CN112633076A - Commercial vehicle monitoring system based on big data analysis - Google Patents
Commercial vehicle monitoring system based on big data analysis Download PDFInfo
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- G06V2201/08—Detecting or categorising vehicles
Abstract
The invention provides a commercial vehicle monitoring system based on big data analysis, which comprises: the system comprises a commercial vehicle monitoring server, a commercial vehicle monitoring server and a vehicle management server, wherein the commercial vehicle monitoring server is used for constructing a commercial vehicle assessment model according to enterprise vehicle operation data, monitoring the enterprise commercial vehicles by using the commercial vehicle assessment model, and sending abnormal alarm information to an enterprise management server corresponding to an abnormal vehicle when monitoring that the operation of the vehicle is abnormal, wherein the abnormal alarm information comprises vehicle information and abnormal data information; and the enterprise management server calls the operation data of the corresponding vehicle after receiving the abnormal alarm to generate an abnormal alarm report, and the system feeds back the violation data sharing to the operation unit by combining data sharing and data analysis so as to promote supervision, improve operation safety and guarantee safe traffic. The platform data is shared to related functional departments at the same time, and the platform data assists the functional departments to carry out vehicle supervision while providing sharing services, so that a new safety supervision environment is established together.
Description
Technical Field
The invention relates to the technical field of road transportation safety monitoring application, in particular to a commercial vehicle monitoring system based on big data analysis.
Background
The dynamic anomaly detection of key commercial vehicles is one of the research hotspots of the current anomaly detection technology based on traffic big data. The risk factor born by the illegal operation vehicle in operation is small, so that the safety of passengers cannot be guaranteed. As a result, many governments have begun to restrict illegal commercial vehicle service. However, since it is time-consuming and labor-consuming to obtain evidence of a large number of illegal operating vehicles, it is very difficult for related departments to obtain valid evidence under the condition of limited human resources. Data-driven approaches are used to solve this outlier detection problem instead of manually as sensing devices and location-aware technologies and the study of population movement. Human behavior models can be efficiently mined by their trajectories, and as such, large-scale vehicle movement data can be used to identify illegal operating vehicles. Furthermore, the large volume of vehicle and crowd movement data, including Call Detail Records (CDRs), positioning data, social network check-in data, etc., provides the possibility of extracting suspicious vehicles from normal vehicles. Recently, the related departments have used a new data, license plate recognition data (VLPR), as an emerging analysis tool. The new data comes from the vehicle passing record data of the automatic license plate recognition checkpoint. The automatic license plate recognition technology integrates advanced optical technology, image technology and pattern recognition technology, takes pictures of each passing vehicle, recognizes the license plate, brand, type, speed and other information related to the vehicle, and stores the information into a database.
However, these data are not effectively applied to the aspect of illegal commercial vehicle identification. Big data based identification of illegal operating vehicles faces the following challenges:
(1) the vehicle cardinality is large, and suspicious vehicles are difficult to find. Most illegal operation vehicles are provided by private vehicles, working hours are uncertain, and suspicious vehicles cannot be identified only from the types, speeds and short-term behavior characteristics of the vehicles.
(2) It is difficult to obtain evidence. When relevant departments find suspicious vehicles for inspection, effective evidences need to be collected, and because trapping modes cannot be adopted, some passengers carrying illegal operating vehicles do not actively cooperate with the relevant departments to bring great difficulty to evidence collection.
(3) The early warning rule set is difficult to determine how to accurately judge abnormal vehicles or illegal potential, and the current big data-based analysis technology has no universal algorithm and index set.
Disclosure of Invention
The invention aims to solve at least one technical problem in the background art, and the embodiment of the invention provides a commercial vehicle monitoring system based on big data analysis. The system comprises:
the system comprises a commercial vehicle monitoring server, a commercial vehicle monitoring server and a vehicle management server, wherein the commercial vehicle monitoring server is used for constructing a commercial vehicle assessment model according to enterprise vehicle operation data, monitoring the enterprise commercial vehicles by using the commercial vehicle assessment model, and sending abnormal alarm information to an enterprise management server corresponding to an abnormal vehicle when monitoring that the operation of the vehicle is abnormal, wherein the abnormal alarm information comprises vehicle information and abnormal data information;
and the enterprise management server calls the operation data of the corresponding vehicle to generate an abnormal alarm report after receiving the abnormal alarm.
