CN111325422A - Work order distribution method and system - Google Patents
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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; determining whether the obtained key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes; determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the work order of the target service according to the emergency degree. Based on the big data technology, the problems that the individual work customization mode has over-strong individual subjective consciousness, the work arrangement difficulty coefficient is low, no global consciousness exists on the one side of the work arrangement and the like are solved, the problem that the upper level assignment work mode increases the personal workload of the upper level supervisor is also solved, the scientific basis is lost as the reference, and the assignment work task still possibly has certain irrationality.
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 used as an important link of channel tip management, and bears a plurality of tasks 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 triggering and tracking of the channel manager comprises two types of personal work customization and superior distribution work, so that the work progress of the channel manager can be effectively tracked, and the channel operation management is assisted.
The individual work customizing mode is used for customizing individual work content for a channel manager, after the customization is completed, the work content is completed within the time allowed by a system plan and the work result is fed back.
The method comprises the steps that a superior supervisor of a channel manager dispatches work tasks for the channel manager, firstly, the superior supervisor selects a target channel, a system is associated to a sub-packaged channel manager according to the selected channel, and after receiving the dispatched work tasks, the channel manager completes the work tasks within the time allowed by the system customization and feeds back the work results. The mode is beneficial to the superior supervisor to fully consider the market development situation and the global work arrangement, more scientifically carry out work distribution and progress control, but simultaneously increase the personal workload of the superior supervisor to a certain extent, and lack scientific basis as reference, and the distributed work task still possibly has certain irrationality.
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 dispatching 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;
determining whether the obtained key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes;
determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the 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 dispatching 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 determining whether the acquired key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes;
and the work order automatic dispatcher is used for determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, generating the work order of the target service according to the emergency degree and dispatching the work order.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
Compared with the traditional self-arrangement work and leader arrangement work mode, the work order distribution method and the work order distribution system provided by the embodiment of the invention are based on the big data technology, integrate all indexes and service development data, can continuously update and optimize along with the market development situation, have more pertinence in work distribution, can effectively find the existing problems of all channels, assist channel managers to more effectively develop work, improve the work efficiency, further improve the platform management function of the channel managers, and effectively promote the overall improvement of the platform function. The data of each layer of the existing channel development is fully analyzed, effective control on the development situation and the service development condition of each channel is covered, and the leader on the upper layer can effectively control the overall development condition of the channel and timely find the inferior indexes of each channel and the direction to be promoted when the method is applied, so that the method is more scientific in the aspect of work guidance.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a work order dispatching method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a work order dispatching system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an autofilter parameter configurator according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an intelligent indicator filter according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of an automatic work order dispatcher according to an embodiment of the present invention;
fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
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 some, but not all, embodiments of the present invention. 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.
Fig. 1 is a flowchart of a work order dispatching method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
Specifically, the service in the embodiment of the present invention refers to a mobile service, that is, a service provided by each operator, and the service categories are mainly divided into two major categories, namely, a basic service and a value-added service. Wherein, the basic service is the service required by each user; the value added service is a service which is provided by an operator on the basis of the mobile basic service and can be selected and used by users aiming at different user groups and market requirements. The basic services can be divided into services such as mobile phone shutdown/recovery, loss report and hang-up release and the like; the value added service can be divided into business card, mobile phone internet traffic packet, video call and other services. It should be noted that the service types in the embodiment of the present invention may be only two types, namely, a basic service and a value added service, and may also be subdivided into the types of a mobile phone shutdown/reset, a loss report release, an incoming call name card, a mobile phone internet traffic packet, a video call, and the like.
There are multiple indicators for each type of service, for example, a video call service may include the following indicators: a video call completing rate, a video call drop rate, and the like, which are not specifically limited in the embodiment of the present invention. For each type of service, the quality of the service can be judged by the index, specifically, whether the index is normal or not is judged, and whether the index is normal or not can be determined by the normal threshold range of the index, that is, if the value of the index is in the normal threshold range of the index, the index is judged to be normal, otherwise, the index is abnormal.
Since there are many indexes of each type of service, and some less important indexes exist in the indexes, the unimportant indexes need to be filtered out, and for convenience of description, the important indexes are referred to as key indexes in the embodiments of the present invention. The embodiment of the invention is based on big data technology, obtains all indexes of the service, and determines key indexes from all indexes.
