CN116993083A - Comprehensive command scheduling system and method based on big data - Google Patents
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Abstract
The invention discloses a comprehensive command and dispatch system and method based on big data, which relate to the technical field of big data, and specifically comprise the following steps: step S1: extracting characteristic information of each received letter content, and setting a difficulty degree grading value and a grading weighting weight for each characteristic information; step S2: calculating the processing difficulty degree grading value of each letter, and evaluating the priority processing level of each letter according to the calculated result; step S3: matching each letter with a corresponding scheduling strategy according to the priority processing level of each letter; step S4: based on the results of priority evaluation and scheduling policy matching of the letters, personnel of the letter event investigation team are dynamically adjusted on the basis of meeting the resource constraint and response time requirements. The invention can improve the processing efficiency and quality of letter problems and bring beneficial effects to the optimization of the comprehensive command and dispatch system.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a comprehensive command scheduling system and method based on big data.
Background
The traditional letter comprehensive commanding and dispatching system has some disadvantages and shortcomings. First, in the problem evaluation stage, lack of uniform evaluation standards and criteria, subjective judgment easily causes inconsistency of evaluation results, and affects the accuracy of priority ranking and scheduling decision of the problem. In addition, personnel adjustment is often based on a fixed scheduling strategy, real-time and dynamic personnel configuration cannot be realized, and therefore personnel allocation is unreasonable, and the capability and efficiency of professionals cannot be fully exerted. Therefore, in order to improve the traditional letter comprehensive command and dispatch system, a big data technology is introduced to improve the accuracy of feature information acquisition, a unified evaluation standard and a model are established to improve the objectivity of evaluation, and a real-time data and intelligent algorithm are adopted to dynamically adjust a dispatch strategy.
Disclosure of Invention
The invention aims to provide a comprehensive command and dispatch system and method based on big data, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a comprehensive command scheduling method based on big data includes: step S1: and step S2 of extracting the characteristic information of each received letter content and setting a difficulty degree grading value and a grading weighting weight for each characteristic information: calculating the processing difficulty degree grading value of each letter, and evaluating the priority processing level of each letter according to the calculated result; step S3: matching each letter with a corresponding scheduling strategy according to the priority processing level of each letter; step S4: based on the results of priority evaluation and scheduling policy matching of the letters, personnel of the letter event investigation team are dynamically adjusted on the basis of meeting the resource constraint and response time requirements.
Further, the working process for extracting the characteristic information and scoring the difficulty level of each letter comprises the following steps:
s1-1: extracting the main information of the sender and the main information of the receiver in each letter content; wherein the subject comprises a unit or individual; setting the main body information of each letter as first characteristic information of each letter;
s1-2: constructing word libraries of various letters; wherein the letter categories include: complaint letters, help letters and advice letters; extracting a letter content part of each letter, extracting keywords from the letter content part, and generating a keyword set corresponding to each letter; matching the keyword set with the word library of each type of letters respectively, and determining the corresponding type of each letter according to the keyword set with the highest matching degree and the word library of each type of letters; setting the category of each letter as second characteristic information of each letter;
s1-3: dividing sentences in each letter content to obtain a sentence list, calculating the length of each sentence in the sentence list according to the number of characters, summing the lengths of each sentence to obtain the total length of the sentence, and obtaining the average length of the sentences according to the total length of the sentence and the number of the sentences; secondly, scanning each sentence in each letter content, defining long sentences when the sentences in the letter content exceed the average length, and defining short sentences when the sentences in the letter content are shorter than the average length; thirdly, calculating the ratio of the number of long sentences in each letter to the total number of sentences; setting the average sentence length and the long sentence occupation ratio of each letter as third characteristic information of each letter;
s1-4: different difficulty degree grading values A are respectively given to the main body types of different letters, different difficulty degree grading values B are respectively given to the categories of different letters, different difficulty degree grading values C are respectively given to the average length and the long sentence occupation ratio of different letters, the grading weighting weight of the first characteristic information is defined as th1, the grading weighting weight of the second characteristic information is defined as th2, and the grading weighting weight of the third characteristic information is defined as th3; wherein, th2> th1> th3.
