CN117689141A - Case data automatic processing method, system, device and storage medium based on artificial intelligence assistance - Google Patents
Case data automatic processing method, system, device and storage medium based on artificial intelligence assistance Download PDFInfo
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Abstract
The application relates to an automatic case data processing method, system, device and storage medium based on artificial intelligence assistance, and relates to the field of data processing, wherein the method comprises the steps of acquiring a transmission task, wherein the transmission task comprises a task type and task timeliness: determining the residual time according to task aging and current time node analysis; according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade, analyzing and determining the emergency degree grade of different types of tasks, wherein the emergency degree grade can be classified as A, B, C, D; forward ordering is carried out on the task types of the data channels according to the emergency degree grades of the tasks of different types, wherein the forward ordering is the ordering from high to low according to the emergency degree grades; and executing the transmission tasks according to the ordering condition of the task types. The method and the device have the effect of reducing the influence of data acquisition on emergency case processing.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a case data automatic processing method, system, device and storage medium based on artificial intelligence assistance.
Background
At present, financial business scenes are miscellaneous, including online, offline and other transaction scenes. The financial disputes relate to numerous institutions and insufficient administrative mediation institutions resources, and the financial disputes often need to be mediated and processed by a complaint source platform so as to reduce the acceptance burden of a court.
After the complaint source platform receives the cases submitted by the financial institutions, data required by the cases are collected from different institutions, the data of different institutions are decoded and integrated, and the data are converted into a data storage mode of the complaint source platform, so that the cases required to be accepted by the law court can be reduced, and meanwhile, the efficiency of programs such as case data collection after being accepted is improved.
Financial cases often have different emergency degrees, and when the system introduces cases in batches, because the speed of data transmission is limited, the system needs a certain time to collect data, and the data collection is performed according to the time sequence of introduction, so that the processing time of part of emergency cases is easily influenced, and inconvenience is caused.
Disclosure of Invention
In order to reduce the influence of data acquisition on emergency case processing, the application provides an artificial intelligence assistance-based case data automatic processing method, system and device and a storage medium.
In a first aspect, the present application provides an automatic case data processing method based on artificial intelligence assistance, which adopts the following technical scheme:
The case data automatic processing method based on artificial intelligence assistance comprises the steps of obtaining a transmission task, wherein the transmission task comprises a task type and task timeliness;
determining the residual time according to task aging and current time node analysis;
according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade, analyzing and determining the emergency degree grade of different types of tasks, wherein the emergency degree grade can be classified as A, B, C, D;
forward ordering is carried out on the task types of the data channels according to the emergency degree grades of the tasks of different types, wherein the forward ordering is the ordering from high to low according to the emergency degree grades;
and executing the transmission tasks according to the ordering condition of the task types.
By adopting the technical scheme, the transmission tasks are divided into the emergency degrees according to the residual time length, the emergency degree is higher when the residual time is shorter, the transmission tasks are transmitted according to the emergency degree grade order, and compared with the original transmission mode according to the input order, the situation that the task with the longer residual time is preferentially transmitted, so that the task with the shorter residual time is influenced and exceeds the task processing timeliness is avoided, so that the influence of the transmission order of data butt joint on the emergency case processing can be reduced.
Optionally, the analyzing the emergency level to determine the different task types includes:
analyzing whether the acquired task type is an emergency task with a preset emergency level;
if yes, comparing and acquiring the emergency degree grade with the corresponding task type;
analyzing and determining the influence degree of the emergency degree according to the corresponding relation between the residual time interval in which the residual time falls and the influence degree of the emergency degree;
if the emergency degree level is lower than the highest level and the influence degree of the emergency degree exceeds the preset influence degree, increasing the emergency degree of the corresponding task type by one level;
if not, analyzing and determining the emergency degree grade of the task type according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade.
By adopting the technical scheme, the emergency degree is preset for the task type in the transmission task, the emergency degree grade improvement or the unchanged of the transmission task can be determined according to the emergency degree influence degree corresponding to the residual time, and the special type cases needing emergency treatment in the cases can be further transmitted preferentially, so that the influence of data acquisition on emergency case treatment is further reduced.
Optionally, forward ordering task types of the data channel according to urgency levels of different types of tasks includes:
analyzing whether task types with the same emergency degree level exist or not;
if yes, acquiring the number of different types of tasks contained in the transmission task and the number of on-duty personnel for processing the different types of tasks, and calculating the average task quantity required to be accepted by the on-duty personnel for processing the different types of tasks;
according to the corresponding relation between the task type and the average task carrying quantity of the on-duty personnel and the average task quantity required to be carried by the on-duty personnel processing different types of tasks, calculating and obtaining the ratio of the average task quantity required to be carried by the on-duty personnel processing the same type of tasks to the average carrying task quantity as the overload degree;
aiming at task types of the same emergency level, performing secondary forward sequencing according to the overload level from large to small;
if not, the transmission tasks of the data channel are forward ordered according to the emergency degree level of the transmission tasks.
By adopting the technical scheme, for the task types with the same emergency level, the ratio is calculated according to the average task carrying quantity of on-duty personnel and the average task quantity required to be carried, which correspond to different task types, the task types are secondarily sequenced from the big to the small according to the overload level, so that the transmission tasks which are required to be processed and have larger workload among the on-duty personnel and correspond to the task types are preferentially transmitted, and more processing time is given to the on-duty personnel with larger workload, thereby further reducing the influence of data acquisition on emergency case processing.
Optionally, calculating a ratio of an average task amount to be accepted by on-duty personnel processing the same type of task, wherein the ratio includes, as the overload degree:
analyzing and acquiring the current residual task quantity of on-duty personnel processing the same type of task, and acquiring the total residual task quantity;
according to the corresponding relation between the task type and the average task carrying quantity of the on-duty personnel and the total quantity of the residual tasks, analyzing and obtaining the actual average task carrying quantity of the on-duty personnel;
and calculating and obtaining the ratio of the average task quantity required to be accepted by the on-duty personnel processing the same type of task to the average accepted task quantity as the overload degree.
By adopting the technical scheme, when the average receiving task quantity is calculated, the total quantity of the residual tasks is removed to obtain the real average receiving task quantity, the ratio of the required receiving average task quantity to the real average receiving task quantity is calculated to be used as the overload degree, the overload degree calculated by the calculation of the average receiving task quantity is smaller due to the fact that the average receiving task quantity is larger by counting the total quantity of the residual tasks is avoided, the overload degree of on-duty personnel is reflected more truly, the data acquisition sequence is more in accordance with the real requirements of the on-duty personnel, and therefore the influence of data acquisition on emergency case processing is further reduced.
Optionally, analyzing and acquiring the current residual task amount of the on duty personnel processing the same type of task, and acquiring the total residual task amount includes:
analyzing whether the current residual task quantity of on-duty personnel processing the same type of task is 0;
if not, acquiring the residual progress of the current processed task and the amount of the task to be processed of the on-Shift personnel processing the same type of task, and counting to acquire the sum of the residual progress of the current processed task of all the on-Shift personnel as the total amount of the task in the processing;
and taking the sum of the task total amount in processing and the task total amount to be processed as the total amount of the residual tasks.
By adopting the technical scheme, the task amount in processing is counted into the total amount of the residual tasks, so that the situation that the average accepted task amount is larger and the calculated overload degree is smaller due to the fact that the residual task amount in processing is counted into the accepted task amount is avoided, the overload degree of on-duty personnel is reflected more truly, the data acquisition sequence is more in line with the real requirements of the on-duty personnel, and therefore the influence of data acquisition on emergency case processing is further reduced.
Optionally, the sum of the remaining progress of the tasks currently processed by all on-duty personnel is obtained in a statistical mode, and the sum of the remaining progress of the tasks currently processed by all on-duty personnel is used as the total amount of the tasks in the processing process, wherein the total amount of the tasks in the processing process comprises:
Acquiring the ratio of time consumption of completion of all on-duty personnel histories about the residual progress of the same task to the whole time consumption, and taking the corresponding ratio as the residual progress;
and counting and obtaining the sum of the overall residual progress as the total amount of the tasks in the processing.
By adopting the technical scheme, the task progress is calculated with the time-consuming proportion, so that the situation that the calculation of the residual task quantity does not accord with the actual processing situation due to different processing efficiency in the task process is avoided, the actual residual task situation can be reflected more truly, and the data acquisition sequence is more accord with the actual processing situation of on-duty personnel so as to reduce the influence of data acquisition on the emergency processing.
Optionally, obtaining a ratio of time consumption of completion of all on-duty personnel histories about the remaining progress of the same task to overall time consumption, and taking the corresponding ratio as the remaining progress includes:
acquiring off-duty time in the process of historic processing of the same task residual progress of all on-duty personnel;
calculating the actual completion time of all the on Shift personnel histories about the same task residual progress according to the off Shift time of all the on Shift personnel histories in the process of processing the same task residual progress and the completion time of all the on Shift personnel histories about the same task residual progress;
And calculating and obtaining the ratio of the actual completion time consumption of all the on-duty personnel histories about the residual progress of the same task to the overall time consumption, and taking the corresponding ratio as the residual progress.
By adopting the technical scheme, when the time consumption of the completion of the residual progress of the same task is the whole time consumption ratio of the histories of all the on-duty personnel, the off-duty time of the on-duty personnel in the time consumption of the completion of the residual progress of the same task is removed, and the accuracy of the time consumption calculation of the completion of the residual progress is prevented from being influenced by the on-duty personnel in the external work, vacation and the like in the processing process, so that the time consumption of the completion of the residual progress of the same task in the historical processing is reflected more truly, the calculation of the residual task quantity is more accurate, and the data acquisition sequence is more in accordance with the actual processing condition of the on-duty personnel so as to reduce the influence of data acquisition on emergency case processing.
In a second aspect, the application provides an automatic case data processing system based on artificial intelligence assistance, which adopts the following technical scheme:
an artificial intelligence assistance-based case data automatic processing system, comprising:
the acquisition module is used for acquiring a transmission task;
a memory for storing a program of the control method of the artificial intelligence-assisted case data automatic processing of the first aspect;
And the processor is used for loading and executing programs in the memory by the processor and realizing the control method based on the automatic case data processing assisted by the artificial intelligence in the first aspect.
By adopting the technical scheme, the program of the case data automatic processing method based on artificial intelligence assistance of the first aspect is stored through the memory, the acquisition module is used for acquiring the task type and task timeliness and transmitting the task timeliness to the processor, the processor is used for determining the residual time according to the task timeliness and the current time node analysis, the transmission task is divided into emergency degrees according to the residual time length, the emergency degree is higher when the residual time is shorter, the transmission task is transmitted according to the emergency degree grade order, and compared with the mode of originally transmitting according to the input order, the situation that the task with the longer residual time is influenced by the task priority transmission and the task with the shorter residual time is beyond the task processing timeliness is avoided, so that the influence of the transmission order of data butt joint on the emergency case processing can be reduced.
In a third aspect, the present application provides an automatic case data processing device based on artificial intelligence assistance, which adopts the following technical scheme:
an artificial intelligence assistance-based case data automatic processing apparatus includes a memory and a processor, the memory having stored thereon a computer program capable of being loaded by the processor and executing the method of the first aspect.
By adopting the technical scheme, the storage stores the program of the case data automatic processing method based on the artificial intelligence assistance in the first aspect, the processor can process the transmission task, determine the residual time according to the task aging and the node analysis of the current time, then divide the transmission task into emergency degrees according to the residual time length, and the emergency degree is higher when the residual time is shorter, so that the transmission task is transmitted according to the emergency degree grade order, and compared with the original transmission mode according to the input order, the situation that the task with the shorter residual time is influenced by the priority transmission of the task with the longer residual time and exceeds the task processing aging is avoided, so that the influence of the transmission order of data butt joint on the emergency case processing can be reduced.
In a fourth aspect, the present application provides a computer storage medium, capable of storing a corresponding program, and having a characteristic of being convenient for reducing an impact of data collection on emergency processing, and adopting the following technical scheme:
a computer storage medium storing a computer program capable of being loaded by a processor and executing the case data automatic processing method based on artificial intelligence assistance of the first aspect.
By adopting the technical scheme, the computer program of the method described in the first aspect is stored and can be loaded and executed by the processor, so that the method can be applied to a case mediation center or other needed mechanisms, when the mechanism collects mass data, the transmission tasks are divided according to the residual time length by the emergency degree, wherein the shorter the residual time is, the higher the emergency degree is, the transmission tasks are transmitted according to the emergency degree grade order, and compared with the mode of originally transmitting according to the input order, the influence of the transmission order of data docking on the emergency case processing can be reduced.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the transmission tasks are classified according to the residual time length, the emergency degree grades are higher when the residual time is shorter, the transmission tasks are ordered according to the emergency degree grades, whether the emergency degree grades are improved or not is judged according to the residual time interval in the task types, the on-duty personnel overload degree corresponding to the task types is ordered from high to low for the task types with the same emergency degree grade for the second time, data acquisition is carried out on the transmission tasks according to the ordering, and more urgent tasks are transmitted preferentially so that the influence of the transmission sequence of data butt joint on emergency case processing is reduced.
2. When the average accepting task quantity is calculated, the residual task quantity is removed to obtain the real average accepting task quantity, the task quantity in processing is calculated to the residual task quantity, the task progress in processing is calculated according to the time-consuming proportion, off-duty time of on-duty personnel is removed in calculation, so that the calculation of overload degree is more real, the order of transmitting tasks in secondary sequencing is more consistent with the actual processing condition of the on-duty personnel, and the influence of data acquisition on emergency case processing is reduced.
Drawings
Fig. 1 is a schematic flow chart of an automatic case data processing method based on artificial intelligence assistance according to an embodiment of the application.
FIG. 2 is a flow chart illustrating an analysis of determining urgency levels for different task types in accordance with another embodiment of the present application.
FIG. 3 is a flow chart illustrating the forward ordering of task types of a data lane according to the urgency level of different types of tasks according to another embodiment of the present application.
FIG. 4 is a flow chart of another embodiment of the present application for calculating the ratio of the average number of tasks to be accepted by on Shift personnel handling the same type of task as the degree of overload.
FIG. 5 is a flow chart of another embodiment of the present application for analyzing the current amount of tasks remaining for an on Shift person handling the same type of task and for obtaining the total amount of tasks remaining.
FIG. 6 is a flow chart of a process for statistically taking the sum of the remaining progress of all tasks currently being processed by on Shift personnel as the total amount of tasks in the process, according to another embodiment of the present application.
FIG. 7 is a flow chart of another embodiment of the present application for obtaining the ratio of the time spent completing all the on-Shift personnel histories on the remaining progress of the same task to the overall time spent and using the corresponding ratio as the remaining progress.
FIG. 8 is a system block diagram of an artificial intelligence assisted case data automation processing system according to an embodiment of the present application.
Reference numerals illustrate: 1. a first acquisition module; 2. a second acquisition module; 3. a first analysis module; 4. a second analysis module; 5. a sequencing processing module; 6. and executing the module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to fig. 1 to 8 and the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The embodiment of the application discloses an automatic case data processing method based on artificial intelligence assistance. Referring to fig. 1, the case data automatic processing method based on artificial intelligence assistance includes:
Step S100, a transmission task is obtained, wherein the transmission task comprises a task type and task timeliness.
The transmission task refers to a task that a case mediation center system collects files required by mediating cases through a database interface provided by a court after receiving a case mediation application.
After the application of the reconciliation case is acquired, information extraction can be performed through artificial intelligence assistance. The method mainly comprises the steps of deep learning historical case data, classifying and extracting key information in a case application text by using an NLP (natural language processing) algorithm to obtain information such as task timeliness of a transmission task, case-involved personnel, related institutions and the like, and matching historical cases with high similarity according to keywords by using an edit distance algorithm to obtain a task type and a required file type corresponding to the task type. And finally, traversing and searching in a data interface provided by a court according to the file type and the related mechanism to obtain a file corresponding interface of the related mechanism, and analyzing the case application through artificial intelligence to obtain a transmission task for subsequent processing.
The required file may be, for example, a complaint form obtained from a court database, a credit card acceptance contract, a repayment record, a consumption record, etc. obtained from a relevant bank database, a traffic accident record obtained from a car management, a insurance contract obtained from a relevant insurance company, etc. The transmission task can be obtained by leading in a foreground interface and submitting the interfaces in batches, wherein the interfaces submitted in batches are interfaces for interfacing with a post-loan system of a financial institution.
The task types include credit card cases, financial borrowing contract cases, financing lease contract cases, bill cases, insurance cases, securities cases, financial commission financial contract cases, and the like.
The task aging mainly refers to a time node of case processing, including a latest time node of cases and a time node of an intermediate program, for example, the expiration time of a borrow in a borrow repayment overdue dispute, and if the case mediation processing is not timely, the borrow overdue may affect case results.
Step S200, determining the remaining time according to task aging and current time node analysis.
The time node closest to the current time node in the case processing flow is selected by task aging, the current time node is obtained from a system clock, and the residual time is obtained by calculating the task aging and the difference value of the current time node and is used for evaluating the emergency degree of the transmission task on aging.
In step S300, according to the correspondence between the remaining time interval in which the remaining time falls and the emergency level, the emergency level of the different types of tasks is determined by analysis, and the emergency level can be classified as A, B, C, D.
The emergency level is used for evaluating the emergency level of the task, the calculated remaining time is compared with the remaining time interval in length, the interval of the maximum remaining time with the remaining time not larger than the remaining time is the falling remaining time interval, the corresponding relation between the remaining time interval and the emergency level is set by a worker, the emergency level corresponding to the longer the remaining time is lower, for example, the emergency level A corresponds to within ten days, the emergency level B corresponds to from ten days to one month, the emergency level C corresponds to from one month to half year, and the emergency level D corresponds to more than half year.
Step S400, forward ordering is carried out on task types of the data channel according to the emergency degree grades of the tasks of different types, wherein the forward ordering is the ordering from high to low according to the emergency degree grades.
Specifically, the task types are ranked according to the order of the emergency degree from high to low, namely according to the order of A, B, C, D.
Step S500, executing the transmission task according to the ordering condition of the task types.
The main content of the execution of the transmission task is files required by acquiring the cases from each interface. For example, if the task is a credit card expiration case, the required file may be determined according to information of the file collected by the credit card expiration case. Taking a credit card repayment record file as an example, according to the characteristic field contained in the transmission task, the information such as the name, the identification card number and the like of the person involved in the case and the bank name can be obtained, and the interface provided by the court is inquired to a bank credit card record database interface according to the bank name, namely, the information of the person involved in the case is input into the bank credit card record database interface to inquire the credit card repayment record of the person involved in the case.
The implementation principle of this embodiment is as follows: the transmission tasks are divided according to the difference value between the next time node and the current time node of the tasks, the emergency degree grade of the transmission tasks is judged according to the emergency degree grade, the transmission tasks are ordered and executed according to the emergency degree grade from high to low, and compared with the mode of originally carrying out transmission according to the input sequence, the problem that the tasks with long residual time are influenced by the task with short residual time and exceed task processing timeliness due to the priority transmission of the tasks with long residual time is avoided, so that the influence of the transmission sequence of data docking on emergency case processing can be reduced.
In step S300 of the embodiment shown in fig. 1, in order to further ensure accuracy of the emergency level classification, further analysis of the emergency level corresponding to the task type is required, and specifically, the detailed description is given by the embodiment shown in fig. 2.
Referring to fig. 2, the analysis to determine the urgency level of different task types includes the steps of:
in step S310, it is analyzed whether the obtained task type is an urgent task preset with an urgent level. If yes, go to step S320; if not, step S350 is performed.
The preset emergency level is obtained by inquiring a database storing the emergency level corresponding to the task type. For example, the task type sets a major financial case, and the emergency level of the major financial case is a, and the level corresponding to the longer remaining time is D, so that further analysis of the emergency level of the transmission task is required.
In step S320, the emergency level with the corresponding task type is obtained by comparison.
Specifically, the urgency level a of the significant financial case stored in the database is obtained.
Step S330, analyzing and determining the influence degree of the emergency degree according to the corresponding relation between the residual time interval in which the residual time falls and the influence degree of the emergency degree.
Specifically, the influence degree of the emergency degree is determined by comparing the residual time interval corresponding to the task type with the influence degree of the emergency degree.
According to the different time intervals, the influence degree of the emergency degree is different, the influence degree of the emergency degree is smaller for the time interval with shorter time intervals, and the influence degree of the emergency degree is relatively larger for the time interval with longer time intervals.
For example, for a case with a preset emergency level B, the corresponding influence degree when the emergency level corresponding to the interval in which the remaining time falls is a is 0.1, the corresponding influence degree when the emergency level corresponding to the interval in which the remaining time falls is B is 0.4, and the corresponding influence degree when the emergency level corresponding to the interval in which the remaining time falls is C is 0.8.
In step S340, if the emergency level is lower than the highest level and the influence level of the emergency level exceeds the preset influence level, the emergency level of the corresponding task type is increased by one level.
The preset influence level may be obtained from a database storing the preset influence level. When the influence degree of the emergency degree exceeds the preset influence degree, the emergency degree grade corresponding to the residual time interval corresponding to the task type is insufficient to judge the emergency degree of the task type, so that the emergency degree grade is improved. For example, the preset influence degree is 0.5, and for the case with the preset emergency degree grade B, when the emergency degree grade corresponding to the interval in which the remaining time falls is C, the influence degree is 0.8, and the emergency degree is promoted by one grade, namely B.
Step S350, analyzing and determining the emergency degree grade of the task type according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade.
When the influence degree of the emergency degree is lower than the preset influence degree, the emergency degree grade corresponding to the residual time interval corresponding to the task type is indicated to be enough to judge the emergency degree of the task type, so that the emergency degree grade corresponding to the residual time interval is taken as the emergency degree grade of the task type. For example, the preset influence degree is 0.5, and for the case with the preset emergency degree grade B, when the emergency degree grade corresponding to the interval in which the remaining time falls is a or B, the influence degree is lower than 0.5, and the grade does not need to be raised.
The implementation principle of this embodiment is as follows: the emergency degree is preset for the task type in the transmission task, the emergency degree influence degree corresponding to the task type can be determined according to the emergency degree influence degree corresponding to the residual time, the emergency degree influence degree is compared with the preset influence degree to determine whether the emergency degree level of the task type is improved or unchanged, and special cases needing emergency treatment in the cases can be transmitted preferentially further, so that the influence of data acquisition on emergency case treatment is further reduced.
In step S400 of the embodiment shown in fig. 1, in order to further ensure the rationality of the transmission task ordering, further analysis of the transmission task ordering with the same level of urgency is required, specifically, the embodiment shown in fig. 3 is described in detail.
Referring to fig. 3, forward ordering task types of a data lane according to urgency levels of different types of tasks includes the steps of:
step S410, analyze whether there is a task type with the same urgency level. If yes, go to step S420; if not, step S450 is performed.
And counting the number of task types of each emergency level, and if the number of task types of the same emergency level is greater than one, further sequencing the task types of the same emergency level is needed.
Step S420, the number of different types of tasks contained in the transmission task and the number of on-duty personnel for processing the different types of tasks are obtained, and the average task quantity required to be accepted by the on-duty personnel for processing the different types of tasks is calculated.
The number of on-duty personnel refers to the number of personnel currently available for task processing, and the number of on-duty personnel can be obtained from an attendance system for adjusting the center of gravity. And calculating the ratio of the number of tasks corresponding to the task types in the transmission task to the number of on-duty personnel so as to obtain the average task quantity required to be accepted by the on-duty personnel corresponding to different task types. For example, assuming that four on-duty persons of the same type are first, second, third and fourth, and the number of tasks corresponding to the task types in the transmission task is ten, the average number of tasks to be accepted is 2.5.
Step S430, calculating and obtaining the ratio of the average task quantity required to be accepted by the on-Shift personnel processing the same type of task to the average accepted task quantity as the overload degree according to the corresponding relation between the task type and the average accepted task quantity of the on-Shift personnel and the average task quantity required to be accepted by the on-Shift personnel processing different types of tasks.
The corresponding relation between the task type and the average job receiving amount of the on-duty personnel is obtained from a database storing the corresponding relation between the task type and the average job receiving amount of the on-duty personnel. For example, assuming that four on-duty persons of the same type are first, second, third and fourth persons, respectively, the historical average carrying capacity of the first, second, third and fourth persons in unit time is 1.5 pieces, the average task capacity required to be carried is 2.5 pieces, and the overload degree of the first, second, third and fourth persons is 1.67.
Step S440, aiming at the task types with the same emergency level, performing secondary forward sequencing according to the overload level from large to small.
For example, the overload degree of the first, second, third and fourth on Shift personnel is 1.67, the overload degree of the fifth and sixth on Shift personnel is 1.5, the overload degree of the seventh and eighth on Shift personnel is 2.5, the seventh and eighth on Shift personnel transmit the first transmission task, the second transmission task, the fifth and sixth on Shift personnel transmit the last transmission task, and the fourth on Shift personnel transmit the first, second, third and fourth on Shift personnel.
Step S450, forward ordering the transmission tasks of the data channel according to the urgency level of the transmission tasks.
The forward ordering of the transmission tasks of the data channel according to the urgency level of the transmission tasks is described in step S400, and will not be described here.
The implementation principle of this embodiment is as follows: for the task types with the same emergency level, calculating the ratio according to the average task carrying quantity of on-Shift personnel and the average task quantity required to be carried, which correspond to different task types, and sequencing the task types twice according to the order of the overload level from large to small, so that the transmission tasks which are required to be processed and have larger workload in the on-Shift personnel and correspond to the task types are preferentially transmitted, thereby giving the on-Shift personnel with larger workload more processing time, and further reducing the influence of data acquisition on emergency case processing.
In step S430 of the embodiment shown in fig. 3, in order to further ensure the rationality of the calculated overload level, a further analysis of the average amount of tasks received by the on Shift personnel corresponding to the task type is required, specifically, the embodiment shown in fig. 4 is described in detail.
Referring to fig. 4, calculating the ratio of the average task volume to the average task volume accepted by the on Shift personnel who process the same type of task as the overload level includes the steps of:
Step S431, analyzing and acquiring the current residual task quantity of the on-duty personnel processing the same type of task, and acquiring the total residual task quantity.
The residual task amount refers to the number of tasks which are not processed currently by the on Shift personnel and can be obtained through task result statistics, for example, four on Shift personnel of the same type are respectively the residual task amount of A, B, C and T, wherein the residual task amount of A is 0, the residual task amount of B is 1, the residual task amount of C is 1, the residual task amount of T is 0, and the total obtained residual task amount is obtained through accumulation of the calculated number of tasks which are not processed, namely 2.
Step S432, analyzing and obtaining the real average task carrying quantity of the on-duty personnel according to the corresponding relation between the task type and the average task carrying quantity of the on-duty personnel and the total quantity of the residual tasks.
The true average task load is the difference of the average task load minus the total residual tasks, and is used for more accurately evaluating the task load that can be accepted by the on-duty personnel. For example, the average receiving task amount of the same type of on-duty personnel is 1.5 pieces, the total amount of the residual tasks is 2, the average residual task amount of four people is 0.5 pieces, and the real average receiving task amount is 1.0 piece.
Step S433, calculating and obtaining the ratio of the average task quantity to the average task quantity required to be accepted by the on-duty personnel processing the same type of tasks as the overload degree.
For example, the real average task load is 1.0, the average task load required to be received is 2.5, and the overload degree is 2.5.
The implementation principle of this embodiment is as follows: when the average receiving task quantity is calculated, the total quantity of the residual tasks is removed to obtain the real average receiving task quantity, the ratio of the required receiving average task quantity to the real average receiving task quantity is calculated to be used as the overload degree, the overload degree calculated by the calculation of the average receiving task quantity is smaller due to the fact that the average receiving task quantity is larger by counting the total quantity of the residual tasks is avoided, the overload degree of on-duty personnel is reflected more truly, the data acquisition sequence is more in line with the real requirements of the on-duty personnel, and therefore the influence of data acquisition on emergency case processing is further reduced.
In step S431 of the embodiment shown in fig. 4, in order to further ensure the accuracy of the total amount of the remaining tasks, further analysis of the remaining tasks is required, specifically, the embodiment shown in fig. 5 is described in detail.
Referring to FIG. 5, analyzing the current amount of remaining tasks of an on Shift person who is handling the same type of task and obtaining the total amount of remaining tasks includes the steps of:
step SA00, analyze whether the current residual task amount of the on duty personnel processing the same type of task is 0. If yes, the residual task amount is 0; if not, step SB00 is executed.
The residual task quantity refers to the number of tasks which are not processed by the on-duty personnel at present and can be obtained through task result statistics.
Step SB00, the residual progress of the task currently processed by the on Shift personnel processing the same type of task and the amount of the task to be processed are obtained, and the sum of the residual progress of the task currently processed by all the on Shift personnel is obtained through statistics and used as the total amount of the task in processing.
The current remaining progress of the processed task refers to the ratio of the remaining task amount of the task in the processing of the on-Shift personnel to the entire task amount, for example, the remaining task progress of the first is 0.2, the remaining task progress of the second is 0.3, the remaining task progress of the third is 0.2, the remaining task progress of the fourth is 0.3, and the total amount of the task in the processing is 1.0.
And step SC00, taking the sum of the task total amount in process and the task total amount to be processed as the total amount of the residual tasks.
The total amount of the residual tasks is the total amount of the tasks in the process of all the on-duty personnel and the accumulated amount of the tasks to be processed. For example, the total amount of tasks in the processing of the first, second, third and fourth is 1.0, the amount of tasks to be processed is 2, and the total amount of the remaining tasks is 3.0.
The implementation principle of this embodiment is as follows: the method comprises the steps of adding the processed task amount into the total amount of the residual tasks so as to avoid the situation that the average accepted task amount is larger and the calculated overload degree is smaller due to the fact that the residual task amount in the processing is added into the accepted task amount, so that the overload degree of on-duty personnel is reflected more truly, the data acquisition sequence is more in line with the real requirements of the on-duty personnel, and the influence of the data acquisition on emergency case processing is further reduced.
In step SB00 of the embodiment shown in fig. 5, further analysis of the remaining progress is required in order to further secure the accuracy of the remaining progress, specifically, the embodiment shown in fig. 6 will be described in detail.
Referring to FIG. 6, the statistics of the sum of the remaining progress of all tasks currently processed by the on Shift personnel is obtained as the total amount of tasks in the process including the steps of:
step SB10, the ratio of the time consumption of the completion of all the on-duty personnel histories about the residual progress of the same task to the overall time consumption is obtained, and the corresponding ratio is used as the residual progress.
The time consumption of the completion of the residual progress of the same task is obtained from a database storing the time consumption of the completion of the same task of all the on-duty personnel histories, and as the time consumption of different task blocks in the task processing is not necessarily proportional to the task quantity, the residual progress is estimated to be more consistent with the actual residual progress by using the time consumption of the residual progress, for example, the time consumption of the previous 50% of the task of the same task is 0.2 days, the time consumption of the next 50% of the task is 0.3 days, the time of the previous 50% of the task is 0.4, and the time of the next 50% of the task is 0.6.
Step SB20, the sum of the overall remaining progress is obtained statistically and used as the total amount of tasks in the process.
The sum of the overall remaining progress is calculated as an accumulated value of the time consumption rate of completion of the remaining progress of all the on-duty personnel, for example, the same type of on-duty personnel is four people, namely, A, B, C and T, the remaining task progress of A is 0.2, the remaining task progress of B is 0.3, the remaining task progress of C is 0.2, the remaining task progress of T is 0.3, and the total amount of the tasks in the process is 1.0.
The implementation principle of this embodiment is as follows: the task progress is calculated according to the time consumption ratio, so that the situation that the calculation of the residual task quantity does not accord with the actual processing situation due to the different processing efficiencies of different task blocks is avoided, the actual residual task situation can be reflected more truly, the data acquisition sequence is more accord with the actual processing situation of on-duty personnel, and the influence of the data acquisition on the emergency case processing is reduced.
In step SB10 of the embodiment of FIG. 6, further analysis of all on Shift personnel history regarding completion time of the remaining progress of the same task is needed to further ensure accuracy of the remaining progress, specifically by way of the embodiment of FIG. 7.
Referring to FIG. 7, the ratio of the time consumed for completion of all on-Shift personnel histories with respect to the remaining progress of the same task to the overall time consumed is obtained, and the corresponding ratio is taken as the remaining progress, comprising the steps of:
step SB11, obtaining off-duty time in the process of historic processing of the same task residual progress by all on-duty personnel.
For example, if the average outdoor duty is 0.05 days and the average leave duty is 0.05 days during the process of historic processing of the same task, the off-duty time is 0.1 days during the process of historic processing of the same task.
Step SB12, calculating the actual completion time of all the on Shift personnel histories about the same task residual progress according to the off Shift time of all the on Shift personnel histories in the process of processing the same task residual progress and the completion time of all the on Shift personnel histories about the same task residual progress.
For example, the time taken for all on Shift personnel to complete the history of the remaining progress of the same task is 0.3 days, while the time taken for all on Shift personnel to leave the post in the history of processing the remaining progress of the same task is 0.1 days, then the time taken for all on Shift personnel to complete the history of the remaining progress of the same task is 0.2 days.
Step SB13, calculating and obtaining the ratio of the actual completion time consumption of all the on-duty personnel histories about the residual progress of the same task to the whole time consumption, and taking the corresponding ratio as the residual progress.
For example, the overall time consumption of the same task for all on Shift personnel histories averages 0.5 days, wherein the overall time consumption has been removed from the off Shift time, and the actual completion time of the same task for all on Shift personnel histories is 0.2 days, then the remaining progress is 0.4.
The implementation principle of this embodiment is as follows: when the time consumption of the completion of the residual progress of the same task is the whole time consumption ratio of the histories of all the on Shift persons, the off Shift time of the on Shift persons in the time consumption of the completion of the residual progress of the same task is removed, and the accuracy of the time consumption calculation of the completion of the residual progress is prevented from being influenced by the on Shift persons in the external work, the holiday and the like in the processing process, so that the time consumption ratio of the completion of the residual progress of the same task in the history processing can be reflected more truly, the calculation of the residual task quantity is more accurate, and the data acquisition sequence is more in accordance with the actual processing condition of the on Shift persons so as to reduce the influence of data acquisition on the emergency case processing.
Referring to fig. 8, based on the same inventive concept, an embodiment of the present invention provides an artificial intelligence assistance-based case data automatic processing system, including:
the first acquisition module 1 is used for acquiring task timeliness.
And the second acquisition module 2 is used for acquiring the task type.
The first analysis module 3 is used for determining the remaining time according to task aging and current time node analysis.
The second analysis module 4 analyzes and determines the emergency degree grades of different types of tasks according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grades, and the emergency degree grades can be classified into A, B, C, D.
The sorting processing module 5 performs forward sorting on the task types of the data channel according to the emergency degree grades of the tasks of different types, wherein the forward sorting is sorting from high to low according to the emergency degree grades.
And the execution module 6 is used for executing the transmission tasks according to the ordering condition of the task types.
The implementation principle of the embodiment is as follows: the first acquisition module 1 is used for acquiring task types and transmitting the task types to the second analysis module 4, the second acquisition module 2 is used for acquiring task timeliness and transmitting the task timeliness to the first analysis module 3, the first analysis module 3 is used for analyzing and determining the residual time according to task timeliness and current time nodes, the second analysis module 4 is used for analyzing and determining the emergency degree grades of different types of tasks, the sorting processing module 5 is used for carrying out forward sorting on the task types of the data channels according to the emergency degree grades of the different types of tasks, and finally the execution module 6 is used for executing transmission tasks according to the sorting condition of the task types, so that more urgent tasks are preferentially executed to reduce the influence of data acquisition on emergency case processing.
Based on the same inventive concept, the embodiment of the invention provides an automatic case data processing device based on artificial intelligence assistance, which comprises a memory, a processor and a program stored on the memory and capable of running on the processor, wherein the stored program can be loaded and executed by the processor to realize the automatic case data processing method based on artificial intelligence assistance as shown in any one of figures 1 to 7.
The implementation principle of the embodiment is as follows: the computer program based on any one of the automatic case data processing methods assisted by artificial intelligence is stored in the memory, and then the computer program stored in the memory is loaded and executed by the processor, so that the data acquisition sequence is more consistent with the case emergency and the on-duty personnel processing conditions, and the influence of the data acquisition on the emergency case processing is reduced.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The embodiment of the invention provides a computer storage medium storing a computer program capable of implementing a method as in any one of fig. 1 to 7 when loaded and executed by a processor.