Further optionally, the method further includes:
the vehicle movement data acquisition module comprises at least one license plate recognition device and is used for acquiring a vehicle movement track;
and/or the presence of a gas in the gas,
the passenger movement data acquisition module comprises at least one user terminal device and is used for acquiring passenger movement data.
Further optionally, the service vehicle monitoring server includes:
the index monitoring submodule is used for collecting index data of monitoring operation vehicles and operation units thereof;
the model establishing submodule is used for establishing a commercial vehicle assessment model according to the index data of the commercial vehicle and the commercial unit thereof;
the evaluation management submodule is used for evaluating the running index state of the commercial vehicle based on the commercial vehicle evaluation model, and informing the operating unit to process when the index is abnormal;
the information auditing submodule is used for receiving and auditing the operating vehicle processing information of the operating unit and establishing an emergency management system;
and the assessment and statistics submodule is used for assessing the operation units and assessing and counting the performance.
Further optionally, the service vehicle monitoring server further includes: and the service notification submodule is used for sending an item notification, a file forwarding or a service processing notification to the operation unit.
Further optionally, the service vehicle monitoring server further includes: and the assessment index query submodule is used for querying and monitoring the assessment index condition of the operating vehicle, wherein the query can be carried out according to the operating unit, the operating type, the index detail or the vehicle stop and report detail.
Further optionally, the model building submodule includes:
the evaluation index system building unit is used for studying and judging historical illegal data through the multidimensional neural network, extracting abnormal and illegal clusters, screening, sorting and classifying indexes, and preliminarily building a comprehensive evaluation index system;
and the comprehensive evaluation model construction unit is used for determining a weight coefficient by applying a fuzzy recognition principle and combining a multidimensional fuzzy analytic hierarchy process and a comprehensive integration weighting method based on big data, and is used for a comprehensive evaluation model of a road transport vehicle satellite positioning system service provider, a road transport enterprise and a driver.
Further optionally, the evaluation management sub-module counts the evaluation result of each individual index of the evaluation object in the operation process, calculates the corresponding weight of each individual evaluation index by using a principal component analysis method, obtains a safety evaluation result and a comprehensive evaluation result by using a weighted average method, and completes evaluation and recording of the evaluation object.
According to one scheme of the invention, the commercial vehicle is ensured to be brought into supervision through monitoring of the network access rate index, the supervision coverage rate is provided, the data of the vehicle brought into supervision are subjected to statistical analysis, the score ranking and the like are calculated through a reasonable calculation model, the accuracy and the reliability of the data collected by the system are ensured, and the driving safety of the commercial vehicle is improved. Through each index monitoring and through a correction algorithm, abnormal data caused by unstable signals are filtered, the monitored positioning data can truly reflect the running state of the vehicle, and reliable and accurate data support is provided for illegal evidence obtaining. On this basis, the platform uses the location data that has revised through big data analysis technique, reduces the index and judges the error, again according to index weight calculation method, calculates the concrete numerical value of every index, reduces and judges the error, and unified standard of judging reduces because of equipment model is different, the error that the signal strength inequality led to the fact, improves the accuracy, reliability, the stability of index, realizes unified standard road vehicle transportation control, reduces the potential safety hazard.
The illegal data sharing is fed back to the enterprise and the operator through the import assessment mechanism in combination with data sharing, data analysis and the like, and the enterprise and the operator are assessed simultaneously through establishing a corresponding assessment mechanism, so that supervision is promoted, operation safety is improved, and safe passing is guaranteed. The platform data is shared to related functional departments at the same time, and the platform data assists the functional departments to carry out vehicle supervision while providing sharing services, so that a new safety supervision environment is established together.
