value and the time sub-numerical value is greater than the preset difference value, if the difference value of the absolute value is greater than the preset difference value, the reason that the index analysis coefficient is smaller at the moment is proved to be mainly caused by user dissatisfaction, so that the satisfaction preset document is called at the moment, satisfaction processing data are generated, and the corresponding satisfaction is improved. The satisfaction preset document can be characters, images, videos and the like, and the content of the satisfaction preset document can be preset knowledge for improving the processing satisfaction, such as a communication mode, a maintenance mode and the like.

If the difference between the absolute value of the time sub-numerical value and the absolute value of the satisfaction sub-numerical value is less than or equal to the preset difference, and the difference between the absolute value of the satisfaction sub-numerical value and the absolute value of the time sub-numerical value is less than or equal to the preset difference, respectively calling an efficiency preset document and a satisfaction preset document to generate time processing data and satisfaction processing data. At this time, the efficiency preset document and the satisfaction preset document have large influence on the index analysis coefficient, so that the corresponding efficiency preset document and the satisfaction preset document are respectively called at this time to obtain time processing data and satisfaction processing data, and learning of shortening the processing time, improving the processing efficiency and improving the satisfaction is performed.

The invention also provides a panoramic index data analysis processing device constructed based on block data, as shown in fig. 3, including:

The present invention also provides a storage medium having a computer program stored therein, the computer program being executable by a processor to implement the methods provided by the various embodiments described above.

The storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media can be any available media that can be accessed by a general purpose or special purpose computer. For example, a storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the storage medium may reside as discrete components in a communication device. The storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, and the like.

The present invention also provides a program product comprising execution instructions stored in a storage medium. The at least one processor of the device may read the execution instructions from the storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.

In the above embodiments of the terminal or the server, it should be understood that the Processor may be a Central Processing Unit (CPU), other general-purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The panoramic index data analysis processing method constructed based on block data is characterized by comprising the following steps of:

2. The block data-based panoramic index data analysis processing method according to claim 1,

the acquiring block data of a previous moment corresponding to a first unit grid, dividing the block data into a plurality of first index information, classifying the plurality of first index information into first associated index sets according to index classification information, wherein each first associated index set corresponds to a first scene tag, and the acquiring method includes:

3. The block data-based panoramic index data analysis processing method according to claim 2,

the calculating the index similarity between the second associated index set and the corresponding first associated index set, and taking the first associated index set with the index similarity greater than the preset similarity as a third associated index set, includes:

4. The block data-based panoramic index data analysis processing method according to claim 3,

the obtaining of the second keyword corresponding to each piece of second index information, counting a first word quantity of the first keyword and a second word quantity of the second keyword in the first association index set, and obtaining a quantity similarity based on the first word quantity and the second word quantity includes:

5. The block data-based panoramic index data analysis processing method according to claim 3,

the method comprises the following steps of counting all first keywords and second keywords with the same dimensionality, and if the first keywords and the second keywords are judged to be main keywords, obtaining main body similarity according to the first keywords and the second keywords, wherein the main body similarity comprises the following steps:

6. The method for analyzing and processing panoramic index data constructed based on block data according to claim 5, wherein,

the method for calculating the first main body sub-coefficient and the second main body sub-coefficient corresponding to the first keyword and the second keyword which are all main body keywords comprises the following steps of:

7. The block data-based panoramic index data analysis processing method according to claim 3,

if the first keyword and the second keyword are judged to be numerical keywords, numerical similarity is obtained according to the first keyword and the second keyword, and the method comprises the following steps:

and calculating according to the numerical difference and the numerical average value to obtain numerical similarity.

8. The method for analyzing and processing panoramic index data constructed based on block data according to any one of claims 3 to 7,

and performing fusion calculation according to the quantity similarity, the body similarity and the numerical similarity to obtain index similarity, wherein the method comprises the following steps:

wherein the content of the first and second substances,

in order to indicate the degree of similarity,

in order to be a measure of the similarity of the quantities,

in order to be a number weight value,

in order to determine the similarity of the main body,

is the weight value of the main body,

in order to be a measure of the similarity of values,

a numerical weight value, a numerical normalization value,

the number of words that are the first number of words,

to the extent that the second number of words,

the values are normalized for the subject and,

is as follows

is as follows

a second coefficient quantity value of a second main body sub-coefficient of the first keyword and the second keyword in the same dimension，

The value is normalized for the value of the value,

is as follows

The value of each of the first key words,

is a first

The value of the second key word is determined,

is as follows

is a constant value.

9. The method for analyzing and processing panoramic index data constructed based on block data according to claim 8, wherein,

respectively extracting second target information and third target information corresponding to the second correlation index set and the third correlation index set, and analyzing and calculating the second target information and the third target information to obtain an index analysis coefficient corresponding to the second scene label, including:

extracting second work order information in the second work order data based on the second target information, wherein the second work order information at least comprises second time information, second route information and second satisfaction information;

10. The block data-based panoramic index data analysis processing method according to claim 9,

the comparing the second time information, the second route information, and the second satisfaction information with the average time information, the average route information, and the average satisfaction information to obtain an index analysis coefficient includes:

11. The block data-based panoramic index data analysis processing method according to claim 10,

the fusion calculation of the time subsystem numerical value, the distance subsystem numerical value and the satisfaction subsystem numerical value to obtain an index analysis coefficient comprises the following steps:

the index analysis coefficient is calculated by the following formula,

wherein the content of the first and second substances,

for the purpose of index analysis of the coefficients,

is a value of the time sub-system,

is a weight value of the time, and is,

for the value of the distance sub-system,

in order to be a weight value for the journey,

in order to satisfy the sub-values of degree,

in order to have a weight value of the satisfaction degree,

in order to average out the time information,

is the second time information that is to be transmitted,

in order to obtain the average distance information,

in order to be the second route information,

in order to average out the satisfaction information,

is the second satisfaction information.

12. The block data-based panoramic index data analysis processing method according to claim 10,

if the index analysis coefficient is smaller than an index preset coefficient, generating corresponding processing data based on the second scene label, wherein the processing data comprises:

13. The block data-based panoramic index data analysis processing method according to claim 12,

the comparing the quantity value of the time sub-quantity value and the satisfaction sub-quantity value to generate corresponding processing data includes:

14. Panorama index data analysis processing system based on block data founds, its characterized in that includes: