CN117198055A - Highway electromechanical control method and system based on big data cloud service - Google Patents
Highway electromechanical control method and system based on big data cloud service Download PDFInfo
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Abstract
The invention relates to a highway electromechanical control method and system based on big data cloud service, wherein the method comprises the following steps: acquiring position data of each electromechanical device in a designated range on a highway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; acquiring traffic flow data in a preset time period in a specified range on a highway from a big data cloud service platform, acquiring a daily average value of traffic flow according to the traffic flow data in the preset time period, and comparing the daily average value with a traffic flow early warning threshold range to obtain a second comparison result; constructing input data based on data acquired by a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data; and inputting the input data into a pre-trained machine learning model to obtain the reliability score of the electromechanical equipment in the appointed range on the expressway.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a highway electromechanical control method and system based on big data cloud service.
Background
In conventional highway electromechanical control systems, reliability assessment of electromechanical devices is often based on manual inspection or maintenance planning at a fixed frequency, which may result in inaccurate or timely assessment. In addition, real-time traffic flow, traffic conditions, and other related environmental factors of the highway often affect the operating state and life of the electromechanical device, but such data is often underutilized.
Disclosure of Invention
In view of the defects and shortcomings of the prior art, the invention provides a highway electromechanical control method and system based on big data cloud service, which solve the technical problems that the existing highway electromechanical equipment is often checked and maintained manually and is not accurate and timely enough.
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
in a first aspect, an embodiment of the present invention provides a highway electromechanical control method based on big data cloud service, including:
s1, acquiring position data of each electromechanical device in a designated range on a highway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring sensor, the electronic toll collection device, the intelligent street lamp, the electronic speed limit marking equipment and the traffic monitoring camera;
s2, acquiring traffic flow data in a preset time period in a specified range on the expressway from a big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result;
the big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway is uploaded to a big data cloud service platform in real time;
s3, constructing input data based on data acquired by a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data;
s4, inputting the input data into a pre-trained machine learning model to obtain reliability scores of electromechanical equipment in a specified range on the expressway;
training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one.
Preferably, the method further comprises, after S1:
if the first comparison result is that the position data of the electromechanical equipment is different from the preset specific position of the electromechanical equipment, a first prompt message is sent;
the first prompt information is that the position data of the electromechanical device is different from a preset specific position of the electromechanical device.
Preferably, the S2 specifically includes:
s21, acquiring vehicle flow data in a preset time period in a specified range on a highway from a big data cloud service platform;
the preset time period is the month before the current time;
s22, processing the traffic flow data in a preset time period in a specified range on the expressway by adopting a data analysis tool to obtain a daily average value of the traffic flow in the preset time period;
the data analysis tool is a Hadoop tool or a Spark tool;
s23, comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result.
Preferably, the method further comprises, after S2:
and if the second comparison result is that the daily average value is not within the preset traffic flow early warning threshold range, the big data cloud service platform sends out alarm information.
Preferably, the method further comprises, after S2:
based on the daily average value of the traffic flow and the environmental factor data, acquiring the workload of any electromechanical device by adopting a formula (1), judging whether the workload of the electromechanical device exceeds a preset threshold value, and sending out alarm information if the workload exceeds the preset threshold value;
the environmental factor data includes daily: average temperature, minimum temperature, maximum temperature, precipitation, and wind speed;
workload of the electromechanical device = β0+β1 daily average value of vehicle flow + β2 daily average temperature + β3 daily precipitation + epsilon;
wherein β0 is a preset first value; β1 is a predetermined second value; β2 is a preset third value; β3 is a fourth value set in advance; a fifth value pre-set by epsilon.
Preferably, the step S3 specifically includes:
s31, acquiring traffic delay based on data acquired by a traffic monitoring camera in the electromechanical equipment;
the data collected by the traffic monitoring camera in the electromechanical equipment is traffic video;
s32, the number of vehicles passing through the toll station every day is acquired based on a license plate recognition system in the electromechanical equipment;
s33, the traffic delay, the number of vehicles passing through the toll station every day, the daily average value of the traffic flow and/or the environmental factor data are formed into input data.
Preferably, the machine learning model is a predictive model of a deep neural network.
On the other hand, the embodiment also provides a highway electromechanical control system based on big data cloud service, which comprises:
the position identification module is used for acquiring the position data of each electromechanical device in a designated range on the expressway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring sensor, the electronic toll collection device, the intelligent street lamp, the electronic speed limit marking equipment and the traffic monitoring camera;
the data analysis module is used for acquiring traffic flow data in a preset time period in a specified range on the expressway from the big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result;
the big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway is uploaded to a big data cloud service platform in real time;
the input set construction module is used for constructing input data based on data acquired by the traffic monitoring camera and the license plate recognition system in the electromechanical equipment, a daily average value of the traffic flow and/or environmental factor data;
the machine learning module is used for inputting the input data into a pre-trained machine learning model to obtain the reliability score of the electromechanical equipment in the appointed range on the expressway;
training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one.
Preferably, the method comprises the steps of,
the position identification module further comprises a geographic information system interface, wherein the geographic information system interface is used for importing and updating a preset geographic position database;
the data analysis module interfaces with a plurality of different big data cloud service providers and is used for acquiring traffic flow data provided by the different big data cloud service providers.
Preferably, the method comprises the steps of,
the input set construction module is also used for screening and cleaning data acquired based on a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data before constructing input data;
the machine learning model is a prediction model of a deep neural network;
the prediction model of the deep neural network of the machine learning module can adjust and optimize model parameters in real time in an online learning mode.
The beneficial effects of the invention are as follows: according to the highway electromechanical control method and system based on the big data cloud service, various data are collected and analyzed in real time, so that the working state and the abrasion degree of electromechanical equipment on a highway can be known in time. And judging whether the workload of the electromechanical equipment exceeds a preset threshold value, and if so, sending out alarm information, thereby reducing sudden maintenance caused by the failure of the electromechanical equipment and ensuring the continuity and the planeness of maintenance work.
According to the highway electromechanical control method and system based on the big data cloud service, the equipment can be ensured to be always in the optimal working state through regular and predictive maintenance. The damage to the equipment caused by excessive wear or sudden faults is avoided, so that the overall service life of the equipment is prolonged, and the overall possession cost of the equipment is reduced.
According to the road electromechanical control method and system based on the big data cloud service, through real-time monitoring and analysis of traffic flow, traffic delay and toll station data, a road operator can more flexibly adjust traffic flow direction and strategy, so that traffic jam is reduced, and the overall operation efficiency of a road is improved.
According to the road electromechanical control method and system based on the big data cloud service, predictive maintenance is adopted, so that resources can be more accurately distributed for maintenance work, unnecessary maintenance is avoided, and the cost of excessive maintenance or excessive purchasing is reduced.
Drawings
FIG. 1 is a flow chart of a highway electromechanical control method based on big data cloud service;
FIG. 2 is a schematic diagram of a highway electromechanical control architecture based on big data cloud service according to the present invention;
FIG. 3 is a schematic diagram of a position recognition module according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a data analysis module according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an input set construction module according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a machine learning module according to an embodiment of the invention.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
In order that the above-described aspects may be better understood, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1, the present embodiment provides a highway electromechanical control method based on big data cloud service, including:
s1, acquiring position data of each electromechanical device in a designated range on a highway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring system comprises a traffic flow monitoring sensor, an electronic toll collection device, an intelligent street lamp, electronic speed limit marking equipment and a traffic monitoring camera.
Each electromechanical device on the road is located using a high-precision Global Positioning System (GPS) or geomagnetic sensor. These data may be automatically uploaded every few minutes to ensure real-time of the location data. The electromechanical device in this embodiment may further include: an electronic toll collection system: devices in the electronic toll station are turned on when a vehicle passes through, for identifying the vehicle, collecting road tolls, and recording traffic information. Intelligent street lamp: some cities employ intelligent street light systems that automatically increase the brightness as the vehicle approaches to provide better illumination. Electronic speed limit flag: some areas use electronic speed limit signs that automatically adjust the speed limit and display it to the driver when needed. Tunnel ventilation and safety system: in road tunnels, ventilation systems and emergency safety devices are turned on as needed as vehicles pass through to ensure safety and ventilation within the tunnel. Dynamic information display screen: these displays may provide real-time information such as traffic congestion, accident warnings, etc. based on traffic conditions and events.
The method further comprises, after S1:
and if the first comparison result is that the position data of the electromechanical equipment is different from the preset specific position of the electromechanical equipment, a first prompt message is sent out.
The first prompt information is that the position data of the electromechanical device is different from a preset specific position of the electromechanical device.
S2, acquiring traffic flow data in a preset time period in a specified range on the expressway from a big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result.
The big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; and uploading the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway to a big data cloud service platform in real time.
The step S2 specifically comprises the following steps:
s21, acquiring vehicle flow data in a preset time period in a specified range on the expressway from a big data cloud service platform.
The preset time period is the month before the current time.
S22, processing the traffic flow data in a preset time period in a specified range on the expressway by adopting a data analysis tool to obtain a daily average value of the traffic flow in the preset time period; the data can be adjusted by considering seasonal factors, holidays and other factors, so that the data is more representative.
The data analysis tool is a Hadoop tool or a Spark tool.
S23, comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result.
The method further comprises, after S2:
and if the second comparison result is that the daily average value is not within the preset traffic flow early warning threshold range, the big data cloud service platform sends out alarm information.
The method in this embodiment further includes, after S2:
based on the daily average value of the traffic flow and the environmental factor data, the workload of any electromechanical device is obtained by adopting the formula (1), whether the workload of the electromechanical device exceeds a preset threshold value is judged, and if the workload exceeds the preset threshold value, alarm information is sent.
The environmental factor data includes daily: average temperature, minimum temperature, maximum temperature, precipitation, and wind speed.
Workload of the electromechanical device = β0+β1 daily average value of vehicle flow + β2 daily average temperature + β3 daily precipitation + epsilon;
wherein β0 is a preset first value; β1 is a predetermined second value; β2 is a preset third value; β3 is a fourth value set in advance; a fifth value pre-set by epsilon.
The β0, β1, β2 and β3 of the method model in the embodiment are obtained by carrying out regression analysis on the daily average value of the historical traffic flow and the environmental factor data in advance, so that the best fit model is found, and the estimation of the workload of the electromechanical equipment is further realized. The workload of the electromechanical device can be accurately evaluated, excessive or insufficient maintenance is reduced, and the cost is reduced.
S3, constructing input data based on data acquired by the traffic monitoring cameras and the license plate recognition system in the electromechanical equipment, a daily average value of the traffic flow and/or environmental factor data.
In practical application of this embodiment, the step S3 specifically includes:
s31, acquiring traffic delay based on data acquired by a traffic monitoring camera in the electromechanical equipment; the data collected by the traffic monitoring camera in the electromechanical equipment is traffic video.
S32, the number of vehicles passing through the toll booth every day is acquired based on a license plate recognition system in the electromechanical equipment.
S33, the traffic delay, the number of vehicles passing through the toll station every day, the daily average value of the traffic flow and/or the environmental factor data are formed into input data.
S4, inputting the input data into a pre-trained machine learning model to obtain the reliability score of the electromechanical equipment in the appointed range on the expressway.
Training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one. The multi-dimensional input dataset can provide a more comprehensive evaluation perspective, not limited to a single vehicle flow data. The electromechanical device is scored using the trained model. A high score indicates that the device is in good condition and a low score indicates that the device may need maintenance or replacement. The machine learning algorithm can extract effective information from complex data and more accurately predict the reliability of the equipment, thereby providing more reliable data support for maintenance decisions.
In this embodiment, the machine learning model is a prediction model of a deep neural network.
