CN111325422B - Work order dispatching method and system - Google Patents
Work order dispatching method and system Download PDFInfo
- Publication number
- CN111325422B CN111325422B CN201811531465.4A CN201811531465A CN111325422B CN 111325422 B CN111325422 B CN 111325422B CN 201811531465 A CN201811531465 A CN 201811531465A CN 111325422 B CN111325422 B CN 111325422B
- Authority
- CN
- China
- Prior art keywords
- indexes
- index
- determining
- key
- degree
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000002159 abnormal effect Effects 0.000 claims abstract description 60
- 230000005856 abnormality Effects 0.000 claims abstract description 50
- 238000005516 engineering process Methods 0.000 claims abstract description 25
- 238000012216 screening Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 238000011161 development Methods 0.000 description 28
- 230000018109 developmental process Effects 0.000 description 28
- 238000007726 management method Methods 0.000 description 14
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 10
- 238000004891 communication Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 4
- 238000010219 correlation analysis Methods 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000002457 bidirectional effect Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 235000019633 pungent taste Nutrition 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000012163 sequencing technique Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Telephonic Communication Services (AREA)
Abstract
The embodiment of the invention provides a work order dispatching method and system. The method comprises the following steps: determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology to generate a service rule configuration list; judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index; determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree. Based on big data technology, the problems that personal subjective consciousness is too strong, work arrangement difficulty coefficient is low, overall consciousness does not exist on one side of work arrangement in a personal work customization mode are avoided, the personal workload of a superior director is increased by a superior dispatching work mode, scientific basis is omitted as a reference, and certain unreasonable problems still exist in a dispatched work task are avoided.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data analysis, in particular to a work order dispatching method and system.
Background
The channel manager is taken as an important link of channel terminal management, bears multiple works such as channel management, channel training and information collection, and in the channel manager management function corresponding to the channel operation management system, the work trigger tracking of the channel manager comprises two types of personal work customization and superior dispatch work, so that the work progress of the channel manager can be effectively tracked, and the channel operation management is assisted.
The personal work customizing mode is that a channel manager customizes personal work content by himself, after customization is completed, the work content is completed within the time allowed by the system plan, and the work result is fed back.
The upper-level dispatching work mode is that an upper-level supervisor of a channel manager dispatches work tasks for the channel manager. The method is beneficial to the superior director to fully consider market development situation and global work arrangement, work distribution and progress control are more scientifically carried out, but the personal workload of the superior director is increased to a certain extent, scientific basis is omitted as reference, and certain irrational degree still exists in the distributed work task.
Disclosure of Invention
Aiming at the technical problems in the prior art, the embodiment of the invention provides a work order dispatching method and a work order dispatching system.
In a first aspect, an embodiment of the present invention provides a work order distribution method, including:
determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology to generate a service rule configuration list;
judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index;
determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree.
In a second aspect, an embodiment of the present invention provides a work order dispatch system, including:
the automatic screening parameter configurator is used for determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology so as to generate a service rule configuration list;
the intelligent index filter is used for judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index;
and the work order automatic dispatch device is used for determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and dispatching the work order of the target service according to the emergency degree.
In a third aspect, an embodiment of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method as provided in the first aspect when the program is executed.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as provided by the first aspect.
Compared with the traditional self-arrangement work and leader arrangement work modes, the work order distribution method and system provided by the embodiment of the invention are based on big data technology, integrate various indexes and business development data, can continuously update and optimize along with market development situation, have more pertinence in work distribution, can effectively find existing problems of various channels, assist channel manager work to be more effectively developed, improve the work efficiency of the channel manager work, further improve the platform management function of the channel manager, and also effectively promote the integral improvement of the platform function. The method has the advantages that all layers of data of the existing channel development are fully analyzed, the situation of the channel development and the situation of business development are effectively controlled, when the method is applied, an upper layer leader can effectively control the overall channel development, timely discover the inferior indexes of all channels and the direction to be improved, and the method is more scientific in the aspect of work guidance.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a work order dispatch method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a work order distribution system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an automatic screening parameter configurator according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an intelligent index filter according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a construction of an automatic job ticket dispatcher according to an embodiment of the present invention;
fig. 6 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of a work order dispatching method according to an embodiment of the present invention, as shown in fig. 1, where the method includes:
step 101, determining key indexes of various services and normal threshold ranges of the key indexes based on big data technology to generate a service rule configuration list.