Further, the working process of calculating the processing difficulty degree grading value V of each letter and determining the priority processing level of each letter according to the calculated result comprises the following steps:
s2-1: and calculating a processing difficulty degree grading value V of each letter according to the formula:
V＝A*th1+B*th2+C*th3
s2-2: according to the processing difficulty degree grading value V of each letter, the priority processing grade arrangement of each letter is carried out, the letters are divided into three grades from high to low, and when the processing difficulty degree grading value V of the letter is more than G, the corresponding letter is divided into a first priority processing grade; when the processing difficulty degree grading value F of the letters is less than V and less than G, dividing the corresponding letters into second priority processing levels; when the processing difficulty degree grading value V of the letters is less than or equal to F, dividing the corresponding letters into a third priority processing level; where G and F are adjustable thresholds.
Further, the comprehensive command scheduling system forms letters investigation team people number Mi with different priority treatment levels by scheduling people with different numbers and different professional levels, and the method is based on the formula:
Mi＝pi*g1+hi*g2+ki*g3
wherein i is the number of different priority treatment levels, g1 is the total number of advanced professionals, g2 is the total number of intermediate professionals, g3 is the total number of low professionals, pi represents the corresponding duty ratio weight of g1 under the ith priority treatment level, hi represents the corresponding duty ratio weight of g2 under the ith priority treatment level, ki represents the corresponding duty ratio weight of g3 under the ith priority treatment level, if a letter is the first priority treatment level, p1 x g1> h1 x g2> k1 x g3; if the letter is the second priority, h2 g2> p2 g1> k2 g3; if the letter is of the third priority, then k3 g3> h3 g2> p3 g1; finally, matching the letters with different priority levels with the letter investigation team persons Mi with different priority levels.
Further, setting the number of high-level professionals as G1, setting the number of medium-level professionals as G2, setting the number of low-level professionals as G3, and ensuring that the number of professionals allocated to each letter investigation team does not exceed the constraint value of the number of professionals, namely, G1 is less than or equal to G1, G2 is less than or equal to G2, and G3 is less than or equal to G3; setting the corresponding specified processing time of the letter investigation team persons Mi with different priority levels as Ti, setting the actual processing time of the letter investigation team with different priority levels as Ti, when Ti is less than or equal to Ti, the letter investigation team persons Mi with different priority levels do not need to be adjusted, when Ti is more than Ti, the letter investigation team persons Mi with different priority levels readjust personnel composition, and distributing n1 x g1 advanced professionals to the investigation team; wherein n1 is dynamically adjusted according to the emergency degree of each letter processing.
Furthermore, for better realizing the method, the system also provides a comprehensive command scheduling system based on big data, and the system comprises a characteristic information extraction and analysis module, a letter priority processing level assessment module, a scheduling strategy matching module and an optimal scheduling scheme confirmation module;
the feature information extraction and analysis module extracts feature information of the received letters and sets a difficulty degree grading value and a grading weighting weight for each feature information;
the letter priority processing grade evaluation module calculates the processing difficulty grade value of each letter according to the difficulty grade value and the grade weighting weight set by each characteristic information, and determines the priority processing grade of each letter according to the calculated result;
the scheduling policy matching module matches each letter problem with a preset scheduling policy according to the priority processing level of each letter problem, wherein the scheduling policy comprises schemes of allocating different teams, assigning specific personnel and allocating time resources;
on the premise of meeting the resource constraint and response time requirements, the optimal scheduling scheme confirming module sets constraint values of the number of each professional and the specified processing time of each letter based on the matching of the evaluation results of the priority processing levels of the letters and the scheduling strategies, and dynamically adjusts the personnel of each letter event investigation team.
Further, the characteristic information extraction and analysis module comprises a characteristic information extraction unit and a scoring weight setting unit, wherein the characteristic information extraction unit firstly obtains the main body information of each letter, secondly constructs a word library of each type of letter, determines the type of the letter by extracting keywords, and performs sentence segmentation on the content of each letter again, defines long short sentences and calculates the occupation ratio of the long sentences; the scoring weight setting unit sets a difficulty degree scoring value and a scoring weight for each piece of feature information.
Further, the letter priority processing level evaluation module comprises a difficulty degree scoring calculation unit and a priority processing level determination unit, wherein the difficulty degree scoring calculation unit calculates the processing difficulty degree scoring value of each letter through the difficulty degree scoring value and the scoring weighting weight of each piece of characteristic information; the priority processing level determining unit performs a custom threshold according to the calculated result to determine the priority processing level of each letter.
Further, the scheduling policy matching module comprises a team allocation unit, a personnel assignment unit and a priority level matching unit, wherein the team allocation unit allocates the questions to teams with corresponding priority levels for processing according to the priority level of each letter question; the personnel assignment unit assigns different grades and different numbers of experts to deal with letter questions of different priority treatment grades; the priority processing level matching unit matches the priority processing level of each letter problem with a preset scheduling strategy, and determines the processing sequence and the priority processing level of the problem.
Further, the optimal scheduling scheme confirmation module comprises a resource constraint setting unit, a processing time specifying unit and a personnel adjusting unit; the resource constraint setting unit sets constraint values of the number of each professional, so that reasonable allocation and utilization of resources are ensured; the processing time prescribing unit sets prescribed processing time for each letter, so as to ensure that the problem is processed in time; the personnel adjusting unit dynamically adjusts personnel of each letter investigation team based on the evaluation result of each letter problem and the matching of the scheduling strategy, and ensures reasonable configuration of professionals.
Compared with the prior art, the invention has the following beneficial effects: the invention extracts the characteristic information of the received letters and sets the difficulty degree score and the score weighting weight for each characteristic information; secondly, calculating the processing difficulty degree score of each letter according to the difficulty degree score and the scoring weighting weight set by each piece of characteristic information, and determining the priority processing level of the letter according to the calculated result; thirdly, the system matches the priority of the letter problem by setting a scheduling strategy in advance, so that the fine management of scheduling decisions is realized; finally, based on the results of letter problem evaluation and scheduling policy matching, the invention can dynamically adjust investigation team constituent personnel for letter events with different priority processing levels, and ensure reasonable configuration of different professionals. Compared with the prior art, the invention has higher degree of automation, accuracy and flexibility, can improve the processing efficiency and quality of the letter problem, and brings remarkable beneficial effects for optimizing the comprehensive command and dispatch system and improving the letter work.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a big data based integrated command and dispatch system of the present invention;
fig. 2 is a step diagram of a comprehensive command and dispatch method based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: a comprehensive command scheduling method based on big data includes: step S1: and step S2 of extracting the characteristic information of each received letter content and setting a difficulty degree grading value and a grading weighting weight for each characteristic information: calculating the processing difficulty degree grading value of each letter, and evaluating the priority processing level of each letter according to the calculated result; step S3: matching each letter with a corresponding scheduling strategy according to the priority processing level of each letter; step S4: based on the results of priority evaluation and scheduling policy matching of the letters, personnel of the letter event investigation team are dynamically adjusted on the basis of meeting the resource constraint and response time requirements.
