CN111695820B - Engineering vehicle electronic coupon management method and device, terminal and storage medium - Google Patents
Engineering vehicle electronic coupon management method and device, terminal and storage medium Download PDFInfo
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Abstract
The invention discloses an engineering vehicle electronic coupon management method, device, terminal and storage medium, wherein the method comprises the following steps: acquiring GPS data of the engineering vehicle in real time; when the engineering vehicle is determined to move to the preset area based on the GPS data, vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area are obtained; scoring by using a pre-trained scoring model, vehicle data and engineering data, and comparing a scoring result with a pre-trained scoring threshold value; and when the scoring result is greater than or equal to the scoring threshold value, generating or finishing the electronic coupons. Through the mode, the electronic bill management method and the electronic bill management system can optimize the electronic bill management of the engineering vehicle and avoid the frequent occurrence of the situation that more electronic bills are generated or the electronic bills are not generated.
Description
Technical Field
The application relates to the technical field of engineering construction supervision, in particular to an engineering vehicle electronic coupon management method, device, terminal and storage medium.
Background
In the engineering construction process, often need engineering vehicle transportation building material or discarded object to go to the building site or accomodate the place, in order to strengthen the management to engineering vehicle transportation process and building material or discarded object transportation condition, at present, it is comparatively general to adopt the electron to ally oneself with the list and carry out whole journey management and control to the transportation, utilizes the electron to ally oneself with the list and realizes the whole process real-time management of two points and one line between the site source (emission) to terminal processing (accomodate the place). In the process of managing by using the electronic coupon, firstly, each construction site and each legal storage place are divided into electronic fences on a map according to the position coordinates of the construction site and GPS data information of an engineering vehicle is involved in an intelligent supervision system, when the engineering vehicle is monitored to drive into the corresponding electronic fence area of the construction site and stay for a certain time, the electronic coupon is generated, after the confirmation of workers, the engineering vehicle is allowed to drive out, and when the engineering vehicle is monitored to drive into the corresponding electronic fence area of the storage place, after the confirmation of the workers, the electronic coupon is finished, so that the transportation condition of the engineering vehicle is recorded.
At present, the mode of determining whether an engineering vehicle enters a construction site or a storage place by utilizing GPS positioning still has defects, which mainly comprise the following two points: firstly, the GPS positioning is not accurate, the GPS module of the vehicle-mounted intelligent terminal device has the problem of positioning deviation, the actual position of a vehicle and the GPS positioning position may have large deviation, and the corresponding part of a construction site correspondingly establishes an area which can be reached by most parts of an electronic fence area under the condition of an unconventional road, so that the accurate positioning can not be carried out through the existing map software, and the final positioning result also has the phenomenon of 'drifting'; secondly, the electronic fence is not drawn accurately, and due to different forms of actual construction sites, when workers draw an electronic fence area on map software, the situation that the drawn electronic fence and the actual site enclosure situation are different is likely to occur. The problems of inaccurate GPS positioning and inaccurate electronic fence drawing frequently result in the situation that the system generates more electronic coupons or generates electronic coupons without omission, and great inconvenience is brought to management work.
Disclosure of Invention
The application provides a method, a device, a terminal and a storage medium for managing an electronic coupon of an engineering vehicle, which aim to solve the problem that the electronic coupon is frequently generated or is not generated in the existing engineering management process.
In order to solve the technical problem, the application adopts a technical scheme that: the engineering vehicle electronic coupon management method comprises the following steps: acquiring GPS data of the engineering vehicle in real time; when the engineering vehicle is determined to move to the preset area based on the GPS data, vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area are obtained; scoring by using a pre-trained scoring model, vehicle data and engineering data, and comparing a scoring result with a pre-trained scoring threshold value, wherein the scoring model and the scoring threshold value are obtained by using historical vehicle data and historical engineering data for training; and when the scoring result is greater than or equal to the scoring threshold, generating or finishing the electronic bill.
As a further improvement of the method, the method comprises the following steps of scoring by using a pre-trained scoring model, vehicle data and engineering data, and comparing a scoring result with a pre-trained scoring threshold value, wherein the method comprises the following steps: confirming a service scene corresponding to a preset area, and acquiring a target scoring threshold and a target scoring model corresponding to the service scene, wherein each service scene corresponds to one group of scoring threshold and scoring model; inputting the vehicle data and the engineering data into a target scoring model to obtain a target scoring result; and comparing the target scoring result with the target scoring threshold value.
As a further improvement of the invention, the method also comprises the step of respectively training a scoring model based on different business scenes, which comprises the following steps: confirming corresponding business rules based on the business scene, wherein each business rule comprises at least one influence factor; acquiring historical vehicle data and historical engineering data corresponding to the influence factors; inputting historical vehicle data and historical engineering data corresponding to the influence factors into a logistic regression model for training to obtain a weight item and an optimal scoring value corresponding to each influence factor; and constructing a grading model based on the influence factors, the weight items and the optimal grading value.
As a further improvement of the invention, the scoring model is as follows:
wherein m is the number of the business rules, n is the number of the influencing factors corresponding to each business rule, k ij Is the weight term, x, corresponding to each influence factor ij Is the optimal value of credit for each influencing factor.
As a further improvement of the method, after the scoring model is constructed based on the influence factors, the weight terms and the optimal scoring values, the method further comprises the following steps: and acquiring historical vehicle data and historical engineering data in the past preset time period at every interval of the preset time period, and training and updating the scoring model based on the historical vehicle data and the historical engineering data in the past preset time period.
As a further improvement of the invention, the method also comprises a training score threshold value, and the training score threshold value comprises the following steps: inputting a plurality of groups of pre-prepared training sample data into the logistic regression model to train the logistic regression model; inputting a plurality of groups of test sample data prepared in advance into a trained logistic regression model to obtain a plurality of groups of prediction results; calculating the accuracy rate, the real rate and the false positive rate by using the actual results corresponding to the multiple groups of test sample data and the multiple groups of prediction results; and constructing an ROC curve based on the accuracy, the real rate and the false positive rate, and confirming the optimal scoring threshold value by using the ROC curve.
As a further improvement of the present invention, after comparing the scoring result with the pre-trained scoring threshold, the method further includes: and when the scoring result is smaller than the scoring threshold value, the generation or the completion of the electronic coupons is forbidden.
In order to solve the above technical problem, another technical solution adopted by the present application is: provided is an electronic work vehicle coupon management device, including: the first acquisition module is used for acquiring GPS data of the engineering vehicle in real time; the second acquisition module is used for acquiring vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area when the engineering vehicle is determined to move to the preset area based on the GPS data; the scoring module is used for scoring by using a pre-trained scoring model, vehicle data and engineering data, and comparing a scoring result with a pre-trained scoring threshold value, wherein the scoring model and the scoring threshold value are obtained by training historical vehicle data and historical engineering data; and the management module is used for generating or finishing the electronic coupon when the scoring result is greater than or equal to the scoring threshold value.
In order to solve the above technical problem, the present application adopts another technical solution that: the terminal comprises a processor and a memory coupled with the processor, wherein the memory stores program instructions for implementing the engineering vehicle electronic coupon management method; the processor is operable to execute the program instructions stored by the memory to manage an electronic coupon for the work vehicle.
In order to solve the above technical problem, the present application adopts another technical solution that: there is provided a storage medium storing a program file capable of implementing the work vehicle electronic coupon management method according to any one of the above.
The beneficial effect of this application is: the engineering vehicle electronic coupon management method comprises the steps of after an engineering vehicle enters a preset area through GPS positioning, inputting vehicle data of the engineering vehicle and engineering data corresponding to the preset area into a pre-trained scoring model to score, comparing a scoring result with a scoring threshold value obtained through pre-training, and when the scoring result is higher than the scoring threshold value, determining that the engineering vehicle really enters a construction site or a storage site, wherein the scoring model and the scoring threshold value are obtained through historical vehicle data and historical engineering data training, comparing the scoring result obtained by combining the vehicle data and the engineering data through the scoring model with the scoring threshold value to determine the probability of whether the engineering vehicle is in the construction site or the storage site currently, the accuracy of the determination result is obviously higher than that of the GPS positioning determination, influences caused by inaccurate electronic fence drawing are reduced to the greatest extent, and the situation that more electronic coupons are frequently generated or generated is avoided.
Drawings
Fig. 1 is a schematic flow chart of an electronic work vehicle coupon management method according to a first embodiment of the present invention;
fig. 2 is a flowchart illustrating an electronic work vehicle coupon management method according to a second embodiment of the present invention;
FIG. 3 is a functional module schematic diagram of an electronic coupon management device of a construction vehicle according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a storage medium according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second" and "third" in this application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any indication of the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of the feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specified otherwise. In the embodiment of the present application, all the directional indicators (such as the upper, lower, left, right, front, and rear … …) are only used to explain the relative position relationship between the components in a specific posture (as shown in the drawing), the motion situation, and the like, and if the specific posture is changed, the directional indicator is changed accordingly. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a flowchart illustrating a method for managing an electronic work order of a work vehicle according to a first embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 1 if the results are substantially the same. As shown in fig. 1, the method comprises the steps of:
step S101: and acquiring GPS data of the engineering vehicle in real time.
Step S102: and when the engineering vehicle is determined to move to the preset area based on the GPS data, acquiring vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area.
The vehicle data includes: stay time data, GPS data, change state of the box in the area, lifting state, load in and out state, change state of load in the area, ACC state (start-up and shut-down state of the vehicle), speed data, and the like; the engineering data includes: the report state of the project, the emission state of the project, the project electronic fence data, the license plate identification record data and the like. The preset area is an electronic fence, and the pointer is a fence area drawn on the map application for the area where the construction site is located.
In step S102, when the engineering vehicle enters a preset area through GPS data positioning, vehicle data and engineering data are acquired, where the vehicle data may be acquired in real time through a vehicle-mounted terminal preset on the engineering vehicle, part of the engineering data is acquired through a sensor preset on a construction site, and another part of the engineering data is recorded when an engineering project is recorded.
Step S103: and (4) scoring by using the pre-trained scoring model, the vehicle data and the engineering data, and comparing the scoring result with the pre-trained scoring threshold value.
It should be noted that the scoring model and the scoring threshold are obtained by training historical vehicle data and historical engineering data, wherein the scoring threshold is an optimal segmentation point obtained by training to determine whether the engineering vehicle is in a building site or a storage place, and when the score is higher than the scoring threshold, the engineering vehicle is considered to be in the building site or the storage place, and an electronic coupon needs to be generated or completed; and when the score is lower than the score threshold value, the engineering vehicle is not considered to be in the construction site or the storage site, and the electronic coupon does not need to be generated or completed.
Preferably, the historical vehicle data and the historical engineering data refer to historical vehicle data and historical engineering data within a preset time period before the current time, for example, historical vehicle data and historical engineering data within one month of the process, and the result obtained by training is ensured to be closer to the current situation by training with the recent historical data.
Step S104: and when the scoring result is greater than or equal to the scoring threshold value, generating or finishing the electronic coupons.
Further, in this embodiment, when the scoring result is smaller than the scoring threshold, the generation or completion of the electronic coupons is prohibited, so as to avoid generating more electronic coupons.
According to the engineering vehicle electronic coupon management method, after the engineering vehicle enters the preset area through GPS positioning, vehicle data of the engineering vehicle and engineering data corresponding to the preset area are input into a pre-trained scoring model to be scored, the scoring result is compared with the scoring threshold obtained through pre-training, when the scoring result is higher than the scoring threshold, the engineering vehicle is considered to enter a construction site or a storage site, the scoring model and the scoring threshold are obtained through historical vehicle data and historical engineering data training, the scoring result obtained by combining the vehicle data and the engineering data through the scoring model is compared with the scoring threshold to confirm the probability of whether the engineering vehicle is in the construction site or the storage site currently, the accuracy of the judgment result is obviously higher than that when the engineering vehicle is judged only through GPS positioning, the influence caused by inaccurate drawing of an electronic fence is greatly reduced, and the situation that more electronic coupons are frequently generated or generated is avoided.
Fig. 2 is a flowchart illustrating an electronic work vehicle coupon management method according to a second embodiment of the present invention. It should be noted that the method of the present invention is not limited to the flow sequence shown in fig. 2 if the results are substantially the same. As shown in fig. 2, the method comprises the steps of:
step S201: and acquiring GPS data of the engineering vehicle in real time.
In this embodiment, step S201 in fig. 2 is similar to step S101 in fig. 1, and for brevity, is not described herein again.
Step S202: and when the engineering vehicle is determined to move to the preset area based on the GPS data, vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area are obtained.
In this embodiment, step S202 in fig. 2 is similar to step S102 in fig. 1, and for brevity, is not repeated herein.
Step S203: and confirming the service scene corresponding to the preset area, and acquiring a target scoring threshold and a target scoring model corresponding to the service scene, wherein each service scene corresponds to one group of scoring threshold and scoring model.
It should be noted that the construction site can be generally divided into a plurality of types, such as a building type, a traffic type, a water service type, a backfill construction site type, a digestion site type, and the like, and vehicle data of the engineering vehicle at different construction sites can also be different.
In step S203, the service scenario refers to the category of the worksite, and each category of worksite corresponds to one service scenario. In this embodiment, different service scenarios respectively correspond to different scoring thresholds and scoring models, and the scoring threshold and the scoring model corresponding to each service scenario are obtained by training historical vehicle data and historical engineering data corresponding to the service scenario.
Further, training the scoring model for different service scenarios specifically includes:
1. and confirming corresponding business rules based on the business scene, wherein each business rule comprises at least one influence factor. The business rule is a rule specified according to a business scenario, for example: the business rules corresponding to the house building engineering comprise: whether the empty vehicle enters or is out of the electronic fence area, whether the empty vehicle stays for more than one hour and the like; the business rules corresponding to the backfilling construction site engineering comprise: whether a heavy vehicle enters or leaves the electronic fence area in an empty state, whether the vehicle is in a flameout state and the like. The daily business rule includes at least one influencing factor, and takes the business rule of "whether empty vehicles enter or leave the electronic fence area again" as an example, the daily business rule includes at least two influencing factors: lifting data of the vehicle and box body state data of the vehicle. When it needs to be explained, the business rules and the influencing factors included in the business rules can be preset by the user according to actual needs, and the data corresponding to the influencing factors are vehicle data and engineering data.
2. And acquiring historical vehicle data and historical engineering data corresponding to the influence factors.
3. And inputting the historical vehicle data and the historical engineering data corresponding to the influence factors into a logistic regression model for training to obtain a weight item and an optimal scoring value corresponding to each influence factor. In the embodiment, a logistic regression model is adopted for training, and historical vehicle data and historical engineering data are input into the logistic regression model as characteristic variables to obtain a prediction result; evaluating the difference between the prediction result and the actual result by using a loss function; and updating and iterating for N times by adopting a gradient ascending method according to the difference between the prediction result and the actual result to obtain a weight item corresponding to each influence factor, and calculating the optimal grade value corresponding to each influence factor based on the weight item.
4. And constructing a grading model based on the influence factors, the weight items and the optimal grading value. The scoring model is as follows:
wherein m is the number of business rulesN is the number of influencing factors corresponding to each business rule, k ij Is the weight term, x, corresponding to each influence factor ij Is the optimal value of credit for each influencing factor.
5. And acquiring historical vehicle data and historical engineering data in the past preset time period at every interval of the preset time period, and training and updating the scoring model based on the historical vehicle data and the historical engineering data in the past preset time period. Due to the complexity of the actual construction work scene and the incapability of guaranteeing the integrity and the correctness of the source data, historical vehicle data and historical engineering data in a preset time period are obtained at intervals of the preset time period, and the historical vehicle data and the historical engineering data are used for training and updating the scoring model so as to guarantee that the scoring model is closer to the actual condition of the current engineering.
Further, obtaining a score threshold value for training different service scenarios specifically includes:
1. and inputting a plurality of groups of pre-prepared training sample data into the logistic regression model to train the logistic regression model.
It should be noted that the training process of the logistic regression model is not described herein again, and please refer to the training process of the scoring model specifically.
2. And inputting a plurality of groups of test sample data prepared in advance into the trained logistic regression model to obtain a plurality of groups of prediction results.
3. And calculating the accuracy rate, the real rate and the false positive rate by using the actual results corresponding to the multiple groups of test sample data and the multiple groups of prediction results.
It should be noted that in the logistic regression model, a confusion matrix is usually used to describe the performance index of the model, and the prediction results of the logistic regression model include true TP (true positive), true negative TN (true negative), false positive FP (false positive), and false negative FN (false negative), where true and false negative are the correct results of prediction. See table 1 for details:
TABLE 1
The accuracy rate can be used as related information indicating the number of misclassified samples, ACC =1-ERR = (TN + TP)/(TN + TP + FN + FP), ACC is the quasi-vanishing rate, and ERR is the prediction error.
The real rate TPR = TP/AP represents the ratio of the number of positive samples to the number of actual positive samples in the prediction and actual classification; false positive rate FPR = FP/AN represents the ratio of the number of samples predicted as positive class actually being negative class to the actual number of negative samples.
4. And constructing an ROC curve based on the accuracy, the real rate and the false positive rate, and confirming the optimal scoring threshold value by using the ROC curve. The ROC curve is drawn by variables 1-Specificity and Sensitivity, wherein the horizontal axis is False Positive Rate (FPR), the vertical axis is true rate (TPR), the diagonal line of the ROC curve represents random guess, the closer the ROC curve is to the upper left corner, the higher the recall ratio of the logistic regression model is, and the point on the ROC curve closest to the upper left corner is the optimal threshold value with the least error of the prediction result.
Step S204: and inputting the vehicle data and the engineering data into a target scoring model to obtain a target scoring result.
Step S205: and comparing the target scoring result with the target scoring threshold value.
Step S206: and when the scoring result is greater than or equal to the scoring threshold value, generating or finishing the electronic coupons.
In this embodiment, step S206 in fig. 2 is similar to step S104 in fig. 1, and for brevity, is not described herein again.
In the embodiment, the corresponding scoring threshold and the corresponding scoring model are respectively configured for different service scenes, and when the electronic coupons are managed, the corresponding scoring threshold and the corresponding scoring model are selected according to the service scenes, so that the accuracy of a final result is ensured to the maximum extent, and the method can be suitable for various different types of projects.
Fig. 3 is a schematic structural diagram of an electronic work vehicle coupon management device according to an embodiment of the present invention. As shown in fig. 3, the apparatus 30 includes a first obtaining module 31, a second obtaining module 32, a scoring module 33, and a management module 34.
The first acquisition module 31 is used for acquiring GPS data of the engineering vehicle in real time;
the second obtaining module 32 is configured to obtain vehicle-mounted terminal data of the engineering vehicle and engineering data of a construction site corresponding to a preset area when it is determined that the engineering vehicle moves to the preset area based on the GPS data;
the scoring module 33 is used for scoring by using the pre-trained scoring model, the vehicle-mounted terminal data and the engineering data, and comparing the scoring result with the pre-trained scoring threshold value, wherein the scoring model and the scoring threshold value are obtained by using the historical vehicle-mounted terminal data and the historical engineering data;
and the management module 34 is used for generating or completing the electronic coupons when the scoring result is greater than or equal to the scoring threshold value.
Optionally, the scoring module 33 may perform scoring by using the pre-trained scoring model, the vehicle data, and the engineering data, and compare the scoring result with the pre-trained scoring threshold: confirming a service scene corresponding to a preset area, and acquiring a target scoring threshold and a target scoring model corresponding to the service scene, wherein each service scene corresponds to a group of scoring threshold and scoring model; inputting the vehicle data and the engineering data into a target scoring model to obtain a target scoring result; and comparing the target scoring result with the target scoring threshold value.
Optionally, before using the trained scoring model, it is further required to train the scoring model based on different service scenarios, including: confirming corresponding business rules based on the business scene, wherein each business rule comprises at least one influence factor; acquiring historical vehicle data and historical engineering data corresponding to the influence factors; inputting historical vehicle data and historical engineering data corresponding to the influence factors into a logistic regression model for training to obtain a weight item and an optimal scoring value corresponding to each influence factor; and constructing a grading model based on the influence factors, the weight items and the optimal grading value.
Optionally, the scoring model is:
wherein m is the number of the business rules, n is the number of the influencing factors corresponding to each business rule, k ij Is the weight term, x, corresponding to each influence factor ij Is the optimal value of credit for each influencing factor.
Optionally, after the operation of constructing the scoring model based on the influencing factors, the weight terms, and the optimal scoring values, the method further includes: and acquiring historical vehicle data and historical engineering data in the past preset time period at every interval of the preset time period, and training and updating the scoring model based on the historical vehicle data and the historical engineering data in the past preset time period.
Optionally, before using the trained score threshold, it is also required to train the score threshold, including: inputting a plurality of groups of pre-prepared training sample data into the logistic regression model to train the logistic regression model; inputting a plurality of groups of test sample data prepared in advance into a trained logistic regression model to obtain a plurality of groups of prediction results; calculating the accuracy rate, the real rate and the false positive rate by using the actual results corresponding to the multiple groups of test sample data and the multiple groups of prediction results; and constructing an ROC curve based on the accuracy rate, the trueness rate and the false-trueness rate, and confirming the optimal scoring threshold value by utilizing the ROC curve.
Optionally, the management module 34 is further configured to prohibit generation or completion of the electronic coupons when the scoring result is less than the scoring threshold.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present invention. As shown in fig. 4, the terminal 40 includes a processor 41 and a memory 42 coupled to the processor 41.
The memory 42 stores program instructions for implementing the method for managing an electronic work vehicle coupon according to any one of the embodiments described above.
The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. Processor 41 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a storage medium according to an embodiment of the invention. The storage medium of the embodiment of the present invention stores a program file 51 capable of implementing all the methods described above, wherein the program file 51 may be stored in the storage medium in the form of a software product, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a mobile hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or terminal devices, such as a computer, a server, a mobile phone, and a tablet.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit. The above are only embodiments of the present application, and not intended to limit the scope of the present application, and all equivalent structures or equivalent processes performed by the present application and the contents of the attached drawings, which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (8)
1. An engineering vehicle electronic coupon management method is characterized by comprising the following steps:
acquiring GPS data of the engineering vehicle in real time;
when the engineering vehicle is determined to move to a preset area based on the GPS data, vehicle data of the engineering vehicle and engineering data of a construction site corresponding to the preset area are obtained;
scoring by utilizing a pre-trained scoring model, the vehicle data and the engineering data, and comparing a scoring result with a pre-trained scoring threshold value, wherein the scoring model and the scoring threshold value are obtained by utilizing historical vehicle data and historical engineering data in a training mode;
confirming a service scene corresponding to the preset area, and confirming corresponding service rules based on the service scene, wherein each service rule comprises at least one influence factor;
acquiring historical vehicle data and historical engineering data corresponding to the influence factors;
inputting historical vehicle data and historical engineering data corresponding to the influence factors into a logistic regression model for training to obtain a weight item and an optimal scoring value corresponding to each influence factor;
constructing a grading model based on the influence factors, the weight items and the optimal grading value; the scoring model is as follows:
wherein m is a business ruleThe number n is the number of the influencing factors corresponding to each business rule, k ij Is the weight term, x, corresponding to each influence factor ij Is the optimal score value corresponding to each influence factor; and when the scoring result is greater than or equal to the scoring threshold value, generating or finishing the electronic coupon.
2. The method for managing the electronic worksheets of claim 1, wherein the scoring with the pre-trained scoring model, the vehicle data and the engineering data and comparing the scoring result with a pre-trained scoring threshold comprises:
acquiring a target scoring threshold and a target scoring model corresponding to the service scenes, wherein each service scene corresponds to a group of scoring thresholds and scoring models;
inputting the vehicle data and the engineering data into the target scoring model to obtain a target scoring result;
comparing the target scoring result with the target scoring threshold.
3. The method for managing the electronic coupons of engineering vehicles according to claim 1, wherein after said constructing a scoring model based on said influencing factors, said weighting terms and said optimal scoring values, further comprising:
and acquiring historical vehicle data and historical engineering data in the past preset time period at every interval of preset time period, and training and updating the grading model based on the historical vehicle data and the historical engineering data in the past preset time period.
4. The method for managing the electronic coupons of engineering vehicles according to claim 2, further comprising a training score threshold, said training score threshold comprising:
inputting a plurality of groups of pre-prepared training sample data into a logistic regression model to train the logistic regression model;
inputting a plurality of groups of test sample data prepared in advance into a trained logistic regression model to obtain a plurality of groups of prediction results;
calculating the accuracy rate, the real rate and the false positive rate by using the actual results corresponding to the multiple groups of test sample data and the multiple groups of prediction results;
and constructing an ROC curve based on the accuracy rate, the trueness rate and the false-trueness rate, and confirming an optimal scoring threshold value by utilizing the ROC curve.
5. The method for managing the electronic worksheets of claim 1, wherein after comparing the scoring result with a pre-trained scoring threshold, the method further comprises:
and when the scoring result is smaller than the scoring threshold value, forbidding generation or completion of the electronic coupons.