Specifically, the service in the embodiment of the present invention refers to a mobile service, that is, a service provided by each operator, and service types are mainly divided into two major types, namely, a basic service and a value added service. Wherein, the basic service is a service required by each user; the value added service is a service which is opened by an operator aiming at different user groups and market demands and can be selected by users for use on the basis of the mobile basic service. Basic services can be divided into services such as mobile phone shutdown/re-shutdown, loss reporting and unhooking and the like; the value added service can be divided into business such as calling card, mobile phone internet traffic packet, video call, etc. It should be noted that, the service types in the embodiment of the present invention may be only two types, namely basic service and value added service, and may be further divided into the categories of mobile phone shutdown/re-connection, loss reporting and unhooking, calling card, mobile phone internet traffic packet, video call, etc.
There are multiple metrics for each class of traffic, for example, a video call traffic may include the following metrics: the video call completing rate, the video call dropping rate, etc., which are not particularly limited in the embodiment of the present invention. For each type of service, the quality of the service can be determined by the index, specifically, whether the index is normal or not, and whether the index is normal or not can be determined by the normal threshold range of the index, namely, if the value of the index is within the normal threshold range of the index, the index is determined to be normal, otherwise, the index is abnormal.
Because the indexes of each type of service are numerous, and some indexes which are less important exist in the indexes, the indexes which are not important need to be filtered, and for convenience in description, the important indexes are called key indexes in the embodiment of the invention. The embodiment of the invention acquires all indexes of the service based on the big data technology, and determines key indexes from all indexes.
Recording the key indexes and the normal threshold ranges thereof to generate a service rule configuration list, wherein the service rule configuration list is used for recording the key indexes of various services and the normal threshold ranges of the key indexes and is mainly used for providing a judging standard for judging the quality of the service.
Step 102, based on the service rule configuration list, determining whether the acquired key index of the target service is abnormal, so as to determine an abnormal index.
Specifically, the service rule configuration list can provide a judging standard for judging whether the service quality is good or not, if the quality of a certain service (namely, a target service) is required to be judged, part or all indexes of the target service can be extracted, and the key indexes of the target service and whether the key indexes are abnormal or not are determined by comparing the service rule configuration list, so that abnormal indexes are determined.
Step 103, determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree.
Specifically, among all the obtained key indexes of the target service, determining the anomaly degree and the number of the anomaly indexes, determining the emergency degree of the target service according to the anomaly degree and the number, and generating and distributing a work order of the target service according to the emergency degree.
Compared with the traditional self-arrangement work and leader arrangement work modes, the method provided by the embodiment of the invention integrates various indexes and business development data based on big data technology, can continuously update and optimize along with market development situation, has more pertinence in work distribution, can effectively find existing problems of various channels, assists channel manager work to be more effectively developed, improves the work efficiency, further improves the platform management function of the channel manager, and also plays an effective promotion role in the integral improvement of the platform function. The method has the advantages that all layers of data of the existing channel development are fully analyzed, the situation of the channel development and the situation of business development are effectively controlled, when the method is applied, an upper layer leader can effectively control the overall channel development, timely discover the inferior indexes of all channels and the direction to be improved, and the method is more scientific in the aspect of work guidance.
On the basis of the above embodiments, the embodiments of the present invention further include:
and tracking the execution progress of the work order.
Specifically, when the channel manager executes the task in the work order, the embodiment of the invention reports the execution progress in real time or periodically, thereby realizing the tracking of the execution progress of the work order.
On the basis of the above embodiments, based on big data technology, determining key indexes of various services and normal threshold ranges of the key indexes to generate a service rule configuration list includes:
and constructing an index heat identification model based on a big data technology.
Specifically, the index heat recognition model can be constructed through a big data technology.
And determining related indexes and the heat degree of each related index from indexes of various services based on the index heat degree identification model.
Specifically, the index heat degree identification model can acquire various services and part or all indexes of the various services, and for each type of service, the related index and the heat degree of the related index can be determined from all the acquired indexes of the service. The relevant index is the index remaining after filtering all the acquired indexes of the service.
And determining key indexes and normal threshold ranges of the key indexes from the related indexes based on the heat of the related indexes so as to generate a business rule configuration list.
Specifically, the relevant indexes are ranked according to the order of the heat of the relevant indexes from high to low, the relevant indexes with the front ranking can be selected to form key indexes, and the normal threshold range of the key indexes is obtained, so that a service rule configuration list is generated.
On the basis of the above embodiments, determining, based on the index heat recognition model, a relevant index and heat of each relevant index from indexes of various services includes:
and reading a system operation log based on the index heat identification model.
Analyzing the operation log, and determining indexes of various services and frequency information of each index.
The frequency information includes a usage frequency, a focus frequency, and a collection frequency.
And determining related indexes from the indexes of the various services based on the attribute information of the indexes of the various services.
Specifically, a metric value is calculated through attribute correlation analysis, correlation among indexes is quantized, analysis characteristics and analysis comparison are carried out, irrelevant and weak correlation indexes are deleted through the selected correlation analysis metrics, and the remaining indexes are used as correlation indexes.
And determining the heat degree of each related index according to the frequency information of each related index.
Specifically, the scores of the relevant indexes are calculated, and the scores of the relevant indexes are calculated according to weights (configurable) X (1- (Ni-1)/Nn) (wherein X is the total score of indexes such as the attention frequency, the collection frequency, the use frequency and the like of the indexes, the score can be configured, the score can be made in a ten-system or a percent-system, ni is the ranking of the indexes when the indexes are ranked according to the attention frequency, the collection frequency and the use frequency, and Nn is the total ranking of the indexes when all the indexes are ranked according to the attention frequency, the collection frequency and the use frequency).
That is, index attention score=index attention score (1- (index attention number ranking-1))/index attention number total rank; index collection score = index collection score (1- (index collection number ranking-1))/index collection number total rank; index usage score = index usage score (1- (index usage ranking-1))/index usage ranking total.
Calculating the total score of each related index, wherein the formula is as follows: total score = index attention score + index reserve score + index use score, the total score is taken as the index hotness of the relevant index.
Based on the above embodiments, based on the service rule configuration list, determining whether the obtained key indicator of the target service is abnormal, to determine an abnormal indicator includes:
and determining key indexes matched with the service rule configuration list from the acquired indexes of the target service based on the service rule configuration list.
Specifically, the service rule configuration list only stores the key indexes and the corresponding normal threshold ranges, and the obtained target service indexes are numerous, and some indexes may not be stored in the service rule configuration list, so that the service rule configuration list needs to be checked first, the key indexes matched with the service rule configuration list are determined from the obtained target service indexes, that is, the indexes existing in the service rule configuration list are determined and used as the key indexes of the target service.
And comparing the value of the key index of the target service with the normal threshold range of the corresponding key index in the service rule configuration list, and determining an abnormal index according to the comparison result.
On the basis of the above embodiments, comparing the value of the key index of the target service with the normal threshold range of the corresponding key index in the service rule configuration list, and determining the abnormal index according to the comparison result, including:
and if the value of the key index of the target service is smaller than the lower limit of the normal threshold range of the corresponding key index in the service rule configuration list or larger than the upper limit of the normal threshold range, marking the key index.
Specifically, if there are 10 key indexes of the target service, the 10 key indexes are numbered A, B, C, and if the 3 key indexes A, B, C meet the condition of being smaller than the lower limit of the normal threshold range or greater than the upper limit of the normal threshold range of the corresponding key indexes in the service rule configuration list, the 3 key indexes are marked A, B, C.
And determining the number of marked key indexes of the target service, determining the ratio of the marked key indexes to the number of all the key indexes of the target service according to the number, and taking the marked key indexes as abnormal indexes if the ratio is smaller than or equal to a ratio threshold value.
Specifically, the number of the marked key indexes of the target service is 3, the number of all the key indexes of the target service is 10, and the ratio is 0.3. If the ratio threshold is 0.5, since 0.3 is smaller than 0.5, all of the 3 key indexes A, B, C are used as abnormality indexes.
Otherwise, resetting the normal threshold range and comparing again to determine the abnormality index.
For example, if the ratio is 0.6, since 0.6 is greater than 0.5, the normal threshold range is reset, and then the comparison process is performed again to determine the abnormality index.
On the basis of the above embodiments, determining the abnormality degree and the number of the abnormality indexes, and determining the urgency degree of the target service according to the abnormality degree and the number includes:
if the abnormality degree of any abnormality index of the target service is greater than an abnormality degree threshold, or the number of the abnormality indexes of the target service is greater than a first number threshold, determining the urgency degree of the target service as a first level;
if the number of the target businesses is larger than a second number threshold value, determining the urgency of the target businesses as a second level;
otherwise, determining the urgency of the target service as three stages.