In step S1, the working process of extracting feature information and scoring difficulty level of each letter includes:
s1-1: extracting the main information of the sender and the main information of the receiver in each letter content; wherein the subject comprises a unit or individual; setting the main body information of each letter as first characteristic information of each letter;
s1-2: constructing word libraries of various letters; wherein the letter categories include: complaint letters, help letters and advice letters; extracting a letter content part of each letter, extracting keywords from the letter content part, and generating a keyword set corresponding to each letter; matching the keyword set with the word library of each type of letters respectively, and determining the corresponding type of each letter according to the keyword set with the highest matching degree and the word library of each type of letters; setting the category of each letter as second characteristic information of each letter;
s1-3: dividing sentences in each letter content to obtain a sentence list, calculating the length of each sentence in the sentence list according to the number of characters, summing the lengths of each sentence to obtain the total length of the sentence, and obtaining the average length of the sentences according to the total length of the sentence and the number of the sentences; secondly, scanning each sentence in each letter content, defining long sentences when the sentences in the letter content exceed the average length, and defining short sentences when the sentences in the letter content are shorter than the average length; thirdly, calculating the ratio of the number of long sentences in each letter to the total number of sentences; setting the average sentence length and the long sentence occupation ratio of each letter as third characteristic information of each letter;
s1-4: different difficulty degree grading values A are respectively given to the main body types of different letters, different difficulty degree grading values B are respectively given to the categories of different letters, different difficulty degree grading values C are respectively given to the average length and the long sentence occupation ratio of different letters, the grading weighting weight of the first characteristic information is defined as th1, the grading weighting weight of the second characteristic information is defined as th2, and the grading weighting weight of the third characteristic information is defined as th3; wherein, th2> th1> th3.
In step S2, the working process of calculating the processing difficulty degree score V of each letter and determining the priority of each letter according to the calculation result includes:
s2-1: and calculating a processing difficulty degree grading value V of each letter according to the formula:
V＝A*th1+B*th2+C*th3
s2-2: according to the processing difficulty degree grading value V of each letter, the priority processing grade arrangement of each letter is carried out, the letters are divided into three grades from high to low, and when the processing difficulty degree grading value V of the letter is more than G, the corresponding letter is divided into a first priority processing grade; when the processing difficulty degree grading value F of the letters is less than V and less than G, dividing the corresponding letters into second priority processing levels; when the processing difficulty degree grading value V of the letters is less than or equal to F, dividing the corresponding letters into a third priority processing level; where G and F are adjustable thresholds.
In step S3, the integrated command and dispatch system composes letters investigation team headcount Mi with different priority treatment levels by dispatching personnel with different numbers and different professional levels according to the formula:
Mi＝pi*g1+hi*g2+ki*g3
wherein i is the number of different priority treatment levels, g1 is the total number of advanced professionals, g2 is the total number of intermediate professionals, g3 is the total number of low professionals, pi represents the corresponding duty ratio weight of g1 under the ith priority treatment level, hi represents the corresponding duty ratio weight of g2 under the ith priority treatment level, ki represents the corresponding duty ratio weight of g3 under the ith priority treatment level, if a letter is the first priority treatment level, p1 x g1> h1 x g2> k1 x g3; if the letter is the second priority, h2 g2> p2 g1> k2 g3; if the letter is of the third priority, then k3 g3> h3 g2> p3 g1; finally, matching the letters with different priority levels with the letter investigation team persons Mi with different priority levels.
The following specific data are assumed as examples:
(1) Setting total number of advanced professionals in department: g1 =80, total number of middle professionals: g2 Total number of low-level professionals =150: g3 =300;
(2) Setting the duty ratio weights of professionals at different levels under the mail events with different priority levels:
the duty cycle weight of high, medium and low professionals under the first priority letter event: p1=0.6, h1=0.3, k1=0.1; the duty cycle weight of the high, medium and low professionals under the second priority letter event: p2=0.4, h2=0.5, k2=0.1; the third priority deals with the duty cycle weight of the high, medium and low professionals under the letter event: p3=0.1, h3=0.2, k3=0.7
(3) The number of persons of the investigation team of different priority classes is now calculated from the data set forth above:
M1＝p1*g1+h1*g2+k1*g3
＝0.6*80+0.3*150+0.1*300
＝48+45+30
=123 (the first priority class survey team M1 has 123 persons, 48 high-class professionals, 45 medium-class professionals, and 30 low-class professionals)
M2＝p2*g1+h2*g2+k2*g3
＝0.4*80+0.5*150+0.1*300
＝32+75+30
=167 (the second priority class survey team M2 has 167 persons, 32 high-level professionals, 75 medium-level professionals, and 30 low-level professionals)
M3＝p3*g1+h3*g2+k3*g3
＝0.1*80+0.2*150+0.7*300
＝8+30+210
=248 (the third priority class survey team M3 has 248 persons, 8 high-class professionals, 30 medium-class professionals, and 210 low-class professionals)
In the step S4, setting the number of high-grade professionals as G1, setting the number of medium-grade professionals as G2, setting the number of low-grade professionals as G3, and ensuring that the number of professionals distributed to each letter investigation team does not exceed the constraint value of the number of professionals, namely, G1 is less than or equal to G1, G2 is less than or equal to G2, and G3 is less than or equal to G3; setting the corresponding specified processing time of the letter investigation team persons Mi with different priority levels as Ti, setting the actual processing time of the letter investigation team with different priority levels as Ti, when Ti is less than or equal to Ti, the letter investigation team persons Mi with different priority levels do not need to be adjusted, when Ti is more than Ti, the letter investigation team persons Mi with different priority levels readjust personnel composition, and distributing n1 x g1 advanced professionals to the investigation team; wherein n1 is dynamically adjusted according to the emergency degree of each letter processing.