According to the highway electromechanical control method based on the big data cloud service, various data are collected and analyzed in real time, so that the working state and the abrasion degree of electromechanical equipment on a highway can be known in time. And judging whether the workload of the electromechanical equipment exceeds a preset threshold value, and if so, sending out alarm information, thereby reducing sudden maintenance caused by the failure of the electromechanical equipment and ensuring the continuity and the planeness of maintenance work.
According to the highway electromechanical control method based on the big data cloud service, equipment can be ensured to be always in an optimal working state through regular and predictive maintenance. The damage to the equipment caused by excessive wear or sudden faults is avoided, so that the overall service life of the equipment is prolonged, and the overall possession cost of the equipment is reduced. And through real-time monitoring and analysis of traffic flow, traffic delay and toll station data, the road operator can more flexibly adjust traffic flow direction and strategy, thereby reducing traffic jam and improving the overall operation efficiency of the road.
Referring to fig. 2, the present embodiment provides a highway electromechanical control system based on big data cloud service, including:
the position identification module is used for acquiring the position data of each electromechanical device in a designated range on the expressway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring system comprises a traffic flow monitoring sensor, an electronic toll collection device, an intelligent street lamp, electronic speed limit marking equipment and a traffic monitoring camera.
The data analysis module is used for acquiring traffic flow data in a preset time period in a specified range on the expressway from the big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result.
The big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; and uploading the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway to a big data cloud service platform in real time.
The input set construction module is used for constructing input data based on data acquired by the traffic monitoring cameras and the license plate recognition system in the electromechanical equipment, a daily average value of the traffic flow and/or environmental factor data.
And the machine learning module is used for inputting the input data into a pre-trained machine learning model to obtain the reliability score of the electromechanical equipment in the appointed range on the expressway.
Training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one.
Referring to fig. 3, the identification module in this embodiment includes: GPS receiving module: the module can receive the signal of Global Positioning System (GPS) and analyze the longitude and latitude coordinates of the current position.
And the interaction module is used for interacting with a pre-established geographic position database and judging the real-time position of the highway by comparing the longitude and latitude coordinates of the current position with the position information in the database.
The position recognition module acquires longitude and latitude coordinates of the current position by acquiring and analyzing the GPS signals, and then matches the longitude and latitude coordinates with a pre-established geographic position database to determine the real-time position of the highway.
The position recognition module can accurately determine the position of the highway in real time, and provides accurate basic information for subsequent data acquisition and control operation.
In some embodiments, other positioning technologies, such as base station positioning, inertial navigation, etc., may be combined to improve positioning accuracy and reliability; in addition to using GPS for position identification, other wireless location technologies may be used, such as the Beidou navigation system, the GLONASS system, and the like.
The position identification module further comprises a geographic information system interface, wherein the geographic information system interface is used for importing and updating a preset geographic position database;
the data analysis module interfaces with a plurality of different big data cloud service providers and is used for acquiring traffic flow data provided by the different big data cloud service providers.
Referring to fig. 4, the data analysis module includes:
the cloud service connection module is used for connecting with the big data cloud service platform to acquire, analyze and calculate traffic flow data of the highway; the data analysis module is connected with the big data cloud service to acquire traffic flow data of the highway, and then performs data analysis and calculation to extract useful information; the data analysis module can extract meaningful information from a large amount of traffic flow data, and provide basis for subsequent decision and control, such as traffic management, congestion prediction and the like.
In some embodiments, more advanced data analysis techniques, such as machine learning, deep learning, etc., may be applied to mine deeper data correlation and predictive capabilities; in addition to using big data cloud services for data analysis, local data analysis tools and platforms, such as open source data analysis software, etc., may be used.