For example, for all abnormal indexes of the target service, if a single abnormal index deviates from 6 (configurable) standard deviations or more or the number of the abnormal indexes of the target service is equal to the number of key indexes, the emergency degree is first-level, and red early warning is carried out; the emergency degree is secondary and the emergency degree is yellow early warning when 60% or more of the key indexes of the target service are abnormal indexes; the other emergency degree is three-level, namely orange early warning.
The system provided in the embodiments of the present invention specifically executes the flow of each embodiment of the method, and specific please refer to the content of each embodiment of the method, which is not described herein again. Compared with the traditional self-arrangement work and leader arrangement work modes, the system provided by the embodiment of the invention integrates various indexes and business development data based on big data technology, can continuously update and optimize along with market development situation, has more pertinence in work distribution, can effectively find existing problems of various channels, assists channel manager work to be more effectively developed, improves the work efficiency, further improves the platform management function of the channel manager, and also has an effective promotion effect on the integral improvement of the platform function. The method has the advantages that all layers of data of the existing channel development are fully analyzed, the situation of the channel development and the situation of business development are effectively controlled, when the method is applied, an upper layer leader can effectively control the overall channel development, timely discover the inferior indexes of all channels and the direction to be improved, and the method is more scientific in the aspect of work guidance.
Fig. 2 is a schematic structural diagram of a work order distribution system according to an embodiment of the present invention, where, as shown in fig. 2, the system includes:
an automatic screening parameter configurator 201, configured to determine key indexes of various services and normal threshold ranges of the key indexes based on big data technology, so as to generate a service rule configuration list;
an intelligent index filter 202, configured to determine whether the obtained key index of the target service is abnormal based on the service rule configuration list, so as to determine an abnormal index;
and the work order automatic dispatcher 203 is configured to determine the degree of abnormality and the number of the abnormality indexes, determine the urgency of the target service according to the degree of abnormality and the number, and generate and dispatch the work order of the target service according to the urgency.
The automatic screening parameter configurator 201, the intelligent index filter 202, and the work order automatic dispatcher 203 are specifically described below:
autofilter parameter configurator 201:
fig. 3 is a schematic structural diagram of an automatic screening parameter configurator according to an embodiment of the present invention, as shown in fig. 3, which includes an index heat recognition model, an index automatic collector, an index pool, and a parameter configurator.
Firstly, constructing an index heat identification model by a big data technology, automatically collecting frequency information such as the use frequency, the attention frequency, the collection frequency and the like of related indexes of various businesses of a system by the model, and judging and outputting the heat of each related index by the model; then, an index automatic collector automatically collects key indexes according to the heat conditions of all related indexes, and inputs the key indexes into an index pool; and finally, extracting key indexes from the index pool by a parameter configurator and determining a normal threshold range so as to form each business rule configuration list. Optionally, the service rule configuration list may include not only the key indicators of the class service and the normal threshold range of each key indicator, but also the unit attribution, personnel, data period, and the like of each key indicator.
The index heat recognition model algorithm is as follows:
step 1: reading a system operation log;
step 2: analyzing the log to generate a word segmentation list, and analyzing operation types, operation times and the like;
step 3: comparing the parsed keywords with the data model, and marking out index word segmentation;
step 4: summarizing indexes, namely summarizing index frequencies according to attribution units, operators and operation categories, wherein the index frequencies comprise use frequencies, attention frequencies and collection frequencies;
step 5: and sequencing the summarized data, sequencing from high to low according to the index use frequency, the attention frequency and the collection frequency, and marking sequence numbers.
Step 6: aiming at the sorted indexes, calculating a metric value through attribute correlation analysis, quantifying the correlation among the indexes, carrying out analysis characteristics and analysis comparison, deleting irrelevant and weak correlation indexes by using the selected correlation analysis metrics, and reserving the most relevant indexes;
step 7: calculating the scores of all related indexes according to weights (configurable) X (1- (Ni-1)/Nn) (wherein X is the total score of indexes such as the attention frequency, the collection frequency, the use frequency and the like of the indexes, the score can be configured, the score can be made in a ten-system or a percent-system, ni is the ranking of the indexes when the indexes are ranked according to the attention frequency, the collection frequency and the use frequency, and Nn is the total ranking of all the indexes when the indexes are ranked according to the attention frequency, the collection frequency and the use frequency). Index attention score = index attention score (1- (index attention number ranking-1))/index attention number total rank; index collection score = index collection score (1- (index collection number ranking-1))/index collection number total rank; index usage score = index usage score (1- (index usage ranking-1))/index usage ranking;
step 8: calculating the total score of each related index, wherein the formula is as follows: total score = index attention score + index reserve score + index use score, the total score is taken as the index hotness of the relevant index.