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FIG. 1 is a functional block diagram of a commercial vehicle monitoring system based on big data analysis according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of another operating vehicle monitoring system based on big data analysis according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating another operating vehicle monitoring method based on big data analysis according to an embodiment of the present invention;
fig. 4 is a flowchart schematically showing a process that a competent department dispatches a bill to notify an enterprise or an operator through a system service notification module to perform the processing in the embodiment of the present invention.
Detailed Description
The content of the invention will now be discussed with reference to exemplary embodiments. It is to be understood that the embodiments discussed are merely intended to enable one of ordinary skill in the art to better understand and thus implement the teachings of the present invention, and do not imply any limitations on the scope of the invention.
As used herein, the term "include" and its variants are to be read as open-ended terms meaning "including, but not limited to. The term "based on" is to be read as "based, at least in part, on". The terms "one embodiment" and "an embodiment" are to be read as "at least one embodiment".
As shown in fig. 1, the service vehicle monitoring system based on big data analysis according to the present invention includes:
the commercial vehicle monitoring server 1 is used for constructing a commercial vehicle assessment model according to enterprise vehicle operation data, monitoring the enterprise commercial vehicles by using the commercial vehicle assessment model, and sending abnormal alarm information to an enterprise management server corresponding to an abnormal vehicle when monitoring that the operation of the vehicle is abnormal, wherein the abnormal alarm information comprises vehicle information and abnormal data information;
and the at least one enterprise management server 2 is used for calling the operation data of the corresponding vehicle after receiving the abnormal alarm to generate an abnormal alarm report.
As a refinement, as shown in fig. 2, the above embodiment may also include, but is not limited to, the following implementation, and the following implementation is not necessary:
further optionally, the commercial vehicle monitoring system based on big data analysis may further include, but is not limited to, the following:
the vehicle movement data acquisition module 3 comprises at least one license plate recognition device and is used for acquiring a vehicle movement track;
and/or the presence of a gas in the gas,
the passenger movement data acquisition module 4 comprises at least one user terminal device for acquiring passenger movement data.
Further optionally, the service vehicle monitoring server 1 includes:
the index monitoring submodule 11 is used for collecting and monitoring index data of the operating vehicles and the operating units thereof;
the model establishing submodule 12 is used for establishing a commercial vehicle assessment model according to the index data of the commercial vehicle and the commercial unit thereof;
the evaluation management submodule 13 is used for evaluating the running index state of the commercial vehicle based on the commercial vehicle evaluation model, and informing the operating unit to process when the index is abnormal;
the information auditing submodule 14 is used for receiving and auditing the operating vehicle processing information of the operating unit and establishing an emergency management system;
and the assessment statistics submodule 15 is used for assessing the operation units and assessing and counting the performance.
Further optionally, the service vehicle monitoring server 1 further includes: and the service notification submodule 16 is configured to send an item notification, a file forwarding notification, or a service processing notification to the operation unit.
Further optionally, the service vehicle monitoring server 1 further includes: and the assessment index query submodule 17 is used for querying and monitoring the assessment index condition of the operating vehicle, wherein the query can be carried out according to an operating unit, an operating type, an index detail or a vehicle stop and report detail.
Further optionally, the model building submodule 12 includes:
an evaluation index system construction unit 121, configured to study and judge historical illegal data through a multidimensional neural network, extract abnormal and illegal clusters, screen, sort, and classify indexes, and initially construct a comprehensive evaluation index system;
the comprehensive evaluation model building unit 122 determines a weight coefficient by applying a fuzzy recognition principle and combining a multidimensional fuzzy analytic hierarchy process and a comprehensive integration weighting method based on big data, and is used for a comprehensive evaluation model of road transport vehicle satellite positioning system service providers, road transport enterprises and drivers.
Further optionally, the evaluation management sub-module 13 counts the evaluation result of each individual index of the evaluation object in the operation process, calculates the corresponding weight of each individual evaluation index by using a principal component analysis method, obtains a safety evaluation result and a comprehensive evaluation result by using a weighted average method, and completes evaluation and recording of the evaluation object.
The operating vehicle monitoring system based on big data analysis provided by the embodiment of the invention combines data sharing and data analysis to feed back violation data sharing to an operating unit, so as to promote supervision, improve operating safety and guarantee safe traffic. The platform data is shared to related functional departments at the same time, and the platform data assists the functional departments to carry out vehicle supervision while providing sharing services, so that a new safety supervision environment is established together.
For the convenience of the reader to understand, the following describes in detail the specific workflow of the above-mentioned commercial vehicle monitoring system based on big data analysis by way of example, as shown in fig. 3, which specifically includes the following steps and implementation details:
a. collecting index data of monitoring commercial vehicles and operating units thereof;
b. evaluating the running index state of the commercial vehicle, and informing the operating unit to process when the index is abnormal;
c. receiving and auditing the operating vehicle processing information of the operating unit, and establishing an emergency management system;
d. and (5) assessing the operation units, and evaluating and counting the performance.
According to an embodiment of the present invention, in the step a, the index data of the commercial vehicle includes a network access rate, an online rate, a track integrity rate, a data qualification rate, a platform connectivity rate, a vehicle violation rate, and the like of the commercial vehicle. The vehicle illegal violation comprises the vehicle violation frequency, the night prohibition frequency, the fatigue driving time, the line alarm frequency and the like. The operation unit index data includes various data information such as the name of the district, the operator or the enterprise, the type and the number of the vehicles operated and managed by the operation unit index data.
According to an embodiment of the present invention, in the steps b and c, when the vehicle assessment index is abnormal, the operation unit is notified to process as soon as possible, and after the operation unit processes the feedback, the supervision department reviews the provision information, so that the provision and feedback can be performed in advance to establish an emergency archive management system.
According to an embodiment of the present invention, before the step d is implemented, the method further includes querying an evaluation index of the monitoring operation vehicle, wherein the querying may be performed according to an operation unit, an operation type, an index detail or a vehicle stop and report detail. The specific query indexes comprise:
and (3) overall index statistics: and in the query time period, the index conditions of the overall operation vehicles comprise online rate statistics, network access rate statistics, track integrity statistics, data qualification rate statistics, overspeed times statistics, fatigue driving statistics, night operation statistics and line deviation statistics. This portion may be queried by operation type.
And (3) counting the indexes of the operators: and in the query time period, the index conditions of the vehicles operated by each operator comprise online rate statistics, track integrity statistics, data qualification rate statistics, positioning drift rate statistics and platform communication rate statistics. This section may be queried by operator name and operation type.
Enterprise index statistics: and in the query time period, the index conditions of the enterprise operating vehicles comprise online rate statistics, network access rate statistics, track integrity statistics, data qualification rate statistics, positioning drift rate statistics, overspeed times statistics, fatigue driving statistics, night operation statistics and line deviation statistics. This portion may be queried by business name and operation type.
And (3) specification of indexes: and inquiring the detailed conditions of the operation examination indexes, including offline vehicle details, track integrity details, data qualification rate details, positioning drift rate details and non-network-connected vehicle details. And data screening can be performed according to the jurisdiction, the operator, the enterprise name, the vehicle type, the integrity value and the qualification value.
And (5) vehicle stop report detail: for querying the details of the vehicles that have reported parking on the platform.
According to an embodiment of the present invention, in the step d, the assessment operator includes jurisdiction assessment, operator assessment and enterprise assessment. In the embodiment, the assessment result is generated based on the index detail information and by combining the assessment index score algorithm. The assessment results are divided into three dimensions of jurisdiction assessment, operator assessment and enterprise assessment, and the assessment results are respectively used for scoring assessment and ranking assessment on different main bodies.
For example, in the examination of the jurisdiction, the base score is 20, the online rate is 5, the network access rate is 5, the track integrity rate is 20, the data qualification rate is 10, the platform connectivity rate is 10, and the vehicle violation rate is 30.
In the assessment of operators, the base score is 50, the online rate is 5, the satellite positioning drift rate is 10, the track integrity rate is 20, the data qualification rate is 5, the platform connectivity rate is 5 and the platform post-checking response rate is 5.
In the enterprise assessment, the basic score is 20, the online rate is 3, the network access rate is 2, the track integrity rate is 10, the data qualification rate is 5, the satellite positioning drift rate is 5, the post-checking response rate is 3, the average overspeed frequency is 16, the average fatigue driving time is 16, and the vehicle illegal and illegal rate is 20.
According to the method, the comprehensive assessment index is researched and established and the assessment items and indexes are refined from the aspects of vehicle data assessment, equipment maintenance management, vehicle violation alarm, ADAS alarm, DMS alarm, field data, non-technical assessment index inspection, personnel allocation and training conditions, platform operation conditions, major accidents and the like by means of big data analysis.
In addition, according to the method, the historical illegal data are studied and judged through the multidimensional neural network, abnormal and illegal clusters are extracted, indexes are screened, sorted and classified, and a comprehensive evaluation index system is preliminarily constructed; determining a weight coefficient by applying a fuzzy recognition principle and combining a multidimensional fuzzy analytic hierarchy process and a comprehensive integration weighting method based on big data, wherein the weight coefficient is used for a comprehensive evaluation model of a road transport vehicle satellite positioning system service provider, a road transport enterprise and a driver; and (4) counting the scoring results of each single index of the evaluation object in the operation process, calculating by using a principal component analysis method to obtain the corresponding weight of each single evaluation index, obtaining a safety evaluation result and a comprehensive evaluation result by using a weighted average method, and finishing the evaluation and recording of the evaluation object.
According to the system setting of the invention, actually, the index monitoring submodule inquires the overall index condition of the local city in real time, including the network access rate, the online rate, the track integrity rate, the data qualification rate, the platform communication rate, the vehicle illegal violation rate and the operator index condition of the vehicle. The illegal vehicle violation comprises overspeed violation times, night prohibition times, fatigue driving time and line alarm times; and the alarm processing condition after the violation is shown.
When the vehicle assessment indexes are abnormal, the assessment management submodule automatically informs operators and enterprises in the jurisdiction to supervise the operators and the enterprises to process as soon as possible, and the operators and the enterprises can log in through a relevant interface of a system platform to inquire and process feedback. Specifically, when the vehicle generates abnormal assessment indexes, the assessment management module automatically dispatches the assessment indexes to an operator or a transportation enterprise, the platform is used for reminding and urging the operator or the transportation enterprise to process the assessment indexes in time, and the operator and the enterprise can log in through a relevant platform interface to inquire and process feedback.
Further, when the supervision department needs to perform event notification or business processing on the operators and the transportation enterprises, the operators and the transportation enterprises can be manually dispatched to perform processing through the business notification submodule. Specifically, when a supervision department needs to perform item notification, file forwarding and service processing on an operator and a transportation enterprise, the supervision department can manually dispatch a list through a service notification module to notify the operator and the transportation enterprise to perform service processing, and a platform is used for reminding to perform reminding. And displaying the quantity of the current unprocessed dispatching information on a page menu bar by using an icon, updating in real time, and reminding when a new alarm exists. Fig. 4 schematically shows a flowchart of processing performed by a competent department by dispatching a bill to notify an enterprise or an operator through a system service notification module, and as shown in fig. 4, the method further includes a process of feeding back the audit of the competent department after the processing by the enterprise or the operator.
In the auditing process, after the information auditing module reports the information through the report operator client, the administrative department reviews the reported information, so that the report can be reported in advance and fed back in time to establish an emergency archive management system.
Further, the assessment index query submodule in the system is used for querying each assessment index condition of the supervision vehicle before assessing the assessment performance, and the specific query indexes comprise:
and (3) overall index statistics: and in the query time period, the index conditions of the overall operation vehicles comprise online rate statistics, network access rate statistics, track integrity statistics, data qualification rate statistics, overspeed times statistics, fatigue driving statistics, night operation statistics and line deviation statistics. This portion may be queried by operation type.
And (3) counting the indexes of the operators: and in the query time period, the index conditions of the vehicles operated by each operator comprise online rate statistics, track integrity statistics, data qualification rate statistics, positioning drift rate statistics and platform communication rate statistics. This section may be queried by operator name and operation type.
