CN113919710A - Regression prediction analysis-based order dispatching method and system - Google Patents
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Abstract
The invention discloses a dispatching method and a dispatching system based on regression prediction analysis, which are used for acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the generated power failure information; acquiring n pieces of first data information, calculating the ith first predicted value by adopting a regression prediction method on the work order information and the ith first data information, and repeating the above operations on the work order information and the n pieces of first data information in sequence to acquire n first predicted values; comparing the sizes of the n first predicted values to obtain a maximum predicted value; and distributing the work order information to maintenance personnel corresponding to the maximum predicted value. The invention has the advantages that the condition that the number of the to-be-processed work order information of maintenance personnel is increased is reduced, the time of a client for waiting for maintaining the work order information is reduced, and the efficiency of maintaining fault information is increased; the method and the system realize the processing of the work order information of the client in the shortest time and ensure the smoothness degree of the order receiving of maintenance personnel.
Description
Technical Field
The invention relates to the field of dispatching orders in a power system, in particular to a dispatching order method and a dispatching order system based on regression prediction analysis.
Background
As power customers increase and the service life of the customer equipment increases but the maintenance is not necessary, the power failures of own property rights of the customers increase, and in addition, the home decoration electricity-related requirements of the customers increase, and the rapid development of an Internet shared maintenance personnel platform is promoted. However, the platform usually considers the aspects of friendliness, safety responsibility, maintenance quality management and the like of a customer repair interface in functional design, the current mainstream internet shared maintenance personnel platform is simple in order dispatching mode, a passive waiting type order dispatching mode of hanging order and grabbing order is usually adopted, or an automatic order dispatching mode considering a single distance factor is adopted, evaluation of the customer after maintenance is not considered, the platform is different from internet platforms such as a network appointment vehicle and a take-away express platform, service instantaneity is relatively low due to the working property of the maintenance personnel, but technical and quality requirements are relatively high, so that if the maintenance personnel platform directly dispatches orders without analyzing any order, the order accumulation of the maintenance personnel cannot be processed, and the waiting and maintenance time of the customer is increased.
In view of this, the present application is specifically made.
Disclosure of Invention
The invention aims to solve the technical problems that maintenance personnel orders are increased due to the fact that a conventional order dispatching method is adopted, and customer waiting maintenance time is increased.
The invention is realized by the following technical scheme:
a dispatching method based on regression prediction analysis comprises the following specific steps:
s1: acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
s2: acquiring n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, and repeating the above operations on the work order information and the n pieces of first data information in sequence to obtain n first predicted values, wherein the ith first data information corresponds to maintenance characteristic information of a maintenance worker;
s3: comparing the sizes of the n first predicted values to obtain a maximum predicted value;
s4: and distributing the work order information to maintenance personnel corresponding to the maximum predicted value.
The invention provides an order dispatching method based on regression prediction analysis, which is used for dispatching corresponding work order information after analyzing and calculating the current maintenance characteristic information of a maintenance worker, so that the condition that the work order information to be processed of the maintenance worker is increased is reduced, and the time for waiting for maintenance of a customer is reduced.
Preferably, the maintenance characteristic information includes geographical location information of maintenance personnel, historical work order information of maintenance personnel, and current state information of maintenance personnel;
the historical work order information of the maintenance personnel comprises skill level proficiency degree D, a first satisfaction degree, a second satisfaction degree Fy, a first time and a second time Gy, wherein the first satisfaction degree is an evaluation value of a historical customer for power failure maintenance of the maintenance personnel, the second satisfaction degree Fy is a customer evaluation value predicted after the maintenance personnel processes the work order information, the first time is the time required for the maintenance personnel to reach historical maintenance position processing failure information, and the second time Gy is the time predicted by the first geographical position information processing failure information;
the current state comprises the current work order to be processed and third time, and the third time is the residual predicted maintenance time Gywait of the current work order to be processed and the required road time Gr in the current work order to be processed.