Key technical points in the autofilter parameter configurator 201:
1) Constructing a system index heat identification model by a big data modeling technology, automatically collecting various indexes of the system by the model, and determining related indexes;
2) Extracting key indexes from the index pool by a parameter configurator, determining a normal threshold range, and forming a business rule configuration list;
intelligent index filter 202:
fig. 4 is a schematic structural diagram of an intelligent index filter according to an embodiment of the present invention, as shown in fig. 4, which includes a data index engine and an index dynamic filter.
The data index engine automatically extracts service period data according to the service rule configuration list output by the automatic screening parameter configurator 201, and realizes bidirectional fusion of the service period data and the rule configuration list to form automatic matching intermediate data of index rules and data; and then the index dynamic filter intelligently judges the data indexes of different services, determines the abnormal indexes and outputs the values of the abnormal indexes.
Wherein the index dynamic filter algorithm is as follows:
step 1: reading business and rule matching data;
step 2: the value of the service index is automatically compared with the normal threshold range of the corresponding index in the rule, and if the value of the service index is larger than the upper limit set by the rule or smaller than the lower limit set by the rule, an abnormal mark is set;
step 3: calculating the abnormal data duty ratio of the index data, if the duty ratio is smaller than or equal to N (configurable), executing the step 5, otherwise executing the step 4;
step 4: recalculating rule abnormal threshold values, and calculating the rule abnormal threshold values by the following steps: maximum = index mean +3 standard deviations; minimum value=index mean value-3 standard deviations, and step 2 is skipped;
step 5: and outputting an abnormality index and a value thereof.
Key technical points in the intelligent index filter 202:
1) The data index engine automatically extracts the service period data according to the service rule configuration list output by the automatic screening parameter configurator 201, and realizes bidirectional fusion of the service period data and the rule configuration list.
2) The index dynamic filter intelligently judges the data indexes of different services, determines the abnormal indexes and outputs the values of the abnormal indexes.
Work order automatic dispatcher 203:
fig. 5 is a schematic structural diagram of an automatic worksheet dispatcher according to an embodiment of the present invention, and as shown in fig. 5, the automatic worksheet dispatcher includes a classification identifier, a worksheet dispatcher, and a worksheet progress tracker.
Specifically, the classification identifier classifies and classifies the abnormal indexes output by the intelligent index filter 202 according to the service rule configuration list output by the automatic screening parameter configurator 201 to generate three-level abnormal states of red, yellow and orange, corresponding to three types of urgent, general and concerned worksheets; after receiving the classified and graded abnormal data, the work order dispatcher queries the user information table, and generates an abnormal work order according to the abnormal type of the data to automatically dispatch (dispatch modes: mail, short message and system work order reminding). In addition, the system also comprises a work order progress tracker which is started to track the work order progress while the work order is dispatched.
Wherein the classification hierarchical identifier algorithm is as follows:
step 1: reading an abnormal data list input by the index dynamic filter;
step 2: classifying the abnormal data according to attribution, personnel and business categories;
step 3: calculating the number of abnormal indexes in each category and the degree (the degree is expressed by an index standard deviation) of the abnormal indexes exceeding a threshold value;
step 4: the business abnormality is classified according to the number of the abnormal indexes and the abnormality degree of the abnormal indexes, and the classification method is as follows: if the single index abnormality deviates from 6 (configurable) standard deviations or more or all indexes of the service are abnormal, red early warning is carried out, 60% or more of the indexes in the service are marked as yellow early warning, and the other indexes are all orange early warning.
Step 5: and outputting the classified and graded abnormal data.
Key technical points in the automatic work order dispatcher 203 are as follows:
1) The classification and grading identifier classifies and grades the abnormal data output in the second step according to the service rule configuration list output by the self-help screening parameter configuration in the first step to generate red-yellow orange three-level abnormality;
2) After receiving the classified and graded abnormal data, the work order dispatcher queries a user information table, and generates an abnormal work order according to the abnormal type of the data to automatically dispatch;
3) The work order progress tracker tracks work order progress.
In summary, the method and the system provided by the embodiment of the invention have the following beneficial effects:
1. the pertinence of channel manager work is enhanced, and the working efficiency is improved.