The input set construction module is also used for screening and cleaning data acquired based on a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data before constructing input data;
referring to fig. 5, the input set construction module in this embodiment includes: a multi-data source interface integration module: the module integrates interfaces of a plurality of data sources, can acquire data from different data sources, and constructs a multi-dimensional input data set; the input set construction module can acquire various data such as environmental data, road information, vehicle type information (recording vehicles of different types such as small vehicles, trucks, motorcycles and the like, and the like) from different data sources through interfaces connected with a plurality of data sources;
the input set construction module can integrate data from different data sources to form a more comprehensive and rich input data set, and provide more characteristic information for subsequent analysis and decision;
in some embodiments, more data source interfaces can be integrated according to actual requirements to acquire more types of data, so that the input dimension and the expression capability of the model are enhanced; in addition to using multiple data source interfaces for data acquisition and integration, conventional data integration methods such as ETL tools, custom data processing scripts, and the like may also be employed.
The machine learning model is a prediction model of a deep neural network;
referring to fig. 6, the prediction model of the deep neural network of the machine learning module in this embodiment can adjust and optimize model parameters in real time by an online learning manner.
The core of the machine learning module is a pre-trained deep neural network model, which is trained through large-scale electromechanical device data, and can evaluate and predict the reliability of the electromechanical device.
The machine learning module can rapidly and accurately evaluate the reliability of the electromechanical equipment through a prediction model of the deep neural network, discover potential faults and problems in advance, and reduce downtime and maintenance cost.
In some embodiments, other machine learning algorithms and models, such as support vector machines, random forests, etc., may be incorporated to improve predictive performance and accommodate different types of electromechanical devices; in addition to using deep neural network models for electromechanical device reliability assessment, conventional statistical methods and models, such as regression models, probability models, and the like, may be used.
In the scheme of the embodiment, the road electromechanical control system based on the big data cloud service realizes functions of acquiring and analyzing traffic flow data, evaluating reliability of electromechanical equipment and the like through integrating the position identification module, the data analysis module, the input set construction module and the machine learning module. By utilizing big data and machine learning technology, the system can provide more accurate and reliable data analysis and decision support, and improve the efficiency and reliability of road traffic management.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; may be a communication between two elements or an interaction between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature is "on" or "under" a second feature, which may be in direct contact with the first and second features, or in indirect contact with the first and second features via an intervening medium. Moreover, a first feature "above," "over" and "on" a second feature may be a first feature directly above or obliquely above the second feature, or simply indicate that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is level lower than the second feature.
In the description of the present specification, the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., refer to particular features, structures, materials, or characteristics described in connection with the embodiment or example as being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that alterations, modifications, substitutions and variations may be made in the above embodiments by those skilled in the art within the scope of the invention.
Claims (10)
1. The highway electromechanical control method based on the big data cloud service is characterized by comprising the following steps of:
s1, acquiring position data of each electromechanical device in a designated range on a highway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring sensor, the electronic toll collection device, the intelligent street lamp, the electronic speed limit marking equipment and the traffic monitoring camera;
s2, acquiring traffic flow data in a preset time period in a specified range on the expressway from a big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result;
the big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway is uploaded to a big data cloud service platform in real time;
s3, constructing input data based on data acquired by a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data;
s4, inputting the input data into a pre-trained machine learning model to obtain reliability scores of electromechanical equipment in a specified range on the expressway;
training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one.
2. The highway electromechanical control method based on big data cloud service according to claim 1, wherein the method further comprises, after S1:
if the first comparison result is that the position data of the electromechanical equipment is different from the preset specific position of the electromechanical equipment, a first prompt message is sent;
the first prompt information is that the position data of the electromechanical device is different from a preset specific position of the electromechanical device.
3. The highway electromechanical control method based on the big data cloud service according to claim 1, wherein the step S2 specifically comprises:
s21, acquiring vehicle flow data in a preset time period in a specified range on a highway from a big data cloud service platform;
the preset time period is the month before the current time;
s22, processing the traffic flow data in a preset time period in a specified range on the expressway by adopting a data analysis tool to obtain a daily average value of the traffic flow in the preset time period;
the data analysis tool is a Hadoop tool or a Spark tool;
s23, comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result.