Preferably, the specific expression of the first predicted value is as follows:
T＝Wf*D*Fy/Fg-Wg*(Gy/D+Gywait/Dz+Gr)/Gg
t is a first predicted value, Wf is a weight coefficient of the first satisfaction degree, Fg is a normalization coefficient of the first satisfaction degree, Wg is a weight coefficient of the first time, Gg is a normalization coefficient of the first time, and Dz is a comprehensive stability coefficient of all repair type work orders of the maintenance personnel.
Preferably, the method for dispatching further comprises: and when the maintenance personnel finish processing the work order information, using the obtained satisfaction degree of the work order information and the maintenance time for optimizing a weight coefficient of the first satisfaction degree in the first predicted value and a weight system of the first maintenance time.
Preferably, the specific expression of the skill level proficiency D of the service person is as follows:
D＝1-e-aN，N≥0
a is the growth factor of the skill level stability proficiency of the maintenance personnel.
Preferably, the time-in-transit is specifically expressed as
Gr＝L/v
L is the sum of the distance of the optimal path which needs to be traveled by the maintenance personnel from the current position to the work order position after sequentially passing through each work order position to be processed and finally reaching the work order position, and v is the average traveling speed of the maintenance personnel.
Preferably, the specific expression of the second satisfaction degree Fy is
Fy＝f(N+1)
df＝Σ||F(n)-f(n)||2，n＝0，1，……N
f (n) is the regression analysis function of the first satisfaction degree, and F (n) is the actual satisfaction degree of the nth processing work order.
Preferably, the specific expression of the second time Gy is
Gy＝G(N+1)
dg＝Σ||G(n)-g(n)||2，n＝0，1，……N
g (n) is a regression function of the first time, and G (n) is the actual first time of the nth processing of the work order.
Preferably, the specific expression of the remaining predicted maintenance time of the current work order to be processed is
Gywait＝Σgyi
Gyi is the predicted repair time for the ith work order to be processed, the Gyi algorithm is the same as the second time algorithm.
The invention also uses a dispatching system based on regression prediction analysis, which comprises
The information acquisition module is used for acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
the analysis module is used for obtaining n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, wherein the ith piece of first data information corresponds to maintenance characteristic information of one maintenance worker, and processing the work order information and the n pieces of first data information in sequence to obtain n first predicted values;
the comparison module is used for comparing the sizes of the n first predicted values to obtain a maximum predicted value;
the order dispatching module is used for dispatching the work order information to the maintenance personnel corresponding to the maximum predicted value
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the invention, the order dispatching method and the order dispatching system based on regression prediction analysis are based on the work order information and the current maintenance characteristic information of maintenance personnel to perform analysis processing, so that the condition that the work order information to be processed of the maintenance personnel is increased is reduced, meanwhile, the time for a customer to wait for the maintenance work order information is reduced, and the efficiency of fault information maintenance is increased;
2. the invention embodiment provides an order dispatching method and an order dispatching system based on regression prediction analysis, which can process the work order information of a client in the shortest time and ensure the smoothness of order receiving of maintenance personnel.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic diagram of a dispatching method
FIG. 2 is a schematic diagram of a dispatch system
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that: it is not necessary to employ these specific details to practice the present invention. In other instances, well-known structures, circuits, materials, or methods have not been described in detail so as not to obscure the present invention.
Throughout the specification, reference to "one embodiment," "an embodiment," "one example," or "an example" means: the particular features, structures, or characteristics described in connection with the embodiment or example are included in at least one embodiment of the invention. Thus, the appearances of the phrases "one embodiment," "an embodiment," "one example" or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combination and/or sub-combination in one or more embodiments or examples. Further, those of ordinary skill in the art will appreciate that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the description of the present invention, the terms "front", "rear", "left", "right", "upper", "lower", "vertical", "horizontal", "upper", "lower", "inner", "outer", etc. indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the scope of the present invention.