Compared with the traditional self-arrangement work and leader arrangement work modes, the mode is modeled through big data of the system, and meanwhile, the mode is closely combined with the existing market development situation, work distribution is more targeted, the existing problems of each channel can be effectively found, channel manager work is assisted to be effectively developed, and the work efficiency of the channel manager is improved.
2. Leading the overall control consciousness to be improved, and leading the work guidance to be more scientific
The mode fully analyzes the data of each layer of the development of the existing channels, covers the effective control of the development situation and the service development situation of each channel, and when the method is applied, an upper layer leader can effectively control the overall development situation of the channels, discover the disadvantage indexes of each channel and the direction to be lifted in time, and is more scientific in the aspect of work guidance.
3. Channel stability is further improved
The channel manager is taken as an important link of channel terminal management, bears multiple works such as channel management, channel training, information collection and the like, and the promotion degree of the works plays an important role in the maintenance and development of the channel.
4. Fully apply big data analysis technology and effectively promote platform function
The method fully applies the big data modeling technology, merges various indexes and business development data, can continuously update and optimize along with market development situation, further improves the platform management function of the channel manager while effectively helping the development of the channel manager work, and also plays an effective promoting role in the integral improvement of the platform function.
5. The applicability and the expandability are strong, and the method can be fully applied to other platforms
The method fully solves the problems of low monitoring index configuration and work dispatching and tracking efficiency, inflexible index configuration and difficult service change adaptation in the management system, can be fully expanded to other work dispatching platforms after being effectively applied in the aspect of channel manager management, and can assist effective promotion of other management platforms.
Fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, where, as shown in fig. 6, the electronic device may include: processor 601, communication interface (Communications Interface) 602, memory 603 and communication bus 604, wherein processor 601, communication interface 602, memory 603 complete the communication between each other through communication bus 604. The processor 601 may invoke a computer program stored in the memory 603 and executable on the processor 601 to perform the methods provided by the above embodiments, for example comprising: determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology to generate a service rule configuration list; judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index; determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree.
Further, the logic instructions in the memory 603 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art or a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the transmission method provided in the above embodiments, for example, including: determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology to generate a service rule configuration list; judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index; determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment of the invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method of dispatch of a work order, comprising:
determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology to generate a service rule configuration list;
judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index;
determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and distributing a work order of the target service according to the emergency degree;
the determining key indexes of various services and normal threshold ranges of the key indexes based on the big data technology to generate a service rule configuration list comprises the following steps:
constructing an index heat recognition model based on a big data technology;
based on the index heat degree identification model, determining related indexes and the heat degree of each related index from indexes of various services;
determining key indexes and normal threshold ranges of the key indexes from the related indexes based on the heat of the related indexes so as to generate a business rule configuration list;
wherein, the related indexes are the indexes which are remained after all the acquired indexes of various services are filtered;
based on the index heat degree identification model, determining the related index and the heat degree of each related index from indexes of various services comprises the following steps:
reading a system operation log based on the index heat identification model;
analyzing the operation log to determine indexes of various services and frequency information of each index;
determining related indexes from the indexes of various services based on attribute information of the indexes of the various services;
determining the heat degree of each related index according to the frequency information of each related index;
wherein the frequency information includes a usage frequency, a focus frequency, and a collection frequency; the popularity of each related index is determined by an index attention score, an index collection score and an index use score.
2. The method as recited in claim 1, further comprising:
and tracking the execution progress of the work order.
3. The method of claim 1, wherein determining whether the obtained key indicator of the target service is abnormal based on the service rule configuration list to determine an abnormal indicator comprises:
determining key indexes matched with the service rule configuration list from the acquired indexes of the target service based on the service rule configuration list;
and comparing the value of the key index of the target service with the normal threshold range of the corresponding key index in the service rule configuration list, and determining an abnormal index according to the comparison result.
4. A method according to claim 3, wherein comparing the value of the key indicator of the target service with the normal threshold range of the corresponding key indicator in the service rule configuration list, and determining the abnormal indicator according to the comparison result comprises:
if the value of the key index of the target service is smaller than the lower limit of the normal threshold range of the corresponding key index in the service rule configuration list or larger than the upper limit of the normal threshold range, marking the key index;
determining the number of marked key indexes of the target service, determining the ratio of the marked key indexes to the number of all key indexes of the target service according to the number, and taking the marked key indexes as abnormal indexes if the ratio is smaller than or equal to a ratio threshold;
otherwise, resetting the normal threshold range and comparing again to determine the abnormality index.