The key indexes and the normal threshold ranges thereof are recorded, so that a service rule configuration list is generated, and it should be noted that 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 judgment standard for judging the quality of the services.
And 102, judging whether the acquired key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes.
Specifically, the service rule configuration list can provide a determination standard for determining the quality of a service, and if the quality of a certain service (i.e., a target service) needs to be determined, part or all of the indexes of the target service can be extracted, and the key indexes of the target service are determined to be abnormal or not by referring to the service rule configuration list, so as to determine abnormal indexes.
103, determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the work order of the target service according to the emergency degree.
Specifically, the abnormal degree and the number of abnormal indexes are determined in all the obtained key indexes of the target service, the emergency degree of the target service is determined according to the abnormal degree and the number, and a work order of the target service is generated and distributed according to the emergency degree.
Compared with the traditional self-arrangement work and leader arrangement work mode, the mode provided by the embodiment of the invention is based on the big data technology, integrates all indexes and service development data, can be continuously updated and optimized along with the market development situation, has pertinence in work distribution, can effectively find the existing problems of all channels, assists the channel manager to more effectively develop the work, improves the work efficiency, further improves the platform management function of the channel manager, and has an effective promoting effect on the integral promotion of the platform function. The data of each layer of the existing channel development is fully analyzed, effective control on the development situation and the service development condition of each channel is covered, and the leader on the upper layer can effectively control the overall development condition of the channel and timely find the inferior indexes of each channel and the direction to be promoted when the method is applied, so that 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 tasks in the work order, the execution progress can be reported in real time or regularly, so that the execution progress of the work order is tracked.
On the basis of the above embodiments, based on the big data technology, determining key indexes of various services and normal threshold ranges of the key indexes to generate a service rule configuration list, including:
and constructing an index heat identification model based on a big data technology.
Specifically, the index heat identification model can be constructed through a big data technology.
And determining related indexes and the heat of each related index from the indexes of various services based on the index heat identification model.
Specifically, the index heat identification model can acquire various services and part or all indexes of the various services, and for each service, relevant indexes and heat of the relevant indexes can be determined from all the acquired indexes of the service. It should be noted that the relevant indexes are indexes remaining after filtering all the acquired indexes of the service.
And determining key indexes and normal threshold ranges of the key indexes from the relevant indexes based on the heat degree of the relevant indexes to generate a business rule configuration list.
Specifically, according to the sequence of the heat degree of each relevant index from high to low, the relevant indexes are sorted, the relevant indexes in the top of the sorting can be selected to form a key index, and the normal threshold range of the key index is obtained, so that a service rule configuration list is generated.
On the basis of the above embodiments, determining the relevant indexes and the heat of each relevant index from the indexes of each type of service based on the index heat recognition model includes:
and reading a system operation log based on the index heat identification model.
And analyzing the operation log, and determining indexes of various services and frequency information of the indexes.
It should be noted that the frequency information includes a use frequency, an attention 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, metric values are calculated through attribute correlation analysis, correlation among all indexes is quantized, analysis characteristics and analysis comparison are carried out, irrelevant and weak relevant indexes are deleted through the selected correlation analysis metric, and the remaining indexes are used as relevant indexes.
And determining the heat degree of each relevant index according to the frequency information of each relevant index.
Specifically, the score of each relevant index is calculated according to the weight (configurable) X (1- (Ni-1)/Nn) (wherein X is the total score of indexes such as the attention times, the collection times, the use times and the like, the score is configurable, the value can be taken in a ten system or a percentage system, Ni is the ranking of the indexes according to the attention times, the collection times and the use times respectively, and Nn is the total ranking of all the indexes according to the attention times, the collection times and the use times respectively).
Namely, the index attention score is index attention score (1- (index attention number ranking-1))/index attention number total ranking; the index collection score is the index collection score (1- (index collection times name-1))/index collection times total ranking; the index usage score ═ index usage score × (1- (index usage times name-1))/index usage times total ranking.
Calculating the total score of each relevant index, wherein the formula is as follows: the total score is the index attention score + the index pool score + the index usage score, and the total score is used as the index popularity of the relevant index.
On the basis of the foregoing embodiments, determining whether an acquired key index of a target service is abnormal based on the service rule configuration list to determine an abnormal index includes:
and determining key indexes matched with the business rule configuration list from the obtained indexes of the target business based on the business rule configuration list.