Example one
The embodiment discloses an order dispatching method based on regression prediction analysis, as shown in fig. 1, the specific method steps of the order dispatching method include:
s1: acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
in this embodiment, the obtained work order information is the fault information of warranty repair at the client, and includes the type of the fault information of repair report and the actual geographic location information sent by the work order information, and the actual geographic location may be matched from the beginning; the maintenance personnel closest to this distance can enable the customer to reduce the time waiting for maintenance.
S2: acquiring n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, and repeating the above operations on the work order information and the n pieces of first data information in sequence to obtain n first predicted values, wherein the ith first data information corresponds to maintenance characteristic information of a maintenance worker;
the maintenance characteristic information comprises geographical position information of maintenance personnel, historical work order information of the maintenance personnel and current state information of the maintenance personnel;
the historical work order information of the maintenance personnel comprises skill level proficiency degree D, the more work orders historically processed by the maintenance personnel, the more stable and more skilled the skill level can be exerted, and therefore the historical work order number of the maintenance personnel can represent the skill level stability and proficiency degree of the maintenance personnel; however, generally, the stability and proficiency of the skill level of the maintenance personnel show a natural law that the skill level is increased rapidly and then increased slowly with the increase of the number of historical workers handled by the maintenance personnel, and the natural growth law is expressed by adopting an exponential function in the embodiment. Thus, the specific expression for the serviceman skill level proficiency D is: d-1-e-aNN is more than or equal to 0, and a is an increasing coefficient of the skill level stability and proficiency of the maintenance personnel. D is a value interval of 0 to 1.
The first satisfaction degree is an evaluation value of the historical customer for performing power failure maintenance on the maintenance personnel, namely the evaluation value of the completion condition of the repair service performed on the maintenance personnel by the customer, which is recorded by the system.
The second satisfaction degree Fy is the customer evaluation value predicted after the maintenance personnel process the work order information,
considering that the skills of maintenance personnel are continuously growing, the current maintenance quality and the current work efficiency are influenced by the customer experience, historical factors which can introduce a period when the previous skill level of the maintenance personnel is weak are evaluated in a historical satisfaction averaging mode, and the current skill level of the maintenance personnel is not favorably embodied, so the prediction satisfaction of the maintenance personnel in processing the current maintenance service is predicted through the historical satisfaction by adopting a regression prediction method. The specific expression of the second satisfaction degree Fy is
Fy＝f(N+1)
df＝Σ||F(n)-f(n)||2，n＝0，1，……N
f (n) is the regression analysis function of the first satisfaction degree, and F (n) is the actual satisfaction degree of the nth processing work order.
The first time is the time required by the maintenance personnel to reach the historical maintenance position to process the fault information, namely the first time is the time from the time when the maintenance personnel arrives at the site where the fault information is generated and needs to be processed to the time when the whole time for confirming the fault information of the maintenance processing is finished.
A second time Gy that is a time predicted by the arrival of the first geographical location information processing fault information;
considering that the skills of the maintenance personnel are continuously growing, what influences the customer experience is the current maintenance quality and the current work efficiency, the historical factors which can introduce the period of weak skill level of the maintenance personnel in the past are evaluated in the mode of averaging the historical maintenance time, and the current skill level of the maintenance personnel is not favorably embodied, so the predicted maintenance time for processing the current maintenance service is predicted through the historical maintenance time by adopting a regression prediction method in the embodiment, and the specific expression of the second time Gy is that
Gy＝G(N+1)
dg＝Σ||G(n)-g(n)||2，n＝0，1，……N
g (n) is a regression function of the first time, and G (n) is the actual first time of the nth processing of the work order.
The current state comprises the current work order to be processed and third time, wherein the third time is the residual predicted maintenance time Gywait of the current work order to be processed and the required road time Gr in the current work order to be processed. The specific expression of the time on the way is
Gr＝L/v
L is the sum of the distance of the optimal path which needs to be traveled by the maintenance personnel from the current position to the work order position after sequentially passing through each work order position to be processed and finally reaching the work order position, and v is the average traveling speed of the maintenance personnel.