5. The method of claim 1, wherein determining the degree of abnormality and the number of abnormality indicators, and determining the urgency of the target service based on the degree of abnormality and the number, comprises:
if the abnormality degree of any abnormality index of the target service is greater than an abnormality degree threshold, or the number of the abnormality indexes of the target service is greater than a first number threshold, determining the urgency degree of the target service as a first level;
if the number of the target businesses is larger than a second number threshold value, determining the urgency of the target businesses as a second level;
otherwise, determining the urgency of the target service as three stages.
6. A work order dispatch system, comprising:
the automatic screening parameter configurator is used for determining key indexes of various services and normal threshold ranges of the key indexes based on a big data technology so as to generate a service rule configuration list;
the intelligent index filter is used for judging whether the acquired key index of the target service is abnormal or not based on the service rule configuration list so as to determine an abnormal index;
the work order automatic dispatcher is used for determining the abnormality degree and the number of the abnormality indexes, determining the emergency degree of the target service according to the abnormality degree and the number, and generating and dispatching the work order of the target service according to the emergency degree;
the determining key indexes of various services and normal threshold ranges of the key indexes based on the big data technology to generate a service rule configuration list comprises the following steps:
constructing an index heat recognition model based on a big data technology;
based on the index heat degree identification model, determining related indexes and the heat degree of each related index from indexes of various services;
determining key indexes and normal threshold ranges of the key indexes from the related indexes based on the heat of the related indexes so as to generate a business rule configuration list;
wherein, the related indexes are the indexes which are remained after all the acquired indexes of various services are filtered;
based on the index heat degree identification model, determining the related index and the heat degree of each related index from indexes of various services comprises the following steps:
reading a system operation log based on the index heat identification model;
analyzing the operation log to determine indexes of various services and frequency information of each index;
determining related indexes from the indexes of various services based on attribute information of the indexes of the various services;
determining the heat degree of each related index according to the frequency information of each related index;
wherein the frequency information includes a usage frequency, a focus frequency, and a collection frequency; the popularity of each related index is determined by an index attention score, an index collection score and an index use score.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 5 when the program is executed.
8. A non-transitory computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811531465.4A CN111325422B (en) | 2018-12-14 | 2018-12-14 | Work order dispatching method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811531465.4A CN111325422B (en) | 2018-12-14 | 2018-12-14 | Work order dispatching method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111325422A CN111325422A (en) | 2020-06-23 |
| CN111325422B true CN111325422B (en) | 2023-10-27 |
Family
ID=71172371
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811531465.4A Active CN111325422B (en) | 2018-12-14 | 2018-12-14 | Work order dispatching method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111325422B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381451A (en) * | 2020-12-03 | 2021-02-19 | 苏州工业园区测绘地理信息有限公司 | Production potential safety hazard monitoring and closed-loop disposal method |
| CN114971108A (en) * | 2021-02-25 | 2022-08-30 | 中国移动通信集团广东有限公司 | Order dividing method and device |
| CN112884328A (en) * | 2021-02-26 | 2021-06-01 | 上海东普信息科技有限公司 | Piece-seizing salesman recommendation method, device, equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7647391B1 (en) * | 2001-09-04 | 2010-01-12 | At&T Intellectual Property, I,L.P. | Processes and systems for creating and for managing trouble tickets and work orders |
| CN104156822A (en) * | 2014-08-11 | 2014-11-19 | 国家电网公司 | SOA-based comprehensive enterprise level information system operation and maintenance management method |
| CN104270787A (en) * | 2014-10-17 | 2015-01-07 | 四川公用信息产业有限责任公司 | Full-automatic closed-loop control method of wireless network work order |
| CN106651161A (en) * | 2016-12-08 | 2017-05-10 | 国网浙江杭州市富阳区供电公司 | Acquisition operation and maintenance and dynamic tasking method |
