CN113435763B - Task automatic getting system and method based on big data - Google Patents
Task automatic getting system and method based on big data Download PDFInfo
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- CN113435763B CN113435763B CN202110757968.9A CN202110757968A CN113435763B CN 113435763 B CN113435763 B CN 113435763B CN 202110757968 A CN202110757968 A CN 202110757968A CN 113435763 B CN113435763 B CN 113435763B
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Abstract
The invention discloses a task automatic getting system and method based on big data, belonging to the technical field of task automatic getting systems, comprising a task issuing unit, wherein the issuing end issues task information with a term to the task issuing unit, and the task issuing unit is connected with a multi-task unit and issues received tasks to the multi-task unit; the multi-task unit automatically takes the tasks and issues prompt information at a receiving end, and the multi-task unit is connected with the task progress unit; and the task progress unit is used for receiving the task progress information and feeding back the progress information to the issuing end, and the task progress unit is connected with the unit in the state of waiting for work. Intermediate layers for task release are reduced, flattening and humanized management are achieved, and working efficiency is improved; the unclear intercourse of the tasks is avoided, and a relatively comfortable working environment is created for the social terrorist crowd.
Description
Technical Field
The invention relates to the technical field of automatic task getting systems, in particular to an automatic task getting system and method based on big data.
Background
Most enterprises and government agency departments still through traditional under-line handling, under-line communication, under-line feedback mode on the management mode of assigning, tracking, feeding back of task, higher level leaders can't in time effectually look over the completion condition to the task of oneself assigning, and the task completion quality, efficiency etc. that can't audio-visual statistics analysis everyone under each department are gone out to individual task also is convenient for tracking and feeding back to oneself.
Patent No. CN202011436753.9 also discloses an office task management system and method, relating to the technical field of office systems. The intelligent terminal comprises a processor and a terminal, wherein the processor comprises a storage module, a statistical analysis module, a search module and a data transmission module, and the processor can be electrically connected with the terminal or in communication connection in a limited network mode, a wireless network mode and the like. The terminal comprises a task editing module, a task processing module, a task query module, a display module and a communication module. However, the above patents all have the following problems:
1. the managers lead layer by layer and the tasks are issued layer by layer, and the tasks comprise task issuers, executive guide persons, supervisors, task executors, task assistors, task acceptors and the like, the number of middle management layers related to the tasks is large, the task executors are limited greatly, the constraint is more, the working efficiency of the executors is low, the tasks are not suitable for young groups, and the rate of the deputy of young people is high;
2. the task execution management mode adopts an electronic mail issuing mode, a person point-to-point handover task and a conference type issuing task as a mainstream task issuing mode, so that the problems of information error, memory disorder, understanding deviation and the like of a task issuer and a task receiver are caused, and the communication mode causes the working panic of the social terrorist crowd and heavy psychological pressure burden;
3. the task completion condition, efficiency, standard reaching rate, whether the task can be completed in due time, the completion quality, the employee ability and other conditions cannot be known by the manager in one way, the manager knows the conditions of the executor, the task progress is delayed, psychological burden is caused to the executor, and if the administrator is not aware of the conditions, the conditions of carelessness of the manager, negative work of the executor and the like exist.
Disclosure of Invention
The invention aims to provide a task automatic getting system and method based on big data, which reduces intermediate layers for task release, is flat and humanized in management, improves the working efficiency, accords with the working concepts after 90 and after 00, and reduces the job leaving rate of enterprises; direct leadership is reduced, the unclear generation of tasks is avoided, and a relatively comfortable working environment is created for social terrorist crowds; the corresponding task progress monitoring, task completion rate statistics and excellent standard reaching rate statistics parts are arranged, a manager can directly monitor the task condition, know the staff capacity and improve the working enthusiasm of the staff, and therefore the problem brought forward in the background technology is solved.
In order to achieve the purpose, the invention provides the following technical scheme: a big data-based automatic task getting system comprises,
the task issuing unit is connected with the multitask unit and issues the received tasks to the multitask unit;
the multi-task unit automatically takes the tasks and issues prompt information at a receiving end, and the multi-task unit is connected with the task progress unit;
the task progress unit is used for receiving task progress information and feeding back the progress information to the issuing end, and the task progress unit is connected with the unit in the state of waiting for work;
the work waiting state unit monitors whether the task progress unit enters a completion state, and if the task progress unit enters the completion state, the work waiting state is started and a work waiting prompt is sent to the task issuing unit;
the task progress unit is divided into states of task preparation, task starting, task in-progress, task ending and task submitting, wherein a mode in task in-progress is divided according to task characteristics, and each part of a task is submitted by a completer, submitted and triggered by a manager or automatically triggered after a related process program is completed;
the task information comprises task types, task contents, content explanations, task requirements, related remarks, guidance opinions, completion paths, names of departments involved in the completion process, contact ways of related personnel, operation descriptions and notice items of equipment;
the multi-task unit is connected with a task prompting unit, the task prompting unit monitors whether a receiving end of the multi-task unit automatically receives a new task, and if the receiving end receives the new task, the task prompting unit is started to perform corresponding prompting.