The system comprises a characteristic information extraction and analysis module, a letter priority processing level assessment module, a scheduling strategy matching module and an optimal scheduling scheme confirming module;
the feature information extraction and analysis module extracts feature information of the received letters and sets a difficulty degree grading value and a grading weighting weight for each piece of feature information;
the letter priority processing grade evaluation module calculates the processing difficulty grade value of each letter according to the difficulty grade value and the grade weighting weight set by each characteristic information, and determines the priority processing grade of each letter according to the calculated result;
the scheduling policy matching module matches each letter problem with a preset scheduling policy according to the priority processing level of each letter problem, wherein the scheduling policy comprises schemes of allocating different teams, assigning specific personnel and allocating time resources;
on the premise of meeting the resource constraint and response time requirements, the optimal scheduling scheme confirming module sets constraint values of the number of each professional and the specified processing time of each letter based on the matching of the evaluation results of the priority processing levels of the letters and the scheduling strategies, and dynamically adjusts the personnel of each letter event investigation team.
The feature information extraction analysis module comprises a feature information extraction unit and a scoring weight setting unit, wherein the feature information extraction unit firstly obtains the main body information of each letter, secondly constructs a word library of each type of letter, determines the type of the letter by extracting keywords, and performs sentence segmentation on the content of each letter again to define long short sentences and calculate the long sentence occupation ratio; the scoring weight setting unit sets a difficulty degree scoring value and a scoring weight for each piece of feature information.
The letter priority processing grade evaluation module comprises a difficulty grade calculation unit and a priority processing grade determination unit, wherein the difficulty grade calculation unit calculates the processing difficulty grade value of each letter through the difficulty grade value and the grading weighting weight of each piece of characteristic information; the priority processing level determining unit performs a custom threshold according to the calculated result to determine the priority processing level of each letter.
The scheduling policy matching module comprises a team allocation unit, a personnel assignment unit and a priority processing level matching unit, wherein the team allocation unit allocates the questions to teams with corresponding priority processing levels for processing according to the priority processing levels of the letter questions; the personnel assignment unit assigns different grades and different numbers of experts to deal with letter questions of different priority treatment grades; the priority processing level matching unit matches the priority processing level of each letter problem with a preset scheduling strategy, and determines the processing sequence and the priority processing level of the problem.
The optimal scheduling scheme confirming module comprises a resource constraint setting unit, a processing time specifying unit and a personnel adjusting unit; the resource constraint setting unit sets constraint values of the number of each professional, so that reasonable allocation and utilization of resources are ensured; the processing time prescribing unit sets prescribed processing time for each letter, so as to ensure that the problem is processed in time; the personnel adjusting unit dynamically adjusts personnel of each letter investigation team based on the evaluation result of each letter problem and the matching of the scheduling strategy, and ensures reasonable configuration of professionals.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The comprehensive command scheduling method based on big data is characterized by comprising the following steps:
step S1: extracting characteristic information of each received letter content, and setting a difficulty degree grading value and a grading weighting weight for each characteristic information;
step S2: calculating the processing difficulty degree grading value of each letter, and evaluating the priority processing level of each letter according to the calculated result;
step S3: matching each letter with a corresponding scheduling strategy according to the priority processing level of each letter;
step S4: based on the results of priority evaluation and scheduling policy matching of the letters, personnel of the letter event investigation team are dynamically adjusted on the basis of meeting the resource constraint and response time requirements.