Specifically, the business rule configuration list only stores key indexes and corresponding normal threshold ranges, and thus a large number of indexes of the target business are obtained, and some indexes may not be stored in the business rule configuration list, so that the key indexes matched with the business rule configuration list are determined from the obtained indexes of the target business by referring to the business rule configuration list, that is, the indexes existing in the business rule configuration list are determined and used as the key indexes of the target business.
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 an abnormal index according to the comparison result, includes:
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 indicators of the target service, the 10 key indicators are numbered with A, B, C, and if A, B, C of the 3 key indicators satisfy the condition that the lower limit of the normal threshold range of the corresponding key indicator in the service rule configuration list is smaller than or larger than the upper limit of the normal threshold range, the 3 key indicators of A, B, C are marked.
Determining the number of the 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 less 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, A, B, C key indexes are all used as abnormal indexes.
Otherwise, resetting the normal threshold range and comparing again to determine the abnormal 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 executed again to determine the abnormal 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 abnormal index of the target service is greater than an abnormality degree threshold value, or the number of the abnormal indexes of the target service is greater than a first number threshold value, determining the emergency degree of the target service to be a first level;
if the number of the target services is larger than a second number threshold, determining that the urgency of the target services is second-level;
otherwise, determining the urgency of the target service as three levels.
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 thereof, the urgency is first level and red early warning is performed; if the key indexes of the target service comprise abnormal indexes of 60% or more, the emergency degree is second grade, and yellow early warning is performed; and the other emergency degree is three levels, and orange early warning is realized.
The system provided in the embodiment of the present invention specifically executes the flows of the above-mentioned methods, and for details, the contents of the above-mentioned methods are referred to, and are not described herein again. Compared with the traditional self-arrangement work and leader arrangement work mode, the system provided by the embodiment of the invention is based on the big data technology, integrates all indexes and service development data, can continuously update and optimize along with the market development situation, has pertinence in work distribution, can effectively find the existing problems of all channels, assists the channel manager to more effectively develop the work, improves the work efficiency, further improves the platform management function of the channel manager, and has an effective promoting effect on the integral promotion of the platform function. The data of each layer of the existing channel development is fully analyzed, effective control on the development situation and the service development condition of each channel is covered, and the leader on the upper layer can effectively control the overall development condition of the channel and timely find the inferior indexes of each channel and the direction to be promoted when the method is applied, so that 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, and as shown in fig. 2, the system includes:
the automatic screening parameter configurator 201 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 202 is 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 used for determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, generating the work order of the target service according to the emergency degree and dispatching the work order.
The following respectively describes the autofilter parameter configurator 201, the intelligent index filter 202 and the work order dispatcher 203:
the autofilter parameter configurator 201:
fig. 3 is a schematic structural diagram of an autofilter parameter configurator according to an embodiment of the present invention, as shown in fig. 3, which includes an index heat identification model, an index autocollector, an index pool, and a parameter configurator.
Firstly, an index heat identification model is constructed through a big data technology, the model automatically collects frequency information of relevant indexes of various services of a system, such as use frequency, attention frequency and collection frequency, and the heat of the relevant indexes is judged and output through the model; then, the index automatic collector automatically collects key indexes according to the heat condition of each relevant index and inputs the key indexes into an index pool; and finally, extracting key indexes from the index pool by the parameter configurator and determining a normal threshold range, thereby forming each business rule configuration list. Optionally, the service rule configuration list may include not only the key indexes of the class service and the normal threshold range of each key index, but also unit attribution, personnel and data cycle of each key index.
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 the operation type, the operation times and the like;
and step 3: comparing the analyzed keywords with the data model, and marking index word segmentation;
and 4, step 4: summarizing indexes, wherein index frequencies including use frequency, attention frequency and collection frequency are summarized according to attribution units, operators and operation categories;
and 5: and sorting the summarized data, sorting the summarized data from high to low according to the index use frequency, the attention frequency and the collection frequency, and marking sequence numbers.