Furthermore, the multitask unit is connected with a return unit, when the to-be-executed person thinks that the task cannot be completed, the task is unreasonable and the task is wrongly issued, the return unit is started to return the task to the sending end, wherein the return unit is provided with a remark part, the to-be-executed person must set remark for returning the task, and the reason of returning is explained and used as information feedback.
Furthermore, each time the multitask unit automatically receives a new task, a task end is automatically generated, and each task end is correspondingly connected with a task progress unit and a return unit.
Furthermore, the multitask unit is connected with a data backup unit, the data backup unit is respectively connected with the multitask unit, the return unit and the task progress unit, the multitask unit generates a data packet while generating a task end, task information, a task return state and a task state of the task end are synchronously copied and backed up in the data packet, and each task corresponds to one data packet.
Further, the standby state unit monitors all task ends, a task entering return unit of a task end is determined as a task disappearance, a task progress unit is in any part of task preparation, task start and task progress, the task progress unit is determined as a task in progress, the task progress unit is determined as a task disappearance if the task is finished or submitted, and the standby state unit is triggered to send a standby prompt to the task issuing unit on the premise that all tasks are disappeared.
Further, the task issuing unit is connected with a success rate measuring and calculating unit, the success rate measuring and calculating unit collects the completion time, completion rate and excellent standard reaching rate of the same or similar tasks which are completed in the history, and respectively calculates the mean value mu of the calculation completion time and the excellent standard reaching rate in the history tasks;
respectively fitting completion time and excellent standard-reaching rate in historical task data by using a Local Weighted Regression (LWR) algorithm to obtain a fitted function value;
describing a fitting method LWR, wherein the LWR can be calculated only by a weight function and neighborhood parameters, the neighborhood parameters are data provided by a historical task, and the calculation of key parameters is shown as a formula:
the numerical difference between the neighborhood parameters is set by adopting the d value of the Euclidean distance, and the following formula is shown as follows:
a. b are two sets of sequences of neighborhood parameters, which can be expressed in space as:
a＝(a 1 ，a 2 ，....a n )
b＝(b 1 ，b 2 ，....b n )
the weight function is set by a cubic weight function method, so that the weight function W (sigma) in the weighted least square regression is adopted i ) As follows：
W(σ i )＝(1-σ i 3 ) 3 ，0≤σ≤1
Local fitting is carried out by a weighted least square method at each point of the data interval, and the local fitting is fitted into a polynomial function which is used as the estimation of a regression function in numerical values; respectively subtracting the two groups of function values from the data sample to obtain two groups of residual error sequences based on LWR, and establishing a residual error map according to the residual error sequences; taking the two groups of LWR residual sequences as test data, and obtaining an operation trend through the slope of a fitting trend term by using a Pettitt algorithm;
the mean value of the completion time and the excellent standard-reaching rate of the historical task is T μ And S μ If the mean value is set, the completion time, completion rate and average value of the task are set to be unchanged, and X is assumed to be zero i And alternative assumptions of varying average X j At the generation of data points, T μ The former and latter data are compared based on rank, and the Pettitt statistic is represented as K (T) μ )，S μ The preceding and following data were compared based on rank, and the Pettitt statistic was denoted as K (S) μ ) The calculation formula is as follows:
determination of K (T) μ ) And K (S) μ ) Defines the statistic:
T＝arg max(|K(T μ )|)，1≤T μ ≤i
S＝max(|K(S μ )|)，1≤S μ ≤i
wherein: k refers to the final Pettitt statistic, T refers to the time point corresponding to the corresponding general completion time, and the significance probability pair X related to the time point i Is approximately P ≈ 2exp [ -6K 2 (i 3 +i 2 )]If P is<0.5, the working efficiency is considered to be lower than the universality, P =0.5, the working efficiency is considered to be equivalent to the universality, P>0.5, the work efficiency is considered to be equal to the universality, and similarly, the excellent standard-reaching rate in the trend item can be obtained by fitting the slope of the trend item, and the excellent degree of work completion can be graded.
Further, the task scheduling unit includes:
the monitoring subunit is used for acquiring the equipment parameter information of the task execution terminal and generating a task progress monitoring program package and an execution instruction according to the equipment parameter information;
the data issuing subunit is used for sending the generated task progress monitoring program package and the execution instruction to the task execution terminal, and the task execution terminal decompresses and installs the received task progress monitoring program package;
the state detection subunit is used for detecting the running state of the task execution terminal in real time and controlling the task progress monitoring program package to monitor the data processing parameters of the task execution terminal based on the execution instruction when the task execution terminal is detected to run a data processing task;
the data processing subunit is used for matching the task progress tracking function from the preset function library based on the data processing parameters and performing instrumentation on an inlet of the task progress tracking function;
the data processing subunit is also used for obtaining a plurality of node tracking information of the task execution terminal during the operation of the data processing task based on the instrumentation result;
the progress determining subunit is used for obtaining the current task progress based on the plurality of node tracking information and displaying the current task progress on a preset progress display bar;
the data feedback subunit is used for determining the division times of the data of the current task progress based on the data size corresponding to the current task progress and storing the data blocks obtained by division based on the division times into a data queue to be transmitted;
the interface checking subunit is used for determining the IP address of the issuing end and the terminal identifier of the task progress unit after receiving the feedback instruction sent by the issuing end, and matching the IP address of the issuing end with the terminal identifier of the task progress unit;
if the matching is successful, sending the data blocks in the data queue to be transmitted to the issuing end, and finishing the task progress information feedback to the issuing end;
otherwise, the IP address of the issuing end and the terminal identification of the task progress unit are checked and matched again until the task progress information is fed back to the issuing end.
Further, the task scheduling unit further includes:
the data acquisition subunit is used for acquiring the task quantity corresponding to the acquired task;
the calculation subunit is used for calculating the actual execution speed of the task execution terminal on the retrieved task according to the task amount corresponding to the retrieved task, and calculating the qualification rate of the task execution terminal in executing the retrieved task according to the execution speed of the task execution terminal on the retrieved task, and the specific steps include:
calculating the actual execution speed of the task execution terminal on the retrieved task according to the following formula:
wherein, V represents the actual execution speed of the task that the task execution terminal gets; alpha represents the task amount corresponding to the task taken by the task execution terminal; t represents the time length value used by the task execution terminal from the receiving to the completion of the receiving task; delta represents a task quantity statistical error factor, and the value range is (0.05, 0.08); t represents the length value of the invalid working time of the task execution terminal;
and calculating the qualification rate of the task execution terminal when the task execution terminal executes the acquired task according to the following formula:
wherein eta represents the qualification rate of the task execution terminal when the task execution terminal executes the acquired task, and the value range is (0, 1); mu represents a calculation error coefficient, and the value range is (0.02, 0.06); v represents the actual execution speed of the task retrieved by the task execution terminal; q represents a target required speed when the task acquired by the task execution terminal is executed; tau represents an allowable error range and has a value range of (-0.05, 0.05);
the data comparison subunit is used for comparing the calculated qualified rate with a preset qualified rate;
if the calculated qualified rate is less than the preset qualified rate, judging that the task which is obtained by the task execution terminal is unqualified, sending a preset execution speed adjusting instruction to the task execution terminal based on the issuing end, and adjusting the execution speed by the task execution terminal based on the preset execution speed adjusting instruction until the calculated qualified rate is greater than or equal to the preset qualified rate;
otherwise, judging that the task acquired by the task execution terminal is qualified.
According to another aspect of the present invention, there is provided a method for automatically picking up tasks based on big data, including the following steps:
s101: the management system issues task information with a term from the task issuing unit to the multitask unit of the person to be completed;
s102: the multi-task unit automatically picks up the task and prompts the task to be completed;
s103: a to-be-completed person checks the task information and selects to enter a task progress unit, and the task progress unit receives the task progress information and feeds the progress information back to a publishing terminal;
s104: after the task is completed, the completer enters a standby state, the standby state unit sends a standby prompt to the task issuing unit, and the management system preferentially arranges the task for the standby state.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the automatic task getting system and method based on the big data, disclosed by the invention, the number of intermediate layers for task release is reduced, the management is flat and humanized, the working efficiency is improved, the working concept after 90 and after 00 is met, and the job leaving rate of an enterprise is reduced;
2. the task automatic getting system and method based on big data provided by the invention realize electronic management, replace traditional dictation task content with electronic task information, reduce direct leadership, not only avoid the task from becoming unclear, but also create a relatively comfortable working environment for social and terrorist people;
3. according to the automatic task getting system and method based on big data, the corresponding task progress monitoring part, the task completion rate counting part and the excellent standard reaching rate counting part are arranged, a manager can directly monitor the task condition and know the ability of staff, and on the other hand, the working enthusiasm of the staff can be improved, and the staff can be encouraged to make continuous progress.
Drawings
Fig. 1 is an overall structural diagram of a task automatic getting system based on big data according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of an automatic task getting system based on big data according to a first embodiment of the present invention;
FIG. 3 is a flowchart of a method for automatically picking up a task based on big data according to a first embodiment of the present invention;
FIG. 4 is a diagram of an overall structure of an automatic task getting system based on big data according to a second embodiment of the present invention;
FIG. 5 is a diagram of an overall structure of an automatic task getting system based on big data according to a third embodiment of the present invention;
fig. 6 is an overall structural diagram of an automatic task getting system based on big data according to a fourth embodiment of the present invention;
fig. 7 is an overall structural diagram of an automatic task obtaining system based on big data according to a fifth embodiment of the present invention.
In the figure: 1. a task issuing unit; 2. a task prompt unit; 3. a multitasking unit; 4. a retracting unit; 5. a task progress unit; 6. a standby state unit; 7. a success rate measuring unit; 8. and a data backup unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Example one
Referring to fig. 1 to 2, a big data based task automatic picking system includes,
the task issuing unit 1 is characterized in that an issuing end issues task information with a term to the task issuing unit 1, the task information comprises task types, task contents, content explanations, task requirements, related notes, guidance opinions, completion paths, department names related to the completion process, contact ways of related personnel, operation instructions and cautionary items of equipment, the task issuing unit 1 is connected with a multi-task unit 3, and received tasks are issued to the multi-task unit 3;
the multitask unit 3 is used for automatically picking up tasks and issuing prompt information at a receiving end, and the multitask unit 3 is connected with the task progress unit 5;
the task progress unit 5 is used for receiving the task progress information and feeding the progress information back to the issuing end, and the task progress unit 5 is connected with the to-be-worked state unit 6;
and the work waiting state unit 6 monitors whether the task progress unit 5 enters the completion state or not, and if the work waiting state enters the completion state, the work waiting state is started and a work waiting prompt is sent to the task issuing unit 1.
The task progress unit 5 is divided into states of task preparation, task starting, task in-progress, task ending and task submitting, wherein a mode in task in-progress is divided according to task characteristics, and each part of the task is submitted by a completer, a manager or automatically triggered after a related process program is completed.
Referring to fig. 3, in order to better show the process of automatically picking up tasks based on big data, the embodiment now provides a method for automatically picking up tasks based on big data, which includes the following steps:
s101: the management system issues task information with an expiration from the task issuing unit 1 to the multitask unit 3 of the to-be-completed person;
s102: the multi-task unit 3 automatically picks up the task and prompts the task to be completed;
s103: the to-be-completed person checks the task information, selects to enter the task progress unit 5, and the task progress unit 5 receives the task progress information and feeds the progress information back to the issuing end;
s104: after the task is completed, the completer enters a standby state, the standby state unit 6 sends a standby prompt to the task issuing unit 1, and the management system prioritizes the task for the standby state.
Example two
Referring to fig. 4, the difference between the first embodiment and the second embodiment is only that a task prompting unit 2 is additionally provided in the first embodiment, the multitasking unit 3 is connected to the task prompting unit 2, the task prompting unit 2 monitors whether a receiving end of the multitasking unit 3 automatically picks up a new task, and if the new task is picked up, the task prompting unit 2 is started to perform corresponding prompting, so that a task waiting person timely receives task information and completes the task within a corresponding time limit.
EXAMPLE III
Referring to fig. 5, the difference between the present embodiment and the second embodiment is that only a return unit 4 is added in the present embodiment, the multitask unit 3 is connected to the return unit 4, and when a person to be executed considers that a task cannot be completed, the task is unreasonable, and the task is wrongly issued, the return unit 4 is started to return the task to a sending end, where the return unit 4 is provided with a remark part, and the person to be executed must set a return remark to return the task, explain a return reason, and use the remark as information feedback; the task waiting state unit 6 monitors all task ends, the task of the task end enters the return unit 4 and is determined to disappear, the task progress unit 5 is in any part of task preparation, task start and task progress and is determined to be in task progress, the task progress unit 5 is in task end or task submission and is determined to disappear, the task waiting state unit 6 is triggered to send a task waiting prompt to the task issuing unit 1 on the premise that all tasks disappear, the return unit 4 is arranged to avoid errors of a management system and waste of unnecessary time caused by accumulation of unprocessed cases on hands of an unprocessed person and reduce working efficiency, the multi-task unit 3 automatically generates one task end every time a new task is automatically taken, each task end is correspondingly connected with one task progress unit 5 and one return unit 4, each task line is relatively independent, the task progress monitoring is facilitated, the task data statistics is facilitated, and task data statistics is facilitated.