2. The comprehensive command and dispatch method based on big data according to claim 1, wherein the method comprises the following steps: in step S1, the working process of extracting the feature information of each letter and scoring the difficulty level includes:
s1-1: extracting the main information of the sender and the main information of the receiver in each letter content; wherein the subject comprises a unit or individual; setting the main body information of each letter as first characteristic information of each letter;
s1-2: constructing word libraries of various letters; wherein the letter categories include: complaint letters, help letters and advice letters; extracting a letter content part of each letter, extracting keywords from the letter content part, and generating a keyword set corresponding to each letter; matching the keyword set with word libraries of various letters respectively, and determining the corresponding category of each letter according to the keyword set with the highest matching degree and the word libraries of the various letters; setting the category of each letter as second characteristic information of each letter;
s1-3: dividing sentences in each letter content to obtain a sentence list, calculating the length of each sentence in the sentence list according to the number of characters, summing the lengths of each sentence to obtain the total length of the sentence, and obtaining the average length of the sentences according to the total length of the sentence and the number of the sentences; secondly, scanning each sentence in each letter content, defining long sentences when the sentences in the letter content exceed the average length, and defining short sentences when the sentences in the letter content are shorter than the average length; thirdly, calculating the ratio of the number of long sentences in each letter to the total number of sentences; setting the product value of the sentence average length and the long sentence duty ratio of each letter as third characteristic information of each letter;
s1-4: different difficulty degree grading values A are respectively given to the main body types of different letters, different difficulty degree grading values B are respectively given to the categories of different letters, different difficulty degree grading values C are respectively given to the product values of the average lengths and the long sentence occupation ratios of different letters, the grading weighting weight of the first characteristic information is defined as th1, the grading weighting weight of the second characteristic information is defined as th2, and the grading weighting weight of the third characteristic information is defined as th3; wherein, th2> th1> th3.
3. The comprehensive command and dispatch method based on big data according to claim 2, wherein the method is characterized in that: in step S2, the working process of calculating the processing difficulty degree score V of each letter and determining the priority of each letter according to the calculation result includes:
s2-1: and calculating a processing difficulty degree grading value V of each letter according to the formula:
V＝A*th1+B*th2+C*th3
s2-2: according to the processing difficulty degree grading value V of each letter, the priority processing grade arrangement of each letter is carried out, the letters are divided into three grades from high to low, and when the processing difficulty degree grading value V of the letter is more than G, the corresponding letter is divided into a first priority processing grade; when the processing difficulty degree grading value F of the letters is less than V and less than G, dividing the corresponding letters into second priority processing levels; when the processing difficulty degree grading value V of the letters is less than or equal to F, dividing the corresponding letters into a third priority processing level; where G and F are adjustable thresholds.
4. The integrated command and dispatch method based on big data according to claim 3, wherein the method is characterized in that: in step S3, the integrated command and dispatch system composes letters investigation team headcount Mi with different priority treatment levels by dispatching personnel with different numbers and different professional levels according to the formula:
Mi＝pi*g1+hi*g2+ki*g3
wherein i is the number of different priority treatment levels, g1 is the total number of advanced professionals, g2 is the total number of intermediate professionals, g3 is the total number of low professionals, pi represents the corresponding duty ratio weight of g1 under the ith priority treatment level, hi represents the corresponding duty ratio weight of g2 under the ith priority treatment level, ki represents the corresponding duty ratio weight of g3 under the ith priority treatment level, if a letter is the first priority treatment level, p1 x g1> h1 x g2> k1 x g3; if the letter is the second priority, h2 g2> p2 g1> k2 g3; if the letter is of the third priority, then k3 g3> h3 g2> p3 g1; finally, matching the letters with different priority levels with the letter investigation team persons Mi with different priority levels.