Enterprise index statistics: and in the query time period, the index conditions of the enterprise operating vehicles comprise online rate statistics, network access rate statistics, track integrity statistics, data qualification rate statistics, positioning drift rate statistics, overspeed times statistics, fatigue driving statistics, night operation statistics and line deviation statistics. This portion may be queried by business name and operation type.
And (3) specification of indexes: and inquiring the detailed conditions of the operation examination indexes, including offline vehicle details, track integrity details, data qualification rate details, positioning drift rate details and non-network-connected vehicle details. And data screening can be performed according to the jurisdiction, the operator, the enterprise name, the vehicle type, the integrity value and the qualification value.
And (5) vehicle stop report detail: for querying the details of the vehicles that have reported parking on the platform.
And finally, the assessment statistical module generates assessment results based on the index detail information and in combination with an assessment index score algorithm. The assessment results are divided into three dimensions of jurisdiction assessment, operator assessment and enterprise assessment, and the assessment results are respectively used for scoring assessment and ranking assessment on different main bodies.
For example, in the examination of the jurisdiction, the base score is 20, the online rate is 5, the network access rate is 5, the track integrity rate is 20, the data qualification rate is 10, the platform connectivity rate is 10, and the vehicle violation rate is 30.
In the assessment of operators, the base score is 50, the online rate is 5, the satellite positioning drift rate is 10, the track integrity rate is 20, the data qualification rate is 5, the platform connectivity rate is 5 and the platform post-checking response rate is 5.
In the enterprise assessment, the basic score is 20, the online rate is 3, the network access rate is 2, the track integrity rate is 10, the data qualification rate is 5, the satellite positioning drift rate is 5, the post-checking response rate is 3, the average overspeed frequency is 16, the average fatigue driving time is 16, and the vehicle illegal and illegal rate is 20.
As described above, in the conventional vehicle monitoring, the number of vehicles is large, and it is difficult to detect a suspicious vehicle. Most illegal operation vehicles are provided by private vehicles, working hours are uncertain, and suspicious vehicles cannot be identified only from the types, speeds and short-term behavior characteristics of the vehicles. In the past, when relevant departments find suspicious vehicles for inspection, effective evidences need to be collected, and the collection of the evidences is very difficult because a trapping mode cannot be adopted.
Aiming at the traditional technology, the invention ensures that the commercial vehicles are brought into supervision through monitoring the network access rate index, provides the supervision coverage rate, carries out statistical analysis on the vehicle data brought into supervision, calculates the score ranking and the like through a reasonable calculation model, ensures the accuracy and reliability of the data collected by the system, and improves the driving safety of the commercial vehicles. Through each index monitoring and through a correction algorithm, abnormal data caused by unstable signals are filtered, the monitored positioning data can truly reflect the running state of the vehicle, and reliable and accurate data support is provided for illegal evidence obtaining. On this basis, the platform uses the location data that has revised through big data analysis technique, reduces the index and judges the error, again according to index weight calculation method, calculates the concrete numerical value of every index, reduces and judges the error, and unified standard of judging reduces because of equipment model is different, the error that the signal strength inequality led to the fact, improves the accuracy, reliability, the stability of index, realizes unified standard road vehicle transportation control, reduces the potential safety hazard.
According to the invention, by using Beidou/GPS, Internet of things, cloud computing, big data, artificial intelligence and a new generation information technology, multi-source data such as Beidou positioning, video images, ADAS/DSM and meteorology are integrated, functions such as dynamic monitoring, situation assessment, safety early warning, emergency disposal, statistical analysis and comprehensive evaluation are realized, closed-loop management of dynamic supervision of key operation vehicles is formed, potential safety hazards of road transportation are reduced, and the industrial informatization service level is improved.