Example four
Referring to fig. 6, the difference between the present embodiment and the third embodiment is only that a data backup unit 8 is additionally provided in the present embodiment, the multitasking unit 3 is connected to the data backup unit 8, the data backup unit 8 is respectively connected to the multitasking unit 3, the return unit 4, and the task progress unit 5, the multitasking unit 3 generates a data packet while the data backup unit 8 generates a task end, task information, a task return state, and a task state of the task end are synchronously copied and backed up in the data packet, each task corresponds to one data packet, and after data backup is provided, not only can unnecessary loss caused by loss of relevant data such as task information or task progress be avoided, but also task statistics can be independently organized as a historical task database to provide data support for annual performance statistics, inventory, and the like.
EXAMPLE five
Referring to fig. 7, the difference between the present embodiment and the fourth embodiment is only that a success rate measuring unit 7 is added in the present embodiment, the task issuing unit 1 is connected to the success rate measuring unit 7, the success rate measuring unit 7 collects the completion time, completion rate, and excellent standard reaching rate of the same or similar tasks that have been completed in the history, and calculates the mean μ of the completion time and the excellent standard reaching rate in the history task respectively;
respectively fitting completion time and excellent standard-reaching rate in historical task data by using a Local Weighted Regression (LWR) algorithm to obtain a fitted function value;
describing a fitting method LWR, wherein the LWR can be calculated only by a weight function and neighborhood parameters, the neighborhood parameters are data provided by a historical task, and the calculation of key parameters is shown as a formula:
wherein, the numerical difference between the neighborhood parameters is set by adopting the d value of the Euclidean distance, and the following formula is shown:
a. b are two sets of sequences of neighborhood parameters, which can be expressed in space as:
a＝(a 1 ，a 2 ，....a n )
b＝(b 1 ，b 2 ，....b n )
the weight function is set by a cubic weight function method, so that the weight function W (sigma) in the weighted least square regression is adopted i ) The following were used:
W(σ i )＝(1-σ i 3 ) 3 ，0≤σ≤1
local fitting is carried out by a weighted least square method at each point of the data interval, and the local fitting is fitted into a polynomial function which is used as the estimation of a regression function in numerical values; respectively subtracting the two groups of function values from the data sample to obtain two groups of residual error sequences based on LWR, and establishing a residual error map according to the residual error sequences; taking the two groups of LWR residual sequences as test data, and obtaining an operation trend through the slope of a fitting trend term by using a Pettitt algorithm;
the mean value of the completion time and the excellent standard-reaching rate of the historical task is T μ And S μ If the mean value is set, the completion time, completion rate and average value of the task are set to be unchanged, and X is assumed to be zero i And alternative assumptions of varying average X j At the generation of data points, T μ The front data and the back data are compared based on rank, and the Pettitt systemThe metric is represented by K (T) μ )，S μ The former and latter data are compared based on rank, and the Pettitt statistic is represented as K (S) μ ) The calculation formula is as follows:
determination of K (T) μ ) And K (S) μ ) Defines the statistic:
T＝arg max(|K(T μ )|)，1≤T μ ≤i
S＝max(|K(S μ )|)，1≤S μ ≤i
wherein: k refers to the final Pettitt statistic, T refers to the time point corresponding to the corresponding general completion time, and the significance probability pair X related to the time point i Is approximately P ≈ 2exp [ -6K 2 (i 3 +i 2 )]If P is<0.5, the work efficiency is considered to be lower than the universality, P =0.5, the work efficiency is considered to be equivalent to the universality, P>0.5, the work efficiency is considered to be equal to the universality, and similarly, the excellent standard-reaching rate in the trend item can be obtained by fitting the slope of the trend item, so that the excellent degree of the completion of the work is graded, the enthusiasm of the work is improved, and the continuous progress of the staff is encouraged.
In summary, the following steps: according to the automatic task getting system and method based on big data, disclosed by the invention, the number of intermediate layers for task release is reduced, the flat and humanized management is realized, the working efficiency is improved, the working concept after 90 and after 00 is met, and the enterprise rate of job leaving is reduced; electronic management is realized, traditional dictation task content is replaced by electronic task information, direct leadership is reduced, unclear task generation is avoided, and a relatively comfortable working environment is created for social terrorist crowds; corresponding task progress monitoring, task completion rate statistics and excellent standard-reaching rate statistics parts are arranged, a manager can directly monitor task conditions and can know staff capacity, on the other hand, the working enthusiasm of the staff can be improved, and the staff is encouraged to make continuous progress.