| CN106815373A (en) * | 2017-02-10 | 2017-06-09 | 国网山东省电力公司 | Big data methods of exhibiting and system are rushed to repair in distribution based on BI analyses |
| CN107729919A (en) * | 2017-09-15 | 2018-02-23 | 国网山东省电力公司电力科学研究院 | In-depth based on big data technology is complained and penetrates analysis method |
| CN107809322A (en) * | 2016-09-06 | 2018-03-16 | 中兴通讯股份有限公司 | The distribution method and device of work order |
| CN107944761A (en) * | 2017-12-18 | 2018-04-20 | 广东广业开元科技有限公司 | Early warning and monitoring analysis method is complained based on artificial intelligence protection of consumers' rights index enterprise |
| CN108897754A (en) * | 2018-05-07 | 2018-11-27 | 广东省电信规划设计院有限公司 | Recognition methods, system and the calculating equipment of work order type based on big data |
-
2018
- 2018-12-14 CN CN201811531465.4A patent/CN111325422B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7647391B1 (en) * | 2001-09-04 | 2010-01-12 | At&T Intellectual Property, I,L.P. | Processes and systems for creating and for managing trouble tickets and work orders |
| CN104156822A (en) * | 2014-08-11 | 2014-11-19 | 国家电网公司 | SOA-based comprehensive enterprise level information system operation and maintenance management method |
| CN104270787A (en) * | 2014-10-17 | 2015-01-07 | 四川公用信息产业有限责任公司 | Full-automatic closed-loop control method of wireless network work order |
| CN107809322A (en) * | 2016-09-06 | 2018-03-16 | 中兴通讯股份有限公司 | The distribution method and device of work order |
| CN106651161A (en) * | 2016-12-08 | 2017-05-10 | 国网浙江杭州市富阳区供电公司 | Acquisition operation and maintenance and dynamic tasking method |
| CN106815373A (en) * | 2017-02-10 | 2017-06-09 | 国网山东省电力公司 | Big data methods of exhibiting and system are rushed to repair in distribution based on BI analyses |
| CN107729919A (en) * | 2017-09-15 | 2018-02-23 | 国网山东省电力公司电力科学研究院 | In-depth based on big data technology is complained and penetrates analysis method |
| CN107944761A (en) * | 2017-12-18 | 2018-04-20 | 广东广业开元科技有限公司 | Early warning and monitoring analysis method is complained based on artificial intelligence protection of consumers' rights index enterprise |
| CN108897754A (en) * | 2018-05-07 | 2018-11-27 | 广东省电信规划设计院有限公司 | Recognition methods, system and the calculating equipment of work order type based on big data |
Non-Patent Citations (2)
| Title |
|---|
| FDD/TDD LTE无线网络智能优化作业工单系统;郭致毅;;电信技术(03);全文 * |
| 大数据分析技术在采集运维业务中的应用研究;曹永峰;翟峰;岑炜;孙志强;程春光;;电力信息与通信技术(05);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111325422A (en) | 2020-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110417721A (en) | Safety risk estimating method, device, equipment and computer readable storage medium | |
| CN111325422B (en) | Work order dispatching method and system | |
| US10637990B1 (en) | Call center load balancing and routing management | |
| CN108170580A (en) | A kind of rule-based log alarming method, apparatus and system | |
| CN107403326A (en) | A kind of Insurance Fraud recognition methods and device based on teledata | |
| CN105184315A (en) | Quality inspection treatment method and system | |
| CN105719045A (en) | Retention risk determiner | |
| CN111127105A (en) | User hierarchical model construction method and system, and operation analysis method and system | |
| US11716422B1 (en) | Call center load balancing and routing management | |
| CN109767227A (en) | The system and method for payment risk intelligent decision and control is realized by RDS | |
| CN114817681B (en) | Financial wind control system based on big data analysis and management equipment thereof | |
| CN110262919A (en) | Abnormal data analysis method, device, equipment and computer readable storage medium | |
| CN110533525A (en) | For assessing the method and device of entity risk | |
| CN115719283A (en) | Intelligent accounting management system | |
| CN113609393B (en) | Digital platform based on data service and data management | |
| CN113434575A (en) | Data attribution processing method and device based on data warehouse and storage medium | |
| CN113077196A (en) | Dispute event processing method and device, readable storage medium and computer equipment | |
| Wang | Research on bank marketing behavior based on machine learning | |
| CN110706122A (en) | Method, device and readable medium for improving social security agent efficiency based on big data analysis | |
| CN112712270B (en) | Information processing method, device, equipment and storage medium | |
| CN112598225A (en) | Evaluation index determination method and apparatus, storage medium, and electronic apparatus | |
| CN114492877B (en) | Operation and maintenance analysis method and device of business system | |
| CN104079627B (en) | Send the method and apparatus for showing information | |
| CN117094688B (en) | Digital control method and system for power supply station | |
| CN114997879B (en) | Payment routing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |