CN114997527A - Enterprise assessment and evaluation method, system and terminal based on road transportation dynamic data - Google Patents
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Abstract
The invention discloses an enterprise assessment and evaluation method, system and terminal based on road transportation dynamic data, which belong to the technical field of transportation, and the scheme comprises the steps of acquiring basic data and historical vehicle travel data of different service platforms; calculating and generating the next-day predicted travel amount according to historical vehicle travel data; constructing an assessment index library, and determining assessment indexes aiming at enterprises; processing the basic data according to the assessment indexes and the next-day predicted output amount, and dynamically generating an assessment data matrix; and dynamically generating an assessment model, and calculating assessment results of each enterprise according to the assessment data matrix. The method and the device have the effects that when the next-day predicted travel volume is large, the reduction data of the enterprise are obtained in a reducing mode, or the weight ratio of the added items is improved, so that the assessment score of the enterprise is improved in a short time, and the vehicle which can actually go on a journey can meet daily social requirements.
Description
Technical Field
The invention relates to the technical field of transportation, in particular to an enterprise assessment and evaluation method, system and terminal based on road transportation dynamic data.
Background
The thousandth system road transportation assessment and evaluation method and the system refer to the relevant data of a traffic law enforcement and supervision department and graded classification assessment scores on the basis of traffic big data, namely 'one data center and multi-party multiplexing'. The method integrates the behavior data of enterprises and practitioners in the data center of the law enforcement agency, integrates and intensively manages the existing information, and promotes the high integration of the classified evaluation information and the credit information.
The existing dynamic assessment of road transportation is to perform assessment and evaluation on enterprises by acquiring data of the enterprises and using an evaluation model in combination with the acquired data of the enterprises. And the supervision mechanism can supervise and enforce the enterprises and even limit the vehicles of the enterprises to go out if the final assessment score is lower than a certain value according to the final assessment score.
In the process of implementing the present application, the inventors found that the above-mentioned technology has at least the following problems: after the enterprise is assessed by using the evaluation model, if too many enterprises for limiting traveling are available, the mobile vehicles cannot meet the social requirements, so that normal social operation is influenced.
Disclosure of Invention
In order to solve the problem that normal social operation is influenced because mobile vehicles cannot meet social requirements due to the fact that too many enterprises limiting traveling are provided, the application provides an enterprise assessment and evaluation method, system and terminal based on road transportation dynamic data.
In a first aspect, the application provides an enterprise assessment and evaluation method based on road transportation dynamic data, and adopts the following technical scheme;
an enterprise assessment and evaluation method based on road transportation dynamic data comprises the following steps:
acquiring basic data and historical vehicle travel data of different service platforms;
calculating and generating the next-day predicted travel amount according to historical vehicle travel data;
constructing an assessment index library, and determining assessment indexes aiming at enterprises;
processing the basic data according to the assessment indexes and the next-day predicted output amount, and dynamically generating an assessment data matrix, wherein the assessment data matrix comprises a plurality of assessment subdata;
dynamically generating an assessment model according to assessment indexes, and calculating assessment results of each enterprise according to the dynamically generated assessment model and an assessment data matrix, wherein the assessment model is provided with a corresponding weight ratio for each assessment subdata;
the evaluation result of each enterprise evaluation calculated according to the evaluation data matrix specifically comprises the following contents:
obtaining an assessment data matrix, calculating the score corresponding to each assessment subdata through an assessment model, and calculating enterprise assessment results according to the weight ratio corresponding to each assessment subdata;
specifically, the weight ratio corresponding to the assessment subdata is regarded as a coefficient, a corresponding coefficient matrix is generated according to the distribution of the assessment subdata in the assessment data matrix, the assessment data matrix is A, the coefficient matrix is B, and the calculation is carried out according to the following formula:
wherein λ is i To evaluate the corresponding coefficient, alpha, of the subdata i To assess the score corresponding to the subdata, i =1, 2, 3.
By adopting the technical scheme, the predicted trip amount of the next day is obtained according to the historical vehicle trip data prediction, the assessment data matrix is obtained or the corresponding assessment model is generated according to the predicted trip amount of the next day, and when the predicted trip amount of the next day is larger, the reduction data of an enterprise is reduced or the weight ratio of the added items is improved, so that the assessment score of the enterprise is improved in a short time, and the vehicle which can actually trip can meet the daily social demand.
In a specific implementation, the acquiring the basic data of the different service platforms specifically includes:
and presetting a timing operation task, and regularly pulling the basic data of each service platform according to the timing operation task.
By adopting the technical scheme, the real-time performance of the message can be ensured by regularly pulling the basic data, so that some data which have great influence on the enterprise assessment can be timely acquired, the enterprise assessment score can be timely updated, the enterprise which can go out of vehicles can be ensured to meet the standard, and the safety of transportation is improved.
In a specific implementation, the calculating and generating the predicted travel amount of the next day according to the historical vehicle travel data specifically includes:
acquiring day information and current time information;
fitting the day information and the historical vehicle travel data to generate a travel amount change curve;
and obtaining the next-day predicted trip volume based on the trip volume change curve and the current time information according to the characteristic that the trip volume has week similarity.
By adopting the technical scheme, the next-day predicted output is determined from the output change curve generated by fitting according to the current time information, and because the outputs in different time periods of each week are different, the next-day output can be predicted more accurately and objectively by generating the output change curve by fitting.
In a specific embodiment, the processing the basic data according to the assessment indicators and the next-day predicted operation amount, and the dynamically generating the assessment data matrix specifically includes:
classifying the basic data, and marking the same basic data with basic labels, wherein each basic label corresponds to the content of an assessment index;
obtaining a basic label corresponding to the content of the assessment index according to the assessment index, and taking the basic label corresponding to the content of the assessment index as the assessment label;
and screening out basic data corresponding to the assessment labels from all the basic data to be used as an assessment data matrix.
By adopting the technical scheme, the acquired basic data are used for being checked by each platform, however, only the basic data required by the examination can be acquired during the actual examination, and the examination efficiency can be improved by screening the basic data.
In a specific possible embodiment, the obtaining of the basic label corresponding to the content of the assessment index according to the assessment index previously includes:
presetting a trip quantity threshold value, and judging whether the predicted trip quantity the next day is higher than the trip quantity threshold value;
and if the predicted trip amount is higher than the trip amount threshold value the next day, hiding the basic labels corresponding to the basic data of part types, wherein the basic data of part types are preset data which can not participate in assessment and evaluation.
By adopting the technical scheme, part of irrelevant bad basic data is hidden, so that the influence of the data on enterprise assessment is reduced, the enterprise vehicles can normally travel in a short time, and the actual social requirements are met as far as possible.
In a specific possible implementation, the hiding the base tag corresponding to the part of the type of base data specifically includes:
acquiring data attributes corresponding to different types of basic data, wherein the data attributes are divided into high-influence attributes and low-influence attributes, and the partial types of basic data are basic data corresponding to the low-influence attributes;
and changing the basic label corresponding to the basic data with the low-influence attribute into a special label, wherein the assessment model cannot acquire the basic data of which the basic label is changed into the special label according to the assessment index.
By adopting the technical scheme, the basic data can be analyzed when being hidden, the low-impact data is the data without serious impact or serious potential safety hazard, the basic data is hidden, and the traffic transportation safety can be ensured while the enterprise assessment score is ensured to meet the qualified requirement as much as possible.
In a specific embodiment, the dynamically generating the assessment model specifically includes:
the method comprises the steps of obtaining a plurality of assessment subdata, a weight ratio corresponding to each assessment subdata and a data attribute of each assessment subdata, wherein the data attribute is divided into a first attribute and a second attribute;
presetting a trip amount threshold value, and judging whether the predicted trip amount of the next day is higher than the trip amount threshold value;
if the predicted trip amount is higher than the trip amount threshold value on the next day, increasing the weight ratio corresponding to the assessment subdata with the first attribute, and reducing the weight ratio corresponding to the assessment subdata with the second attribute;
and generating an assessment model according to the assessment subdata and the weight ratio corresponding to the modified assessment subdata.
By adopting the technical scheme, the weight ratio corresponding to each assessment subdata is changed, and the weight ratio corresponding to the assessment subdata with the first attribute is improved, so that the assessment score of an enterprise is further improved, and the enterprise vehicle can normally travel.
In a second aspect, the application provides a road transportation dynamic assessment system, which adopts the following technical scheme;
a road transportation dynamic assessment and evaluation system comprises: the system comprises a data acquisition module, a traffic prediction module, an assessment index storage module, a data screening module and a scoring module;
the data acquisition module is used for acquiring basic data, current time information and historical vehicle travel data of different service platforms;
the trip amount prediction module is used for predicting the predicted trip amount the next day according to the historical vehicle trip data and the current time information;
the assessment index storage module is used for storing assessment indexes of enterprises;
the data screening module is used for screening the assessment subdata from all the basic data according to the next-day predicted output and the assessment indexes;
and the scoring module is used for calculating assessment results of each enterprise according to the assessment subdata.
By adopting the technical scheme, the trip amount prediction module predicts the trip amount of the vehicle according to historical vehicle trip data to obtain the predicted trip amount of the next day, acquires an assessment data matrix or generates a corresponding assessment model according to the predicted trip amount of the next day, and reduces the number of acquired reduction data of an enterprise or improves the weight ratio of added items when the predicted trip amount of the next day is large, so that the assessment score of the enterprise is improved in a short time, and the vehicle which can actually trip can meet daily social requirements.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical solution;
an intelligent terminal comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is executed by the processor to realize an enterprise assessment and evaluation method based on road transportation dynamic data.
By adopting the technical scheme, the predicted trip amount of the next day is obtained according to the historical vehicle trip data prediction, the assessment data matrix is obtained or the corresponding assessment model is generated according to the predicted trip amount of the next day, and when the predicted trip amount of the next day is larger, the reduction data of an enterprise is reduced or the weight ratio of the added items is improved, so that the assessment score of the enterprise is improved in a short time, and the vehicle which can actually trip can meet the daily social demand.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions;
a computer-readable storage medium, comprising a readable storage medium and a computer program stored in the readable storage medium for execution, the computer program being loaded by a processor and executed to implement a method for assessing and evaluating an enterprise based on dynamic data of road transportation as described in any one of the above.
By adopting the technical scheme, the predicted trip amount of the next day is obtained according to the historical vehicle trip data prediction, the assessment data matrix is obtained or the corresponding assessment model is generated according to the predicted trip amount of the next day, and when the predicted trip amount of the next day is larger, the reduction data of an enterprise is reduced or the weight ratio of the added items is improved, so that the assessment score of the enterprise is improved in a short time, and the vehicle which can actually trip can meet the daily social demand.
In summary, the present application includes at least one of the following beneficial technical effects:
1. when the predicted travel quantity is large the next day, the reduction data of the enterprise is reduced or the weight ratio of the added items is improved, so that the assessment score of the enterprise is improved in a short period, and the vehicle which can actually go out can meet daily social demands.
2. The next-day trip volume can be predicted more accurately and objectively by generating the trip volume change curve through fitting.
3. The method can ensure the safety of transportation while ensuring that the assessment score of the enterprise meets the qualified requirement as much as possible.
Drawings
Fig. 1 is a schematic overall structure diagram of a road transportation dynamic assessment and evaluation system in an embodiment of the present application.
Fig. 2 is a schematic overall structure diagram of a road transportation dynamic assessment and evaluation system in another embodiment of the present application.
Fig. 3 is an overall flowchart of an enterprise assessment and evaluation method based on road transportation dynamic data in the embodiment of the present application.
Fig. 4 is a schematic diagram of a calculation flow of the predicted travel amount of the next day in the embodiment of the present application.
FIG. 5 is a schematic diagram of an acquisition process of the assessment data matrix in the embodiment of the present application.
Fig. 6 is a schematic flowchart of basic tag hiding in an embodiment of the present application.
FIG. 7 is a schematic flow chart illustrating a process of calculating assessment evaluation results of an enterprise in an embodiment of the present application.
Fig. 8 is a schematic flow chart of updating the weight ratio in the embodiment of the present application.
Description of the reference numerals:
1. a data acquisition module; 2. a trip amount prediction module; 3. an assessment index storage module; 4. a data screening module; 5. and a scoring module.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
One embodiment of the application discloses a road transportation dynamic assessment and evaluation system, and referring to fig. 1, the system comprises a data acquisition module 1, an assessment index storage module 3 and a scoring module 5, wherein the data acquisition module 1 is used for acquiring basic data of different service platforms; the assessment index storage module 3 is used for storing assessment indexes of enterprises; and the scoring module 5 is used for calculating the assessment results of the enterprises according to the assessment models and by combining the collected basic data and assessment indexes.
The data acquisition module 1 calls an API (application program interface) of a service platform in an API (application program interface) mode to realize data pulling processing, and the service platform comprises a provincial and public security system, a vehicle active security system, a freight transportation platform system and a two-passenger and one-dangerous system. The data acquisition module 1 acquires basic data from each platform and summarizes the acquired basic data.
In another embodiment of the present application, referring to fig. 2, the system further includes a trip amount prediction module 2 and a data screening module 4, and the data collection module 1 is further configured to collect current time information and historical vehicle trip data; the trip amount prediction module 2 is used for predicting the predicted trip amount the next day according to the historical vehicle trip data and the current time information; the data screening module 4 is used for screening the assessment subdata from all the basic data according to the next-day predicted output and the assessment indexes.
Specifically, the historical vehicle travel data is travel volume data of one month, and because the travel volumes have the characteristic of week similarity, the travel volume of the same day of each week in one month is predicted by calculating an average value of the travel volume data of the same day of each week.
For example, the amount of one week-old work out is 1.5 ten thousand for the first week, 1.4 ten thousand for the second week, 1.6 ten thousand for the third week, and 1.8 ten thousand for the fourth week, and the average amount of work out per one week in one month is calculated to be 1.575 ten thousand. If the obtained current time information is the week of the fourth week, the predicted running amount of the next day is 1.575 ten thousand.
In implementation, basic data, current time information and historical vehicle travel data are collected through the data collection module 1, next-day predicted travel quantity is predicted through the travel quantity prediction module 2, then the data screening module 4 obtains assessment indexes from the assessment index storage module 3 and screens assessment subdata by combining the next-day predicted travel quantity, and finally the scoring module 5 calculates assessment evaluation results of enterprises according to the assessment subdata. Through a predicted travel quantity optimization scoring mode, the assessment score of an enterprise is improved in a short time, and the vehicle capable of actually traveling can meet daily social requirements.
The following describes in detail the implementation of an enterprise assessment method based on dynamic road transportation data with reference to a dynamic road transportation assessment system:
referring to fig. 3, another embodiment of the present application provides an enterprise assessment and evaluation method based on road transportation dynamic data, including the following steps:
and S10, acquiring basic data and historical vehicle travel data of different service platforms.
In the embodiment of the application, the basic data is obtained by presetting a timing job task and pulling the basic data of each service platform according to the timing job task at regular time. Specifically, the timing task sets the interval time for pulling the basic data, and calls the service platform API interface in an API interface mode to realize the pulling processing of the basic data after the interval time is reached.
The service platform comprises an provincial and administrative platform system, a public security system, a vehicle active security system, a freight platform system and a two-passenger one-danger system.
In another embodiment of the present application, the acquired basic data is collected to a traffic supervision data center, and the basic data is cleaned before being collected to the traffic supervision data center. For different service platforms, different cleaning treatment schemes are generally adopted, and the cleaning treatment schemes specifically include:
the cleaning treatment scheme aiming at the provincial and administrative platform system comprises the following steps: the provincial and administrative platform system relates to basic information data of vehicles, basic information of practitioners and basic information of enterprise registration, spare fields are removed in the data docking process, modification time fields and user fields are created, license plate numbers, license plate colors, annual review validity periods, operation range fields and state fields are reserved, and the state fields are converted from original Chinese identification fields into digital storage and data type standardization definition.
The cleaning treatment scheme aiming at the vehicle active security system comprises the following steps: the active security system of the vehicle relates to dangerous driving behaviors such as overspeed, fatigue driving, prohibition at night and the like, false alarm is removed in the data docking process, and the same vehicle only stores the same type of alarm condition as one piece of basic data every day.
The cleaning treatment scheme of the public security system, the freight platform system and the two-passenger one-danger system is consistent with that of the provincial transportation platform system.
And S20, calculating and generating the next-day predicted travel amount according to the historical vehicle travel data.
Since the trip volume has the characteristic of week similarity, the generation of the next-day predicted trip volume is determined according to the rule of historical vehicle trip data, and referring to fig. 4, the method specifically includes the following steps:
s21, acquiring day information and current time information;
s22, fitting the day information and the historical vehicle travel data to generate a travel amount change curve;
and S23, obtaining the next-day predicted travel amount based on the travel amount change curve and the current time information according to the characteristic that the travel amounts have week similarity.
Specifically, the day information is the days of the week, the generated variation curve of the trip volume is represented in a rectangular coordinate system, and the abscissa of the rectangular coordinate system has seven parameter points which are Monday, Tuesday, Wednesday, Thursday, Friday, Saturday and Sunday respectively; the ordinate of the rectangular coordinate system represents the travel amount, and the time of the acquired historical vehicle travel data corresponding to the abscissa is recorded in the rectangular coordinate system. And the travel amount change curve dynamically changes according to the obtained historical vehicle travel data.
In implementation, the current time information is acquired as Tuesday, the running amount value corresponding to the Tuesday is determined according to the real-time running amount change curve, and the running amount value is used as the next-day predicted running amount.
S30, constructing an assessment index library, and determining assessment indexes for enterprises.
The assessment indexes comprise transportation qualification, safety management, dynamic management, industry supervision, illegal behaviors and scoring items.
And S40, processing the basic data according to the assessment indexes and the next-day predicted running amount, and dynamically generating an assessment data matrix.
In the embodiment of the application, the basic data acquired from each service platform is stored in the traffic supervision data center, so that the data intercommunication of each service platform can be realized conveniently. However, for the assessment of the enterprise, only the basic data corresponding to the assessment indexes needs to be obtained, so that the basic data needs to be screened, and referring to fig. 5, the method specifically includes the following steps:
s41, classifying the basic data, and marking the same basic data with basic labels, wherein each basic label corresponds to the content of an assessment index;
s42, obtaining a basic label corresponding to the content of the assessment index according to the assessment index, and taking the basic label corresponding to the content of the assessment index as the assessment label;
and S43, screening out the basic data corresponding to the assessment labels from all the basic data to be used as an assessment data matrix.
For example, the assessment index is transportation qualification, and the acquired basic data comprises the working qualification of the driver, the scale of the enterprise vehicle, the record of overspeed driving of the vehicle and the occurrence rate of traffic accidents. The system comprises a driver, a vehicle monitoring system, a traffic accident rate monitoring system, a traffic accident monitoring system and a traffic accident monitoring system, wherein the driver applies a qualification certificate and an enterprise vehicle scale to correspond to a transportation qualification label, a vehicle overspeed driving record corresponds to a dynamic supervision label, and the traffic accident rate corresponds to an illegal behavior label. Therefore, the driver professional qualifications and the enterprise vehicle scale in the basic data are used as assessment data, and a corresponding assessment data matrix is generated.
In another embodiment of the present application, in order to meet the social demand for vehicles as much as possible, the method may timely adjust the acquisition of the basic data according to the next-day predicted travel amount, so as to ensure that the vehicles traveling can meet the normal social demand as much as possible, and specifically includes the following steps:
presetting a trip amount threshold value, and judging whether the predicted trip amount of the next day is higher than the trip amount threshold value;
if the predicted trip amount is higher than the trip amount threshold value the next day, hiding the basic labels corresponding to the basic data of part types;
and if the predicted output quantity of the next day is not higher than the output quantity threshold value, hiding the basic label.
After the enterprise is examined every time, whether the enterprise is limited to go out or not can be judged, so that vehicles which can go out by the enterprise can be obtained, and all the vehicles which can go out by the enterprise can be counted. And obtaining vehicles which can travel by all enterprises in the multiple assessment results, and taking the average value of the vehicles which can travel by all enterprises in the multiple assessment results as a travel threshold.
In implementation, after the basic tag is hidden, the basic data corresponding to the basic tag cannot be acquired according to the assessment standard, and some basic data which may reduce the enterprise score cannot be acquired. In order to reduce the potential safety hazard, therefore, the selective hiding processing is performed on the base tag, with reference to fig. 6, the method specifically includes the following steps:
a1, acquiring data attributes corresponding to different types of basic data;
the data attributes are divided into high-impact attributes and low-impact attributes, and the partial types of basic data are basic data corresponding to the low-impact attributes.
And A2, changing the basic label corresponding to the basic data with the low influence attribute into a specific label.
In implementation, the low-impact data are data without serious impact or serious potential safety hazard, such basic data are hidden, and the traffic transportation safety can be ensured while the enterprise assessment scores are ensured to meet the qualified requirements as much as possible. And when the predicted trip amount of the next day is not higher than the trip amount threshold value, the specific label is restored to the basic label, so that the enterprise is accurately evaluated.
And S50, dynamically generating an assessment model according to assessment indexes, and calculating assessment results of each enterprise according to the dynamically generated assessment model and the assessment data matrix.
Referring to fig. 7, the evaluation result of each enterprise evaluation calculated according to the evaluation data matrix specifically includes the following steps:
b1, acquiring an assessment data matrix;
the assessment data matrix comprises a plurality of assessment subdata, and the assessment model is provided with a corresponding weight ratio aiming at each assessment subdata.
And B2, calculating the score corresponding to each assessment subdata through the assessment model, and calculating the assessment result of the enterprise according to the weight ratio corresponding to each assessment subdata.
Specifically, the weight ratio corresponding to the assessment sub-data is regarded as a coefficient, a corresponding coefficient matrix is correspondingly generated according to the distribution of the assessment sub-data in the assessment data matrix, the assessment data matrix is A, the coefficient matrix is B, and the calculation is performed according to the following formula:
wherein λ is i To evaluate the corresponding coefficient, alpha, of the subdata i To assess the score corresponding to the subdata, i =1, 2, 3.
In the embodiment, the assessment model comprises passenger transport assessment, general cargo assessment and dangerous cargo assessment, and assessment indexes of the passenger transport assessment comprise transport quality, safety management, dynamic management, industry supervision and illegal behaviors; the assessment indexes of the dangerous goods assessment and the general goods assessment comprise transportation qualification, safety management, dynamic management, industry supervision, illegal behaviors and addition items.
The data needed by the above six categories of assessment items specifically include:
transportation qualification: and the scale of enterprise vehicles, whether the qualification certificate of the driver is effective or not, the proportion of the driver to the vehicles in operation, the operation license and the road transportation certificate are kept in the valid period and the annual inspection valid period are examined.
Safety management: the safety assessment system comprises safety assessment of main responsible persons of assessment enterprises, safety management personnel configuration, dynamic monitoring personnel allocation, a safety production management system, safety meeting files, driver education and training files, vehicle inspection files, hidden danger investigation files and safety production standardization.
Dynamic management: the method comprises the steps of checking the active security installation proportion, overspeed driving, offline displacement, fatigue driving, physiological fatigue (distraction driving), smoking alarm, abnormal driver, call receiving and making, night prohibition and timely processing rate of serious alarm.
Industry supervision: the method comprises the following steps of nuclear enterprise safety inspection, dynamic supervision, reporting notification, responsible complaints, exposure notification and enterprise job-keeping conditions.
The illegal act: and (3) examining the law-violating behaviors of the transport administration, the incidence rate of traffic accidents, the incidence rate of traffic death accidents, the incidence rate of traffic violations and key traffic violations.
Adding a branch term: the method comprises the steps of examining enterprise safety production standardization, registering safety engineers, displaying and rewarding, and exchanging and sharing experience.
In implementation, the scoring is performed according to a preset weight ratio.
In another embodiment of the present application, in order to meet the social requirements of the vehicle as much as possible, the assessment model is dynamically adjusted according to the predicted traffic next day, with reference to fig. 8, which specifically includes the following steps:
c1, acquiring a plurality of assessment subdata, the weight ratio corresponding to each assessment subdata and the data attribute of each assessment subdata;
the data attribute is divided into a first attribute and a second attribute.
C2, presetting a trip amount threshold value, and judging whether the predicted trip amount the next day is higher than the trip amount threshold value;
if the predicted traffic flow is higher than the traffic flow threshold value on the next day, increasing the weight ratio corresponding to the assessment subdata with the first attribute, and reducing the weight ratio corresponding to the assessment subdata with the second attribute;
and C3, generating an assessment model according to the assessment subdata and the weight ratio corresponding to the modified assessment subdata.
In implementation, the assessment score of an enterprise is improved in a short period by improving the weight ratio of the added items, so that the vehicle which can actually go out can meet daily social requirements.
Based on the same inventive concept, the embodiment of the application also discloses an intelligent terminal. An intelligent terminal comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the computer program is executed by the processor, the enterprise assessment evaluation method based on road transportation dynamic data provided by the embodiment of the method is realized.
Based on the same inventive concept, the embodiment of the present application further discloses a computer-readable storage medium, where at least one instruction, at least one program, a code set, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the code set, or the set of instructions can be loaded and executed by a processor to implement the enterprise assessment evaluation method based on dynamic road transportation data provided by the embodiment of the method.
It should be understood that reference to "a plurality" herein means two or more. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Those skilled in the art will appreciate that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing associated hardware, and the program may be stored in a computer-readable storage medium, where the above-mentioned storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. An enterprise assessment and evaluation method based on road transportation dynamic data is characterized by comprising the following steps:
acquiring basic data and historical vehicle travel data of different service platforms;
calculating and generating the next-day predicted travel amount according to historical vehicle travel data;
constructing an assessment index library, and determining assessment indexes aiming at enterprises;
processing the basic data according to the assessment indexes and the next-day predicted output amount, and dynamically generating an assessment data matrix, wherein the assessment data matrix comprises a plurality of assessment subdata;
dynamically generating an assessment model according to assessment indexes, and calculating assessment results of each enterprise according to the dynamically generated assessment model and an assessment data matrix, wherein the assessment model is provided with a corresponding weight ratio for each assessment subdata;
the evaluation result of each enterprise evaluation calculated according to the evaluation data matrix specifically comprises the following contents:
obtaining an assessment data matrix, calculating the score corresponding to each assessment subdata through an assessment model, and calculating enterprise assessment results according to the weight ratio corresponding to each assessment subdata;
specifically, the weight ratio corresponding to the assessment subdata is regarded as a coefficient, a corresponding coefficient matrix is generated according to the distribution of the assessment subdata in the assessment data matrix, the assessment data matrix is A, the coefficient matrix is B, and the calculation is carried out according to the following formula:
wherein λ is i To evaluate the corresponding coefficient, alpha, of the subdata i To assess the score corresponding to the subdata, i =1, 2, 3.
2. The method for assessing and evaluating the enterprise based on the dynamic data of the road transportation according to claim 1, wherein the obtaining of the basic data of different service platforms specifically comprises:
and presetting a timing operation task, and regularly pulling the basic data of each service platform according to the timing operation task.
3. The enterprise assessment and evaluation method based on road transportation dynamic data as claimed in claim 1, wherein the step of calculating and generating the predicted going quantity of the next day according to the historical vehicle going data specifically comprises:
acquiring day information and current time information;
fitting the day information and the historical vehicle travel data to generate a travel amount change curve;
and obtaining the next-day predicted trip volume based on the trip volume change curve and the current time information according to the characteristic that the trip volume has week similarity.
4. The method for assessing and evaluating the enterprise based on the dynamic data of the road transportation according to claim 1, wherein the step of processing the basic data according to the assessment indexes and the next day predicted output amount and dynamically generating the assessment data matrix specifically comprises the steps of:
classifying the basic data, and marking the same basic data with basic labels, wherein each basic label corresponds to the content of an assessment index;
obtaining a basic label corresponding to the content of the assessment index according to the assessment index, and taking the basic label corresponding to the content of the assessment index as the assessment label;
and screening out basic data corresponding to the assessment labels from all the basic data to be used as an assessment data matrix.
5. The assessment method for the enterprises based on the dynamic data of road transportation as claimed in claim 4, wherein the obtaining of the basic label corresponding to the content of the assessment index according to the assessment index comprises:
presetting a trip quantity threshold value, and judging whether the predicted trip quantity the next day is higher than the trip quantity threshold value;
and if the predicted trip amount is higher than the trip amount threshold value the next day, hiding the basic labels corresponding to the basic data of part types, wherein the basic data of part types are preset data which can not participate in assessment and evaluation.
6. The method for assessing and evaluating the enterprise based on the dynamic data of road transportation according to claim 5, wherein the hiding the basic tags corresponding to the basic data of some types specifically comprises:
acquiring data attributes corresponding to different types of basic data, wherein the data attributes are divided into high-influence attributes and low-influence attributes, and the partial types of basic data are basic data corresponding to the low-influence attributes;
and changing the basic label corresponding to the basic data with the low-influence attribute into a specific label, wherein the assessment model cannot acquire the basic data of which the basic label is changed into the specific label according to the assessment index.
7. The assessment evaluation method for the enterprise based on the dynamic data of road transportation as claimed in claim 4, wherein the dynamically generating assessment model specifically comprises:
the method comprises the steps of obtaining a plurality of assessment subdata, a weight ratio corresponding to each assessment subdata and data attributes of each assessment subdata, wherein the data attributes are divided into first attributes and second attributes;
presetting a trip amount threshold value, and judging whether the predicted trip amount of the next day is higher than the trip amount threshold value;
if the predicted trip amount is higher than the trip amount threshold value on the next day, increasing the weight ratio corresponding to the assessment subdata with the first attribute, and reducing the weight ratio corresponding to the assessment subdata with the second attribute;
and generating an assessment model according to the assessment subdata and the weight ratio corresponding to the modified assessment subdata.
8. The road transportation dynamic assessment and evaluation system is characterized by comprising: the system comprises a data acquisition module (1), a trip quantity prediction module (2), an assessment index storage module (3), a data screening module (4) and a scoring module (5);
the data acquisition module (1) is used for acquiring basic data, current time information and historical vehicle travel data of different service platforms;
the trip amount prediction module (2) is used for predicting the predicted trip amount the next day according to historical vehicle trip data and current time information;
the assessment index storage module (3) is used for storing assessment indexes of enterprises;
the data screening module (4) is used for predicting the output according to the next day and screening the assessment subdata from all the basic data by combining the assessment indexes;
and the scoring module (5) is used for calculating assessment results of each enterprise according to the assessment subdata.
9. An intelligent terminal, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the computer program, when executed by the processor, implements a method for assessing and evaluating an enterprise based on dynamic data of road transportation according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a readable storage medium and a computer program stored in the readable storage medium for execution, the computer program being loaded by a processor and executed to implement the method for assessing and evaluating an enterprise based on dynamic data of road transportation according to any one of claims 1 to 7.
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