EXAMPLE six
A task automatic getting system based on big data, a task progress unit comprises:
the monitoring subunit is used for acquiring the equipment parameter information of the task execution terminal and generating a task progress monitoring program package and an execution instruction according to the equipment parameter information;
the data issuing subunit is used for sending the generated task progress monitoring program package and the execution instruction to the task execution terminal, and the task execution terminal decompresses and installs the received task progress monitoring program package;
the state detection subunit is used for detecting the running state of the task execution terminal in real time, and controlling the task progress monitoring program package to monitor the data processing parameters of the task execution terminal based on the execution instruction when the task execution terminal is detected to run the data processing task;
the data processing subunit is used for matching the task progress tracking function from the preset function library based on the data processing parameters and performing instrumentation on an inlet of the task progress tracking function;
the data processing subunit is also used for obtaining a plurality of node tracking information of the task execution terminal during the operation of the data processing task based on the instrumentation result;
the progress determining subunit is used for obtaining the current task progress based on the plurality of node tracking information and displaying the current task progress on a preset progress display bar;
the data feedback subunit is used for determining the division times of the data of the current task progress based on the data size corresponding to the current task progress and storing the data blocks obtained by division based on the division times into a data queue to be transmitted;
the interface checking subunit is used for determining the IP address of the issuing end and the terminal identifier of the task progress unit after receiving the feedback instruction sent by the issuing end, and matching the IP address of the issuing end with the terminal identifier of the task progress unit;
if the matching is successful, sending the data blocks in the data queue to be transmitted to the issuing end, and finishing the task progress information feedback to the issuing end;
otherwise, checking and re-matching the IP address of the issuing end and the terminal identification of the task progress unit until the task progress information is fed back to the issuing end.
In this embodiment, the device parameter information refers to operation parameter information of the terminal that retrieves the task to execute, and may be, for example: the execution terminal can be a machine or an executive person.
In this embodiment, the task progress monitoring package refers to a monitoring program for monitoring the degree of executing the task, and is used to monitor the progress of the task executing terminal in executing the retrieved task.
In this embodiment, the task progress tracking function refers to an algorithm for tracking the progress of executing the task in real time, and can monitor the execution condition of the task at each time point when the task is executed.
In this embodiment, the instrumentation refers to inserting some probes into the task progress tracking function on the basis of ensuring the original logic integrity of the function, and the probes essentially perform information acquisition, so as to conveniently obtain the task execution conditions of each time node monitored by the task progress tracking function.
In this embodiment, the node tracking information refers to task execution conditions monitored by the task tracking function at different time points, including a current execution progress of the task.
In this embodiment, the preset progress display bar is set in advance and is used for displaying the execution progress of the current task.
In this embodiment, the data column to be transmitted is used to store data to be transmitted.
The beneficial effects of the above technical scheme are: the method comprises the steps of acquiring equipment parameters of a task execution terminal, generating a monitoring data packet of the execution terminal, monitoring the execution progress of the execution terminal in real time through the monitoring data packet, and facilitating accurate acquisition of the execution progress of the current task.
EXAMPLE seven
A task automatic getting system based on big data, the task progress unit, also includes:
the data acquisition subunit is used for acquiring the task quantity corresponding to the acquired task;
the calculation subunit is used for calculating the actual execution speed of the task execution terminal on the retrieved task according to the task amount corresponding to the retrieved task, and calculating the qualification rate of the task execution terminal in executing the retrieved task according to the execution speed of the task execution terminal on the retrieved task, and the specific steps include:
calculating the actual execution speed of the task execution terminal on the retrieved task according to the following formula:
v represents the actual execution speed of the task execution terminal for the retrieved task; alpha represents the task amount corresponding to the task taken by the task execution terminal; t represents the time length value used by the task execution terminal from the receiving to the completion of the receiving task; delta represents a task quantity statistical error factor, and the value range is (0.05, 0.08); t represents the length value of the invalid working time of the task execution terminal;
and calculating the qualification rate of the task execution terminal when the task execution terminal executes the acquired task according to the following formula:
wherein eta represents the qualification rate of the task execution terminal when the task execution terminal executes the acquired task, and the value range is (0, 1); mu represents a calculation error coefficient, and the value range is (0.02, 0.06); v represents the actual execution speed of the task retrieved by the task execution terminal; q represents a target required speed when the task acquired by the task execution terminal is executed; tau represents an allowable error range and has a value range of (-0.05, 0.05);
the data comparison subunit is used for comparing the calculated qualified rate with a preset qualified rate;
if the calculated qualification rate is less than the preset qualification rate, judging that the task acquired by the task execution terminal is unqualified, sending a preset execution speed adjusting instruction to the task execution terminal based on the issuing end, and adjusting the execution speed by the task execution terminal based on the preset execution speed adjusting instruction until the calculated qualification rate is greater than or equal to the preset qualification rate;
otherwise, judging that the task acquired by the task execution terminal is qualified.