Step 6: calculating metric values through attribute correlation analysis aiming at the sorted indexes, quantifying the correlation among the indexes, analyzing characteristics, analyzing and comparing, deleting irrelevant and weak relevant indexes by using the selected correlation analysis metric, and keeping the most relevant indexes;
and 7: and calculating the score of each relevant index according to a weight (configurable) X (1- (Ni-1)/Nn) (wherein X is the total score of indexes such as the attention times, the collection times, the use times and the like, the score can be configured, the value can be taken in a tenth system or a percent system, Ni is the ranking of the indexes according to the attention times, the collection times and the use times respectively, and Nn is the ranking of all the indexes according to the attention times, the collection times and the use times respectively). The index attention score is (index attention score) (1- (index attention frequency name-1))/index attention frequency total ranking; the index collection score is the index collection score (1- (index collection times name-1))/index collection times total ranking; index use score ═ index use score × (1- (index use times name-1))/index use times total ranking;
and 8: calculating the total score of each relevant index, wherein the formula is as follows: the total score is the index attention score + the index pool score + the index usage score, and the total score is used as the index popularity of the relevant index.
The key technical points in the automatic screening parameter configurator 201 are as follows:
1) constructing a system index heat identification model through a big data modeling technology, automatically collecting various system indexes by the model, and determining related indexes;
2) extracting key indexes from the index pool by the parameter configurator and determining a normal threshold range to form a service rule configuration list;
the intelligent index filter 202:
fig. 4 is a schematic structural diagram of an intelligent indicator filter according to an embodiment of the present invention, as shown in fig. 4, including a data indicator engine and an indicator dynamic filter.
The data index engine automatically extracts service cycle data according to the service rule configuration list output by the automatic screening parameter configurator 201, and realizes bidirectional fusion of the service cycle 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 abnormal indexes and outputs the values of the abnormal indexes.
The index dynamic filter algorithm is as follows:
step 1: reading the service 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;
and step 3: calculating the ratio of abnormal data of the index data, if the ratio is less than or equal to N (configurable), executing the step 5, otherwise executing the step 4;
and 4, step 4: recalculating the rule abnormal threshold, wherein the calculation method comprises the following steps: maximum value is index average value +3 standard deviations; jumping to step 2 to execute the minimum value which is the index average value and has 3 standard deviations;
and 5: and outputting the abnormal index and the value thereof.
Key technical points in the intelligent index filter 202 are as follows:
1) the data index engine automatically extracts the service cycle data according to the service rule configuration list output by the automatic screening parameter configurator 201, and realizes bidirectional fusion of the service cycle data and the rule configuration list.
2) The index dynamic filter intelligently judges the data indexes of different services, determines abnormal indexes and outputs the values of the abnormal indexes.
The work order automatic dispatch device 203:
FIG. 5 is a schematic structural diagram of an automatic work order dispatcher according to an embodiment of the present invention, and the automatic work order dispatcher includes a classification identifier, a work order dispatcher, and a work order progress tracker, as shown in FIG. 5.
Specifically, the classification identifier classifies and classifies abnormal indexes output by the intelligent index filter 202 according to a business rule configuration list output by the automatic screening parameter configurator 201, and generates three-level red, yellow and orange abnormity corresponding to three types of work order types, namely emergency, general and attention; after receiving the classified and graded abnormal data, the work order dispatcher inquires a user information table and generates an abnormal work order according to the abnormal type of the data to automatically dispatch (dispatching modes are 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 progress of the work order when the work order is dispatched.
The classification grading recognizer 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 service categories;
and step 3: calculating the number of abnormal indexes in each classification and the degree of the abnormal indexes exceeding the threshold value (the degree is expressed by index standard deviation);
and 4, step 4: and classifying the service abnormity according to the number of the classified abnormal indexes and the abnormal degree of the abnormal indexes, wherein the classification method comprises the following steps: if a single index is abnormal and deviates from 6 (configurable) standard deviations or more, or all indexes of the services are abnormal, red early warning is performed, yellow early warning is performed when 60% or more of the indexes in the services are abnormal, and orange early warning is performed.
And 5: and outputting the classified and graded abnormal data.
Key technical points in the work order automatic dispatcher 203:
1) the classification and classification recognizer classifies and classifies the abnormal data output in the step two according to the business rule configuration list output by the self-help screening parameter configuration in the step one to generate three-level red, yellow and orange abnormity;
2) after receiving the classified and graded abnormal data, the work order distributor inquires a user information table and generates an abnormal work order according to the abnormal type of the data to automatically distribute the abnormal work order;
3) the work order progress tracker tracks the progress of the work order.
To sum up, the method and the system provided by the embodiment of the invention have the following beneficial effects:
1. the pertinence of the channel manager work is enhanced, and the work efficiency is improved.