The illegal data are shared and fed back to the enterprise and the operator through the check mechanism imported into the provincial hall, the data sharing, the data analysis and the like are combined, and the enterprise and the operator are simultaneously checked through establishing the corresponding check mechanism, so that the supervision is promoted, the operation safety is improved, and the safe passage is guaranteed. The platform data is shared to related functional departments at the same time, and the platform data assists the functional departments to carry out vehicle supervision while providing sharing services, so that a new safety supervision environment is established together.
Those of ordinary skill in the art will appreciate that the modules and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and devices may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and in actual implementation, there may be other divisions, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not implemented. 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 modules, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, each functional module in the embodiments of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method for transmitting/receiving the power saving signal according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
It should be understood that the order of execution of the steps in the summary of the invention and the embodiments of the present invention does not absolutely imply any order of execution, and the order of execution of the steps should be determined by their functions and inherent logic, and should not be construed as limiting the process of the embodiments of the present invention.
Claims (7)
1. A service vehicle monitoring system based on big data analysis, comprising:
the system comprises a commercial vehicle monitoring server, a commercial vehicle monitoring server and a vehicle management server, wherein the commercial vehicle monitoring server is used for constructing a commercial vehicle assessment model according to enterprise vehicle operation data, monitoring the enterprise commercial vehicles by using the commercial vehicle assessment model, and sending abnormal alarm information to an enterprise management server corresponding to an abnormal vehicle when monitoring that the operation of the vehicle is abnormal, wherein the abnormal alarm information comprises vehicle information and abnormal data information;
and the enterprise management server calls the operation data of the corresponding vehicle to generate an abnormal alarm report after receiving the abnormal alarm.
2. The big data analysis-based commercial vehicle monitoring system according to claim 1, further comprising:
the vehicle movement data acquisition module comprises at least one license plate recognition device and is used for acquiring a vehicle movement track;
and/or the presence of a gas in the gas,
the passenger movement data acquisition module comprises at least one user terminal device and is used for acquiring passenger movement data.
3. The big data analysis based commercial vehicle monitoring system according to claim 2, wherein the commercial vehicle monitoring server comprises:
the index monitoring submodule is used for collecting index data of monitoring operation vehicles and operation units thereof;
the model establishing submodule is used for establishing a commercial vehicle assessment model according to the index data of the commercial vehicle and the commercial unit thereof;
the evaluation management submodule is used for evaluating the running index state of the commercial vehicle based on the commercial vehicle evaluation model, and informing the operating unit to process when the index is abnormal;
the information auditing submodule is used for receiving and auditing the operating vehicle processing information of the operating unit and establishing an emergency management system;
and the assessment and statistics submodule is used for assessing the operation units and assessing and counting the performance.
4. The big data analysis based commercial vehicle monitoring system of claim 3, wherein the commercial vehicle monitoring server further comprises: and the service notification submodule is used for sending an item notification, a file forwarding or a service processing notification to the operation unit.
5. The big data analysis based commercial vehicle monitoring system of claim 4, wherein the commercial vehicle monitoring server further comprises: and the assessment index query submodule is used for querying and monitoring the assessment index condition of the operating vehicle, wherein the query can be carried out according to the operating unit, the operating type, the index detail or the vehicle stop and report detail.
6. The big data analysis based commercial vehicle monitoring system of claim 3, wherein the model building submodule comprises:
the evaluation index system building unit is used for studying and judging historical illegal data through the multidimensional neural network, extracting abnormal and illegal clusters, screening, sorting and classifying indexes, and preliminarily building a comprehensive evaluation index system;
and the comprehensive evaluation model construction unit is used for determining a weight coefficient by applying a fuzzy recognition principle and combining a multidimensional fuzzy analytic hierarchy process and a comprehensive integration weighting method based on big data, and is used for a comprehensive evaluation model of a road transport vehicle satellite positioning system service provider, a road transport enterprise and a driver.
7. The operating vehicle monitoring system based on big data analysis of claim 6, wherein the evaluation management sub-module counts the evaluation result of each individual evaluation index of the evaluation object in the operating process, calculates the corresponding weight of each individual evaluation index by using a principal component analysis method, obtains the safety evaluation result and the comprehensive evaluation result by using a weighted average method, and completes the evaluation and recording of the evaluation object.
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