A method and system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles

Abstract

The present invention discloses a method and system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemical vehicles, the method comprising the following steps S1, synchronizing business data from a business database to a Redis database of a large database component at regular intervals; S2, subscribing to GPS positioning data and real-time checkpoint vehicle passing data of the vehicle; S3, subscribing to GPS positioning data and checkpoint vehicle passing data, and querying corresponding vehicle information, checkpoint information, pass information, route reporting information, and cargo status information from the Redis database according to key fields to perform data correlation comparison and aggregation. The advantages are: based on the reception, storage and calculation of massive real-time GPS positioning data and checkpoint vehicle passing data, vehicle unlicensed early warning, vehicle route deviation early warning, electronic fence deviation early warning, overtime parking early warning and GPS device abnormal early warning are realized, so that relevant personnel can timely know the status of hazardous chemical vehicles, and provide protection for the transportation safety of hazardous chemical vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and early warning of hazardous chemical vehicles, and in particular to a method and system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemical vehicles. Background Art

[0002] Dangerous chemicals are flammable and explosive. Once problems occur during storage and transportation, they may lead to serious accidents with serious consequences. Dangerous chemical transport vehicles parked at non-designated locations for a long time have very high safety risks. Once an accident occurs, the consequences are disastrous. If we only rely on road patrol police to detect illegally parked vehicles, there will be problems such as limited police force and untimely detection. The existing single service and relational database technology cannot meet the needs of large-scale data access, processing, and storage; it cannot achieve the expected application effect. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemical vehicles, thereby solving the above-mentioned problems existing in the prior art.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] A method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles comprises the following steps

[0006] S1. Synchronize business data from the business database to the Redis database of the big database component on a regular basis;

[0007] S2. Subscribe to the vehicle’s GPS positioning data and store it in Kafka by connecting to the vehicle’s GPS device; store the real-time checkpoint vehicle passing data pushed by the front-end sensing device management platform in Kafka;

[0008] S3, subscribes to the GPS positioning data and checkpoint passing data in Kafka in real time, and passes the subscribed data to the thread pool one by one; the data entering the thread pool queries the corresponding business data from the Redis database according to the key fields, performs correlation comparison and aggregation of the data, and stores the aggregated processed data in Kafka;

[0009] S4: Subscribe to the processed GPS positioning data and checkpoint vehicle data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle data and GPS positioning data entering the thread pool with the business data, associate the corresponding vehicles, and then issue checkpoint warnings and GPS warnings to the corresponding vehicles, and store the checkpoint warning data and GPS warning data in Kafka;

[0010] S5. Subscribe to the processed GPS positioning data, checkpoint vehicle passing data, checkpoint warning data and GPS warning data in Kafka in real time, store the GPS positioning data and checkpoint vehicle passing data in Hbase with the corresponding license plate number as the name; store the GPS warning data and checkpoint warning data in Solr using the thread pool.

[0011] Preferably, the business data includes vehicle information, checkpoint equipment information, electronic fence information, pass information, route reporting information, cargo status information, vehicle GPS track information and warning rules.

[0012] Preferably, the specific process of issuing checkpoint warning to the corresponding vehicle in step S4 is to subscribe to the checkpoint vehicle passing data processed in Kafka in real time, and pass the subscribed data into the thread pool one by one; compare the checkpoint vehicle passing data entering the thread pool with the vehicle GPS trajectory obtained from the Redis database to determine whether the vehicle's GPS trajectory is consistent with the corresponding checkpoint vehicle passing data; if not, give an abnormal warning of the vehicle's GPS device; if so, give a GPS warning to the corresponding vehicle.

[0013] Preferably, the specific process of performing GPS warning on the corresponding vehicle in step S4 is:

[0014] A1. Subscribe to the processed GPS positioning data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the GPS positioning data entering the thread pool with the pass information obtained from the Redis database, associate the cargo status of the corresponding vehicle, determine whether the corresponding vehicle has a pass, and give the results of the corresponding vehicle having obtained a pass, having been registered, not having obtained a pass, and not having been registered according to the warning rules; for vehicles that have not obtained a pass or have not been registered, give a warning of not having obtained a pass, and upload the warning data of not having obtained a pass to Kafka; for vehicles that have obtained a pass or have been registered, proceed to step A2;

[0015] A2. For vehicles that have obtained a license or have been registered, the GPS positioning data of the vehicle is compared with the route data of the vehicle obtained from the Redis database to determine whether the vehicle is moving within its route. If not, a route deviation warning is issued for the vehicle, and the route deviation warning data of the vehicle is uploaded to Kafka. If yes, proceed to step A3.

[0016] A3. Compare the GPS positioning data of the vehicle with the electronic fence information of the vehicle obtained from the Redis database; determine whether the vehicle is within the range of its electronic fence. If not, give the vehicle electronic fence deviation warning and upload the vehicle electronic fence deviation warning to Kafka. If not, proceed to step A4;

[0017] A4. Calculate whether the vehicle's speed is equal to 0 within a preset time period based on the vehicle's GPS trajectory. If so, give the vehicle an overtime parking warning and upload the overtime parking warning to Kafka; if not, it indicates that the vehicle is driving normally.