The specific expression of the remaining predicted repair time of the current work order to be processed is Gywait ═ Σ gyi,
gyi is the predicted repair time for the ith work order to be processed, the Gyi algorithm is the same as the second time algorithm.
According to the foregoing order principle, it can be known that: the larger D is the better, the larger Fy is the better, the smaller Gy is the better, GywaitThe smaller Gr, the better.
Because D is a measure of skill stability and proficiency, is an index reflecting the stability of maintenance quality and work efficiency, and needs to be represented depending on the predicted satisfaction and the predicted time, under the condition of equal maintenance quality, work efficiency and arrival time, a customer is more inclined to select an electrician with better stability and proficiency, D is used as a stability coefficient of the predicted satisfaction and the predicted time, because the predicted satisfaction is a forward index of customer experience, the D is multiplied by an index in the category of the satisfaction, and because the predicted time is a reverse index of the customer experience, the D is divided by an index in the category of the time. The specific expression of the first predicted value is as follows:
T＝Wf*D*Fy/Fg-Wg*(Gy/D+Gywait/Dz+Gr)/Gg
t is a first predicted value, Wf is a weight coefficient of first satisfaction, Fg is a normalization coefficient of the first satisfaction, Wg is a weight coefficient of first time, Gg is a normalization coefficient of the first time, Dz is a comprehensive stability coefficient of all repair type work orders of the maintenance personnel, the weight coefficients reflect different proportions of influences of maintenance quality and time on customer experience, and the weight coefficients are determined according to actual conditions through a large number of experiments and surveys; normally, Fg takes the maximum satisfaction, and Gg takes the historical maximum repair time of the repair work order.
For the same work order information, the calculation and matching with the maintenance characteristic information of a plurality of maintenance personnel are needed, so the same calculation mode is adopted, but after the calculation and matching are carried out on different maintenance personnel, the obtained first predicted value is different, and the matching with the predicted value of each maintenance personnel is needed.
S3: comparing the sizes of the n first predicted values to obtain a maximum predicted value;
and selecting the largest predicted value, and selecting a maintenance worker which can process the best work order information under the work order information in consideration of time and geographic position, so that the work order distribution of the maintenance worker is optimized to a certain extent, and the time for a client to wait for maintenance is reduced.
S4: and distributing the work order information to maintenance personnel corresponding to the maximum predicted value.
The order dispatching method further comprises the following steps: after the maintenance personnel process the work order information, the obtained satisfaction degree of the work order information and the maintenance time are used for optimizing a weight coefficient of the first satisfaction degree in the first predicted value and a weight system of the first maintenance time, the calculation formula obtained after optimization can coherently update the maintenance characteristic information of the maintenance personnel, and the quality and the efficiency of the maintenance personnel processing work are updated and matched in real time.
Example two
The embodiment discloses an order dispatching system based on regression prediction analysis, which is used for implementing an order dispatching method based on regression prediction analysis as an embodiment, as shown in fig. 2, including
The information acquisition module is used for acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
the analysis module is used for obtaining n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, wherein the ith piece of first data information corresponds to maintenance characteristic information of one maintenance worker, and processing the work order information and the n pieces of first data information in sequence to obtain n first predicted values;
the comparison module is used for comparing the sizes of the n first predicted values to obtain a maximum predicted value;
and the order dispatching module is used for dispatching the work order information to the maintenance personnel corresponding to the maximum predicted value.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A dispatching method based on regression prediction analysis is characterized in that the dispatching method comprises the following specific steps:
s1: acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
s2: acquiring n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, and repeating the above operations on the work order information and the n pieces of first data information in sequence to obtain n first predicted values, wherein the ith first data information corresponds to maintenance characteristic information of a maintenance worker;
s3: comparing the sizes of the n first predicted values to obtain a maximum predicted value;
s4: and distributing the work order information to maintenance personnel corresponding to the maximum predicted value.