6. The utility model provides an engineering vehicle electron antithetical couplet list management device which characterized in that includes:
the first acquisition module is used for acquiring GPS data of the engineering vehicle in real time;
the second acquisition module is used for acquiring vehicle data of the engineering vehicle and engineering data of a construction site corresponding to a preset area when the engineering vehicle is determined to move to the preset area based on the GPS data; confirming a service scene corresponding to the preset area, and confirming corresponding service rules based on the service scene, wherein each service rule comprises at least one influence factor;
acquiring historical vehicle data and historical engineering data corresponding to the influence factors;
inputting historical vehicle data and historical engineering data corresponding to the influence factors into a logistic regression model for training to obtain a weight item and an optimal scoring value corresponding to each influence factor;
constructing a grading model based on the influence factors, the weight items and the optimal grading value;
the scoring module is used for scoring by utilizing a pre-trained scoring model, the vehicle data and the engineering data, and comparing a scoring result with a pre-trained scoring threshold value, wherein the scoring model and the scoring threshold value are obtained by utilizing historical vehicle data and historical engineering data in a training mode; the scoring model is as follows:
wherein m is the number of the business rules, n is the number of the influencing factors corresponding to each business rule, k ij Is the weight term, x, corresponding to each influence factor ij Is the optimal score value corresponding to each influence factor;
and the management module is used for generating or finishing the electronic coupon when the scoring result is greater than or equal to the scoring threshold value.
7. A terminal, comprising a processor, a memory coupled to the processor, wherein,
the memory stores program instructions for implementing the work vehicle electronic coupons management method of any one of claims 1-5;
the processor is configured to execute the program instructions stored by the memory to manage an electronic coupon for a work vehicle.
8. A storage medium characterized by storing a program file capable of implementing the work vehicle electronic bill management method according to any one of claims 1 to 5.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103034178A (en) * | 2011-10-10 | 2013-04-10 | 无锡协讯科技有限公司 | Hazardous waste supervisory system and electronic receipt management method thereof |
| CN103927611A (en) * | 2013-11-08 | 2014-07-16 | 深圳市博安达软件开发有限公司 | Hazardous waste transfer electronic manifest management system and method |
| CN106503152A (en) * | 2016-10-21 | 2017-03-15 | 合网络技术（北京）有限公司 | Title treating method and apparatus |
| CN108170909A (en) * | 2017-12-13 | 2018-06-15 | 中国平安财产保险股份有限公司 | Model output method, equipment and the storage medium of a kind of intelligent modeling |
| CN110553681A (en) * | 2019-08-28 | 2019-12-10 | 深圳市航通北斗信息技术有限公司 | method and apparatus for monitoring discharge of waste, and computer-readable storage medium |
| CN110717650A (en) * | 2019-09-06 | 2020-01-21 | 平安医疗健康管理股份有限公司 | Receipt data processing method and device, computer equipment and storage medium |
| CN111294458A (en) * | 2020-01-17 | 2020-06-16 | 广州首联环保服务有限公司 | Client-side APP system for bill-sharing management |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107704495B (en) * | 2017-08-25 | 2018-08-10 | 平安科技（深圳）有限公司 | Training method, device and the computer readable storage medium of subject classification device |
-
2020
- 2020-06-16 CN CN202010547632.5A patent/CN111695820B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103034178A (en) * | 2011-10-10 | 2013-04-10 | 无锡协讯科技有限公司 | Hazardous waste supervisory system and electronic receipt management method thereof |
| CN103927611A (en) * | 2013-11-08 | 2014-07-16 | 深圳市博安达软件开发有限公司 | Hazardous waste transfer electronic manifest management system and method |
| CN106503152A (en) * | 2016-10-21 | 2017-03-15 | 合网络技术（北京）有限公司 | Title treating method and apparatus |
| CN108170909A (en) * | 2017-12-13 | 2018-06-15 | 中国平安财产保险股份有限公司 | Model output method, equipment and the storage medium of a kind of intelligent modeling |
| CN110553681A (en) * | 2019-08-28 | 2019-12-10 | 深圳市航通北斗信息技术有限公司 | method and apparatus for monitoring discharge of waste, and computer-readable storage medium |
| CN110717650A (en) * | 2019-09-06 | 2020-01-21 | 平安医疗健康管理股份有限公司 | Receipt data processing method and device, computer equipment and storage medium |
| CN111294458A (en) * | 2020-01-17 | 2020-06-16 | 广州首联环保服务有限公司 | Client-side APP system for bill-sharing management |
Non-Patent Citations (2)
| Title |
|---|
| 潘腾 ; 张弛 ; 陆大根 ; 王恒俭 ; 刘术军 ; .基于物联网的杭州市危险废物智能监管平台设计与应用.中国环境管理.2016,全文. * |
| 黄鼎曦,王洪涛.基于因特网的危险废物转移联单系统研究与开发.环境污染治理技术与设备.2002,全文. * |
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