5. The comprehensive command and dispatch method based on big data according to claim 4, wherein the method comprises the following steps: in the step S4, setting the number of high-grade professionals as G1, setting the number of medium-grade professionals as G2, setting the number of low-grade professionals as G3, and ensuring that the number of professionals distributed to each letter investigation team does not exceed the constraint value of the number of professionals, namely, G1 is less than or equal to G1, G2 is less than or equal to G2, and G3 is less than or equal to G3; setting the corresponding specified processing time of the letter investigation team persons Mi with different priority levels as Ti, setting the actual processing time of the letter investigation team with different priority levels as Ti, when Ti is less than or equal to Ti, the letter investigation team persons Mi with different priority levels do not need to be adjusted, when Ti is more than Ti, the letter investigation team persons Mi with different priority levels readjust personnel composition, and distributing n1 x g1 advanced professionals to the investigation team; wherein n1 is dynamically adjusted according to the emergency degree of each letter processing.
6. A system applied to the comprehensive command scheduling method based on big data as in any one of claims 1-5, wherein the system comprises a characteristic information extraction and analysis module, a letter priority rating module, a scheduling policy matching module and an optimal scheduling scheme confirming module;
the characteristic information extraction and analysis module extracts characteristic information of the received letters and sets a difficulty degree grading value and a grading weighting weight for each characteristic information;
the letter priority processing grade evaluation module calculates the processing difficulty grade value of each letter according to the difficulty grade value and the grading weighting weight set by each characteristic information, and determines the priority processing grade of each letter according to the calculated result;
the scheduling policy matching module matches each letter problem with a preset scheduling policy according to the priority of each letter problem, and the scheduling policy comprises schemes of allocating different teams, assigning specific personnel and allocating time resources;
the optimal scheduling scheme confirming module sets constraint values of the number of each professional and the specified processing time of each letter on the basis of the evaluation results of the priority processing levels of the letters and the matching of scheduling strategies on the premise of meeting the requirements of resource constraint and response time, and dynamically adjusts the personnel of each letter event investigation team.
7. The system according to claim 6, wherein: the feature information extraction analysis module comprises a feature information extraction unit and a scoring weight setting unit, wherein the feature information extraction unit firstly obtains the main body information of each letter, secondly constructs a word library of each type of letter, determines the type of the letter by extracting keywords, and performs sentence segmentation on the content of each letter again, defines long short sentences and calculates the occupation ratio of the long sentences; the scoring weight setting unit sets a difficulty degree scoring value and a scoring weight for each piece of feature information.
8. The system according to claim 6, wherein: the letter priority processing level evaluation module comprises a difficulty degree scoring calculation unit and a priority processing level determination unit, wherein the difficulty degree scoring calculation unit calculates the processing difficulty degree scoring value of each letter through the difficulty degree scoring value and the scoring weighting weight of each piece of characteristic information; and the priority processing level determining unit is used for carrying out self-defined threshold according to the calculated result and determining the priority processing level of each letter.
9. The system according to claim 6, wherein: the scheduling policy matching module comprises a team allocation unit, a personnel assignment unit and a priority processing level matching unit, wherein the team allocation unit allocates the questions to teams with corresponding priority processing levels for processing according to the priority processing levels of the letters; the personnel assignment unit assigns different levels and different numbers of experts to deal with letter questions of different priority treatment levels; the priority processing level matching unit matches the priority processing level of each letter problem with a preset scheduling strategy, and determines the processing sequence and the priority processing level of the problem.
10. The system according to claim 6, wherein: the optimal scheduling scheme confirming module comprises a resource constraint setting unit, a processing time specifying unit and a personnel adjusting unit; the resource constraint setting unit sets constraint values of the number of each professional, so that reasonable allocation and utilization of resources are ensured; the processing time prescribing unit sets prescribing processing time for each letter to ensure that the problem is processed in time; the personnel adjusting unit dynamically adjusts personnel of each letter investigation team based on the evaluation result of each letter problem and the matching of the scheduling strategy, and ensures reasonable configuration of professionals.
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