2. The method of claim 1, wherein the service characteristic information comprises geographic location information of a service person, historical work order information of the service person, and current status information of the service person;
the historical work order information of the maintenance personnel comprises skill level proficiency degree D, a first satisfaction degree, a second satisfaction degree Fy, a first time and a second time Gy, wherein the first satisfaction degree is an evaluation value of a historical customer for power failure maintenance of the maintenance personnel, the second satisfaction degree Fy is a customer evaluation value predicted after the maintenance personnel processes the work order information, the first time is the time required for the maintenance personnel to reach historical maintenance position processing failure information, and the second time Gy is the time predicted by the first geographical position information processing failure information;
the current state comprises the current work order to be processed and third time, and the third time is the residual predicted maintenance time Gywait of the current work order to be processed and the required road time Gr in the current work order to be processed.
3. The method of claim 1, wherein the first predictor is expressed by the following specific expression:
T＝Wf*D*Fy/Fg-Wg*(Gy/D+Gywait/Dz+Gr)/Gg
t is a first predicted value, Wf is a weight coefficient of the first satisfaction degree, Fg is a normalization coefficient of the first satisfaction degree, Wg is a weight coefficient of the first time, Gg is a normalization coefficient of the first time, and Dz is a comprehensive stability coefficient of all repair type work orders of the maintenance personnel.
4. The method of claim 1, wherein the method further comprises: and when the maintenance personnel finish processing the work order information, using the obtained satisfaction degree of the work order information and the maintenance time for optimizing a weight coefficient of the first satisfaction degree in the first predicted value and a weight system of the first maintenance time.
5. The method of claim 2 or 3, wherein the skill level D of the serviceman is expressed by:
D＝1-e-aN，N≥0
a is the growth factor of the skill level stability proficiency of the maintenance personnel.
6. The method according to claim 2 or 3, wherein the time-in-transit is specifically expressed as
Gr＝L/v
L is the sum of the distance of the optimal path which needs to be traveled by the maintenance personnel from the current position to the work order position after sequentially passing through each work order position to be processed and finally reaching the work order position, and v is the average traveling speed of the maintenance personnel.
7. The method according to claim 2 or 3, wherein the second satisfaction degree Fy is expressed as
Fy＝f(N+1)
df＝Σ||F(n)-f(n)||2，n＝0，1，……N
f (n) is the regression analysis function of the first satisfaction degree, and F (n) is the actual satisfaction degree of the nth processing work order.
8. The method according to claim 2 or 3, wherein the second time Gy is expressed by
Gy＝G(N+1)
dg＝Σ||G(n)-g(n)||2，n＝0，1，……N
g (n) is a regression function of the first time, and G (n) is the actual first time of the nth processing of the work order.
9. The method as claimed in claim 2 or 3, wherein the specific expression of the remaining predicted repair time of the current work order to be processed is
Gywait＝Σgyi
Gyi is the predicted repair time for the ith work order to be processed, the Gyi algorithm is the same as the second time algorithm.
10. A dispatch system based on regression prediction analysis is characterized by comprising
The information acquisition module is used for acquiring work order information, wherein the work order information comprises power failure information and first geographical position information of the power failure information;
the analysis module is used for obtaining n pieces of first data information, calculating the work order information and the ith piece of first data information by adopting a regression prediction method to obtain an ith first predicted value, wherein the ith piece of first data information corresponds to maintenance characteristic information of one maintenance worker, and processing the work order information and the n pieces of first data information in sequence to obtain n first predicted values;
the comparison module is used for comparing the sizes of the n first predicted values to obtain a maximum predicted value;
and the order dispatching module is used for dispatching the work order information to the maintenance personnel corresponding to the maximum predicted value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116485159A (en) * | 2023-06-21 | 2023-07-25 | 国网信通亿力科技有限责任公司 | Work order centralized management method of power supply system |
| CN116882980A (en) * | 2023-09-06 | 2023-10-13 | 神州顶联科技有限公司 | Repair resource allocation method and system based on user repair behavior |
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| CN116882980B (en) * | 2023-09-06 | 2023-11-21 | 神州顶联科技有限公司 | Repair resource allocation method and system based on user repair behavior |
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