4. A highway mechatronic control method based on big data cloud service according to claim 3, characterized by the fact that it also comprises, after S2:
and if the second comparison result is that the daily average value is not within the preset traffic flow early warning threshold range, the big data cloud service platform sends out alarm information.
5. A highway mechatronic control method based on big data cloud service according to claim 3, characterized by the fact that it also comprises, after S2:
based on the daily average value of the traffic flow and the environmental factor data, acquiring the workload of any electromechanical device by adopting a formula (1), judging whether the workload of the electromechanical device exceeds a preset threshold value, and sending out alarm information if the workload exceeds the preset threshold value;
the environmental factor data includes daily: average temperature, minimum temperature, maximum temperature, precipitation, and wind speed;
workload of the electromechanical device = β0+β1 daily average value of vehicle flow + β2 daily average temperature + β3 daily precipitation + epsilon;
wherein β0 is a preset first value; β1 is a predetermined second value; β2 is a preset third value; β3 is a fourth value set in advance; a fifth value pre-set by epsilon.
6. The highway electromechanical control method based on the big data cloud service according to claim 5, wherein the step S3 specifically comprises:
s31, acquiring traffic delay based on data acquired by a traffic monitoring camera in the electromechanical equipment;
the data collected by the traffic monitoring camera in the electromechanical equipment is traffic video;
s32, the number of vehicles passing through the toll station every day is acquired based on a license plate recognition system in the electromechanical equipment;
s33, the traffic delay, the number of vehicles passing through the toll station every day, the daily average value of the traffic flow and/or the environmental factor data are formed into input data.
7. The highway electromechanical control method based on big data cloud service according to claim 6, wherein the machine learning model is a predictive model of a deep neural network.
8. A highway electromechanical control system based on big data cloud service, characterized by comprising:
the position identification module is used for acquiring the position data of each electromechanical device in a designated range on the expressway, and comparing the position data of any electromechanical device with a preset specific position of the electromechanical device to obtain a first comparison result; the electromechanical device includes: the traffic flow monitoring sensor, the electronic toll collection device, the intelligent street lamp, the electronic speed limit marking equipment and the traffic monitoring camera;
the data analysis module is used for acquiring traffic flow data in a preset time period in a specified range on the expressway from the big data cloud service platform, acquiring a daily average value of traffic flow in the preset time period according to the traffic flow data in the preset time period, and comparing the daily average value with a preset traffic flow early warning threshold range to obtain a second comparison result;
the big data cloud service platform is respectively in communication connection with each electromechanical device in a designated range on the expressway; the traffic flow data acquired by the traffic flow monitoring sensor in the appointed range on the expressway is uploaded to a big data cloud service platform in real time;
the input set construction module is used for constructing input data based on data acquired by the traffic monitoring camera and the license plate recognition system in the electromechanical equipment, a daily average value of the traffic flow and/or environmental factor data;
the machine learning module is used for inputting the input data into a pre-trained machine learning model to obtain the reliability score of the electromechanical equipment in the appointed range on the expressway;
training the machine learning model by adopting a multi-dimensional input data set acquired in advance, and acquiring a trained machine learning model; the pre-acquired multi-dimensional input dataset comprises: a plurality of input data and a preset reliability score value corresponding to each input data one by one.
9. The highway machine-to-machine control system based on big data cloud service of claim 8, wherein,
the position identification module further comprises a geographic information system interface, wherein the geographic information system interface is used for importing and updating a preset geographic position database;
the data analysis module interfaces with a plurality of different big data cloud service providers and is used for acquiring traffic flow data provided by the different big data cloud service providers.
10. The highway machine-to-machine control system based on big data cloud service of claim 9, wherein,
the input set construction module is also used for screening and cleaning data acquired based on a traffic monitoring camera and a license plate recognition system in the electromechanical equipment, a daily average value of traffic flow and/or environmental factor data before constructing input data;
the machine learning model is a prediction model of a deep neural network;
the prediction model of the deep neural network of the machine learning module can adjust and optimize model parameters in real time in an online learning mode.
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