The computer storage medium includes, for example: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the preferred embodiments of the present application is not intended to limit the scope of the application, in which any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Claims (10)
1. An automatic case data processing method based on artificial intelligence assistance is characterized by comprising the following steps:
acquiring a transmission task, wherein the transmission task comprises a task type and task timeliness;
determining the residual time according to task aging and current time node analysis;
according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade, analyzing and determining the emergency degree grade of different types of tasks, wherein the emergency degree grade can be classified as A, B, C, D;
Forward ordering is carried out on the task types of the data channels according to the emergency degree grades of the tasks of different types, wherein the forward ordering is the ordering from high to low according to the emergency degree grades;
and executing the transmission tasks according to the ordering condition of the task types.
2. The automatic case data processing method based on artificial intelligence assistance according to claim 1, wherein the analyzing and determining the urgency level of different task types comprises:
analyzing whether the acquired task type is an emergency task with a preset emergency level;
if yes, comparing and acquiring the emergency degree grade with the corresponding task type;
analyzing and determining the influence degree of the emergency degree according to the corresponding relation between the residual time interval in which the residual time falls and the influence degree of the emergency degree;
if the emergency degree level is lower than the highest level and the influence degree of the emergency degree exceeds the preset influence degree, increasing the emergency degree of the corresponding task type by one level;
if not, analyzing and determining the emergency degree grade of the task type according to the corresponding relation between the residual time interval in which the residual time falls and the emergency degree grade.
3. The automatic case data processing method based on artificial intelligence assistance according to claim 1 or 2, wherein the forward ordering of task types of the data channel according to the urgency level of different types of tasks comprises:
Analyzing whether task types with the same emergency degree level exist or not;
if yes, acquiring the number of different types of tasks contained in the transmission task and the number of on-duty personnel for processing the different types of tasks, and calculating the average task quantity required to be accepted by the on-duty personnel for processing the different types of tasks;
according to the corresponding relation between the task type and the average task carrying quantity of the on-duty personnel and the average task quantity required to be carried by the on-duty personnel processing different types of tasks, calculating and obtaining the ratio of the average task quantity required to be carried by the on-duty personnel processing the same type of tasks to the average carrying task quantity as the overload degree;
aiming at task types of the same emergency level, performing secondary forward sequencing according to the overload level from large to small;
if not, the transmission tasks of the data channel are forward ordered according to the emergency degree level of the transmission tasks.
4. The automatic case data processing method based on artificial intelligence assistance according to claim 3, wherein calculating a ratio of an average amount of tasks to be accepted by on-duty personnel who handle the same type of tasks to the average amount of accepted tasks as an overload degree includes:
analyzing and acquiring the current residual task quantity of on-duty personnel processing the same type of task, and acquiring the total residual task quantity;
According to the corresponding relation between the task type and the average task carrying quantity of the on-duty personnel and the total quantity of the residual tasks, analyzing and obtaining the actual average task carrying quantity of the on-duty personnel;
and calculating and obtaining the ratio of the average task quantity required to be accepted by the on-duty personnel processing the same type of task to the average accepted task quantity as the overload degree.
5. The automatic case data processing method based on artificial intelligence assistance according to claim 4, wherein analyzing and acquiring the current residual task amount of an on-duty person who handles the same type of task and acquiring the total residual task amount comprises:
analyzing whether the current residual task quantity of on-duty personnel processing the same type of task is 0;
if not, acquiring the residual progress of the current processed task and the amount of the task to be processed of the on-Shift personnel processing the same type of task, and counting to acquire the sum of the residual progress of the current processed task of all the on-Shift personnel as the total amount of the task in the processing;
and taking the sum of the task total amount in processing and the task total amount to be processed as the total amount of the residual tasks.
6. The automatic case data processing method based on artificial intelligence assistance according to claim 5, wherein statistically obtaining the sum of the remaining progress of all tasks currently processed by on-duty personnel as the total amount of tasks in the processing comprises:
Acquiring the ratio of time consumption of completion of all on-duty personnel histories about the residual progress of the same task to the whole time consumption, and taking the corresponding ratio as the residual progress;
and counting and obtaining the sum of the overall residual progress as the total amount of the tasks in the processing.
7. The automatic case data processing method based on artificial intelligence assistance according to claim 6, wherein the steps of obtaining a ratio of time consumed for completion of all on-duty personnel histories about the remaining progress of the same task to the total time consumed, and using the corresponding ratio as the remaining progress comprises:
acquiring off-duty time in the process of historic processing of the same task residual progress of all on-duty personnel;
calculating the actual completion time of all the on Shift personnel histories about the same task residual progress according to the off Shift time of all the on Shift personnel histories in the process of processing the same task residual progress and the completion time of all the on Shift personnel histories about the same task residual progress;
and calculating and obtaining the ratio of the actual completion time consumption of all the on-duty personnel histories about the residual progress of the same task to the overall time consumption, and taking the corresponding ratio as the residual progress.
8. An automatic case data processing system based on artificial intelligence assistance, comprising:
The acquisition module is used for acquiring a transmission task;
a memory for storing a program of the control method for artificial intelligence-assisted case data automatic processing according to any one of claims 1 to 7;
a processor, a program in a memory capable of being loaded and executed by the processor and implementing the control method for artificial intelligence assistance-based case data automatic processing according to any one of claims 1 to 7.
9. An automatic case data processing device based on artificial intelligence assistance, characterized by comprising a memory and a processor, wherein the memory stores a computer program capable of being loaded by the processor and executing the method according to any one of claims 1 to 7.
10. A computer storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any one of claims 1 to 7.
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