Compared with the traditional self-arrangement work and leader arrangement work mode, the mode is modeled by the big data of the system, 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, the auxiliary channel manager work is more effectively developed, and the work efficiency is improved.
2. Leadership global control consciousness is improved, and work guidance is more scientific
The mode fully analyzes each layer of data developed by the existing channel, covers effective control on the development situation and the service development condition of each channel, and can effectively control the overall development condition of the channel and find the disadvantage indexes of each channel and the direction to be promoted in time when the upper leader applies the method, so that the method is more scientific in the aspect of work guidance.
3. Channel stability is further improved
The channel manager is used as an important link of channel tip management, bears multiple tasks of channel management, channel training, information collection and the like, the work pushing degree plays an important role in maintaining stability and developing of the channel, and the method is used for assisting the channel manager to improve work efficiency and effectively assisting the channel stability and benign development.
4. Fully applies big data analysis technology and effectively improves platform function
The method fully applies big data modeling technology, integrates all indexes and business development data, can continuously update and optimize along with market development situation, effectively assists the development of channel manager work, further promotes the channel manager platform management function, and effectively promotes the overall platform function.
5. The adaptability expandability is strong, and the method can be fully applied to other platforms
The method fully solves the problems of low monitoring index configuration and work distribution, low tracking efficiency, inflexible index configuration and difficult service change adaptation in the management system, can be fully expanded to other work distribution platforms after being effectively applied in the aspect of channel manager management, and can assist the effective promotion of other management platforms.
Fig. 6 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device may include: a processor (processor)601, a communication Interface (Communications Interface)602, a memory (memory)603 and a communication bus 604, wherein the processor 601, the communication Interface 602 and the memory 603 complete communication with each other through the communication bus 604. The processor 601 may call a computer program stored on the memory 603 and executable on the processor 601 to perform the methods provided by the above embodiments, including for example: 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; determining whether the obtained key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes; determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the work order of the target service according to the emergency degree.
In addition, the logic instructions in the memory 603 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods 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), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and the method includes: 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; determining whether the obtained key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes; determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the work order of the target service according to the emergency degree.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A work order dispatching method is characterized by comprising 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;
determining whether the obtained key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes;
determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, and generating and dispatching the work order of the target service according to the emergency degree.
2. The method of claim 1, further comprising:
and tracking the execution progress of the work order.
3. The method of claim 1, wherein determining key indicators of each type of service and a normal threshold range of each key indicator based on big data technology to generate a service rule configuration list comprises:
constructing an index heat identification model based on a big data technology;
determining related indexes and the heat of each related index from the indexes of various services based on the index heat identification model;
and determining key indexes and normal threshold ranges of the key indexes from the relevant indexes based on the heat degree of the relevant indexes to generate a business rule configuration list.
4. The method of claim 3, wherein determining relevant indexes and the heat of each relevant index from the indexes of various services based on the index heat identification model comprises:
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 the indexes;
determining related indexes from the indexes of the various services based on the attribute information of the indexes of the various services;
and determining the heat degree of each relevant index according to the frequency information of each relevant index.
5. The method according to claim 1, wherein determining whether the obtained key index of the target service is abnormal based on the service rule configuration list to determine an abnormal index comprises:
determining key indexes matched with the business rule configuration list from the obtained indexes of the target business based on the business 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.
6. The method according to claim 5, wherein 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 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 the marked key indexes of the target service, determining the ratio of the number 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 less than or equal to a ratio threshold value;
otherwise, resetting the normal threshold range and comparing again to determine the abnormal index.
7. The method of claim 1, wherein determining the degree of abnormality and the number of the 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 abnormal index of the target service is greater than an abnormality degree threshold value, or the number of the abnormal indexes of the target service is greater than a first number threshold value, determining the emergency degree of the target service to be a first level;
if the number of the target services is larger than a second number threshold, determining that the urgency of the target services is second-level;
otherwise, determining the urgency of the target service as three levels.
8. A work order distribution 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 determining whether the acquired key indexes of the target service are abnormal or not based on the service rule configuration list so as to determine abnormal indexes;
and the work order automatic dispatcher is used for determining the abnormal degree and the number of the abnormal indexes, determining the emergency degree of the target service according to the abnormal degree and the number, generating the work order of the target service according to the emergency degree and dispatching the work order.
9. 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 steps of the method according to any of claims 1 to 7 are implemented when the processor executes the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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