In this embodiment, the actual execution speed refers to a theoretical execution speed that is obtained after the theoretical execution speed is affected by external influence factors, such as environment and device performance.
In this embodiment, the task amount statistical error factor refers to an influence factor that causes deviation in task amount statistics due to human factors or different task strengths when the task amount is counted, and the influence factor is represented by specific data, where a larger value indicates a larger influence.
In this embodiment, the calculation error coefficient refers to a parameter that affects the yield, such as calculation accuracy, when calculating the yield, and the larger the value, the more serious the effect is.
In this embodiment, the target required speed refers to a task execution speed that the task execution terminal is expected to reach after a plurality of training, which is known in advance, and the target required speed is greater than the actual execution speed.
In this embodiment, the predetermined yield is set in advance, and is used to measure whether the calculated yield reaches the standard.
In this embodiment, the preset execution speed adjustment instruction is set in advance, and the issue end is used to control the task execution terminal to adjust the execution speed.
The beneficial effects of the above technical scheme are: and calculating the actual execution speed of the task execution terminal, and calculating the qualification rate of the task execution terminal for executing the task according to the actual execution speed. When the actual execution speed is calculated, the ratio of the task amount to the execution time is related, so that the calculated speed value is scientific and accurate, when the qualified rate is calculated, the sum of the ratio of the actual execution speed to the target required speed and the allowable error range is related, and finally the final qualified rate is obtained under the influence of the calculation error coefficient, so that the calculated result is accurate and reliable.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (6)
1. A big data-based automatic task getting system is characterized by comprising,
the task issuing unit (1) is connected with the multitask unit (3) and issues the received task to the multitask unit (3);
the multitask unit (3) automatically receives the tasks and issues prompt information at a receiving end, and the multitask unit (3) is connected with the task progress unit (5);
the task progress unit (5) is used for receiving task progress information and feeding back the progress information to the issuing end, and the task progress unit (5) is connected with the unit (6) with the state of waiting for work;
the work waiting state unit (6) monitors whether the task progress unit (5) enters a completion state or not, and if the work waiting state unit enters the completion state, the work waiting state is started and a work waiting prompt is sent to the task issuing unit (1);
the task progress unit (5) is divided into states of task preparation, task starting, task proceeding, task ending and task submitting, wherein the task proceeding mode is divided according to task characteristics, and each part of the task is submitted by a completer, submitted and triggered by a manager or automatically triggered after a related process program is completed;
the task information comprises task types, task contents, content explanations, task requirements, related remarks, guidance opinions, completion paths, department names related to the completion process, contact ways of related personnel, operation descriptions of equipment and attention points;
the multi-task unit (3) is connected with a task prompting unit (2), the task prompting unit (2) monitors whether a receiving end of the multi-task unit (3) automatically receives a new task, and if the receiving end receives the new task, the task prompting unit (2) is started to perform corresponding prompting;
the task scheduling unit (5) comprises:
the monitoring subunit is used for acquiring the equipment parameter information of the task execution terminal and generating a task progress monitoring program package and an execution instruction according to the equipment parameter information;
the data issuing subunit is used for sending the generated task progress monitoring program package and the execution instruction to the task execution terminal, and the task execution terminal decompresses and installs the received task progress monitoring program package;
the state detection subunit is used for detecting the running state of the task execution terminal in real time, and controlling the task progress monitoring program package to monitor the data processing parameters of the task execution terminal based on the execution instruction when the task execution terminal is detected to run the data processing task;
the data processing subunit is used for matching the task progress tracking function from the preset function library based on the data processing parameters and performing instrumentation on an inlet of the task progress tracking function;
the data processing subunit is also used for obtaining a plurality of node tracking information of the task execution terminal during the operation of the data processing task based on the instrumentation result;
the progress determining subunit is used for obtaining the current task progress based on the plurality of node tracking information and displaying the current task progress on a preset progress display bar;
the data feedback subunit is used for determining the division times of the data of the current task progress based on the data size corresponding to the current task progress and storing the data blocks obtained by division based on the division times into a data queue to be transmitted;
the interface checking subunit is used for determining the IP address of the issuing end and the terminal identifier of the task progress unit after receiving the feedback instruction sent by the issuing end, and matching the IP address of the issuing end with the terminal identifier of the task progress unit;
if the matching is successful, sending the data blocks in the data queue to be transmitted to the issuing end, and finishing the feedback of task progress information to the issuing end;
otherwise, checking and re-matching the IP address of the issuing end with the terminal identification of the task progress unit until the task progress information is fed back to the issuing end;
the task progress unit (5) further comprises:
the data acquisition subunit is used for acquiring the task quantity corresponding to the acquired task;
the calculation subunit is used for calculating the actual execution speed of the task execution terminal on the retrieved task according to the task amount corresponding to the retrieved task, and calculating the qualification rate of the task execution terminal in executing the retrieved task according to the execution speed of the task execution terminal on the retrieved task, and the specific steps include:
calculating the actual execution speed of the task execution terminal on the retrieved task according to the following formula:
wherein, V represents the actual execution speed of the task that the task execution terminal gets; alpha represents the task amount corresponding to the task taken by the task execution terminal; t represents the time length value used by the task execution terminal from the receiving to the completion of the receiving task; delta represents a task quantity statistical error factor, and the value range is (0.05, 0.08); t represents the length value of the invalid working time of the task execution terminal;
and calculating the qualified rate of the task when the task execution terminal executes the acquired task according to the following formula:
wherein eta represents the qualification rate of the task execution terminal when the task execution terminal executes the acquired task, and the value range is (0, 1); mu represents a calculation error coefficient, and the value range is (0.02, 0.06); v represents the actual execution speed of the task retrieved by the task execution terminal; q represents a target required speed when the task acquired by the task execution terminal is executed; tau represents an allowable error range and has a value range of (-0.05, 0.05);
the data comparison subunit is used for comparing the calculated qualified rate with a preset qualified rate;
if the calculated qualification rate is less than the preset qualification rate, judging that the task acquired by the task execution terminal is unqualified, sending a preset execution speed adjusting instruction to the task execution terminal based on the issuing end, and adjusting the execution speed by the task execution terminal based on the preset execution speed adjusting instruction until the calculated qualification rate is greater than or equal to the preset qualification rate;
otherwise, judging that the task acquired by the task execution terminal is qualified.
2. The automatic task getting system based on big data as claimed in claim 1, wherein the multitask unit (3) is connected with a return unit (4), and when the person to be executed considers that the task cannot be completed, the task is unreasonable and the task is wrongly issued, the return unit (4) is started to return the task to the sending end, wherein the return unit (4) is provided with a remark part, and the person to be executed returns the task and must set a return remark to explain the reason of returning as information feedback.
3. The automatic big data-based task getting system as claimed in claim 2, wherein the multitasking unit (3) automatically generates a task end every time a new task is automatically got, and each task end is correspondingly connected with a task progress unit (5) and a return unit (4).
4. The automatic task getting system based on big data according to claim 3, wherein the multitasking unit (3) is connected with a data backup unit (8), the data backup unit (8) is respectively connected with the multitasking unit (3), a return unit (4) and a task progress unit (5), the multitasking unit (3) generates a data packet by the data backup unit (8) while generating a task end, task information, a task return state and a task state of the task end are synchronously copied and backed up in the data packet, and each task corresponds to one data packet.
5. The automatic task getting system based on big data according to claim 4, wherein the wait state unit (6) monitors all task ends, the task entering and returning unit (4) of a task end is determined as task disappearance, the task progress unit (5) is in any one of task preparation, task start and task progress and is determined as task in progress, the task progress unit (5) is in task end or task submission, is determined as task disappearance, and the wait state unit (6) is triggered to send a wait prompt to the task issuing unit (1) on the premise that all tasks disappear.
6. A method for automatic big data based task picking according to any of claims 1-5, characterized by comprising the following steps:
s101: the management system issues task information with an expiration from the task issuing unit (1) to the multitask unit (3) of the person to be completed;
s102: the multi-task unit (3) automatically picks up the tasks and prompts the task to be completed;
s103: the to-be-completed person checks the task information, selects to enter the task progress unit (5), and the task progress unit (5) receives the task progress information and feeds the progress information back to the issuing end;
s104: after the task is completed, the completer enters a standby state, the standby state unit (6) sends a standby prompt to the task issuing unit (1), and the management system preferentially arranges the task for the standby state.
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