[0018] The present invention also aims to provide a system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles, the system being used to implement any of the above-mentioned methods, the system comprising:

[0019] Business data synchronization module: used to synchronize business data from the business database to the Redis database of the big database component at regular intervals;

[0020] Data access module: used to subscribe to the vehicle's GPS positioning data and store it in Kafka by connecting to the vehicle's GPS device; used to store the real-time checkpoint vehicle passing data pushed by the front-end perception device management platform into Kafka

[0021] Data processing module: used to subscribe to GPS positioning data and checkpoint vehicle passing data in Kafka in real time, and pass the subscribed data to the thread pool one by one; the data entering the thread pool queries the corresponding vehicle information, checkpoint information, pass information, route reporting information, and cargo status information from the Redis database according to the key fields, and performs data correlation comparison and aggregation, and stores the aggregated processed data in Kafka;

[0022] Warning calculation module: used to subscribe to the processed GPS positioning data and checkpoint vehicle data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle data and GPS positioning data entering the thread pool with the business data, associate the corresponding vehicles, and then issue checkpoint warnings and GPS warnings to the corresponding vehicles, and store the checkpoint warning data and GPS warning data in Kafka;

[0023] Data storage module; used to subscribe to the processed GPS positioning data, checkpoint vehicle passing data and checkpoint warning data in Kafka in real time and to process GPS warning data, and store the GPS positioning data and checkpoint vehicle passing data in Hbase with the corresponding license plate number as the name; store the GPS warning data and checkpoint warning data in Solr using the thread pool;

[0024] Data query module; used to query business data, checkpoint vehicle passing data, GPS positioning data, GPS warning data and checkpoint warning data.

[0025] The beneficial effects of the present invention are as follows: 1. Based on the vehicle's GPS positioning information, it is possible to detect in real time whether the vehicle has been illegally parked for a long time. The police can command the road police to accurately investigate and deal with illegally parked vehicles based on the overtime parking warning prompted by the system in the monitoring room, and eliminate safety hazards in a timely manner. 2. Based on the reception, storage and calculation of massive real-time GPS positioning data and checkpoint passing data, the vehicle unlicensed warning, vehicle route deviation warning, electronic fence deviation warning, overtime parking warning and GPS equipment abnormality warning are realized, so that relevant personnel can be informed of the status of hazardous chemical vehicles in a timely manner, and the transportation safety of hazardous chemical vehicles is guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a principle flow chart of the method in the embodiment of the present invention;

[0027] Figure 2 It is a flow chart of checkpoint warning and GPS warning in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0029] Embodiment 1

[0030] like Figure 1 As shown, in this embodiment, a method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles is provided, comprising the following steps

[0031] S1. Synchronize business data from the business database to the Redis database of the big database component on a regular basis;

[0032] The business data includes vehicle information, checkpoint equipment information, electronic fence information, pass information, route reporting information, cargo status information, vehicle GPS track information and warning rules.

[0033] The vehicle information includes license plate number, vehicle model, driver's name, contact information and other related information.

[0034] The business database is provided with a vehicle information table, a checkpoint equipment information table, an electronic fence information table, a pass information table, a route reporting information table, a cargo status information table, and a vehicle GPS track information table. The vehicle information, checkpoint equipment information, electronic fence information, pass information, route reporting information, cargo status information, and vehicle GPS track information of the corresponding vehicle are stored in the corresponding tables in order.

[0035] S2. Connect the vehicle's GPS device through the JTT808 standard distributed platform, subscribe to the vehicle's GPS positioning data and store it in Kafka; store the real-time checkpoint vehicle passing data pushed by the front-end perception device management platform in Kafka;

[0036] S3, based on the microservice architecture, implements distributed real-time subscription of GPS positioning data and checkpoint vehicle passing data in Kafka, and passes the subscribed data to the thread pool one by one; the data entering the thread pool queries the corresponding business information (vehicle information, checkpoint information, pass information, route reporting information, cargo status information, vehicle GPS track information) from the Redis database according to the key fields, performs data correlation comparison and aggregation, and stores the aggregated processed data in Kafka;

[0037] S4. Based on the microservice architecture, distributed real-time subscription is implemented for the processed GPS positioning data and checkpoint vehicle passing data in Kafka, and the subscribed data is passed to the thread pool one by one; the checkpoint vehicle passing data and GPS positioning data entering the thread pool are compared with the business data, and the corresponding vehicles are associated, and then the checkpoint warning and GPS warning are performed on the corresponding vehicles, and the checkpoint warning data and GPS warning data are stored in Kafka;

[0038] like Figure 2 As shown, the specific process of issuing checkpoint warning to the corresponding vehicle in step S4 is to subscribe to the checkpoint vehicle passing data processed in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle passing data entering the thread pool with the vehicle GPS trajectory obtained from the Redis database to determine whether the vehicle's GPS trajectory is consistent with the corresponding checkpoint vehicle passing data. If not, an abnormal warning of the vehicle's GPS device is given; if so, a GPS warning is issued to the corresponding vehicle.

[0039] The specific process of performing GPS warning on the corresponding vehicle in step S4 is as follows:

[0040] A1. Subscribe to the processed GPS positioning data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the GPS positioning data entering the thread pool with the pass information obtained from the Redis database, associate the cargo status of the corresponding vehicle, determine whether the corresponding vehicle has a pass, and give the results of the corresponding vehicle having obtained a pass, having been registered, not having obtained a pass, and not having been registered according to the warning rules; for vehicles that have not obtained a pass or have not been registered, give a warning of not having obtained a pass, and upload the warning data of not having obtained a pass to Kafka; for vehicles that have obtained a pass or have been registered, proceed to step A2;

[0041] A2. For vehicles that have obtained a license or have been registered, the GPS positioning data of the vehicle is compared with the route data of the vehicle obtained from the Redis database to determine whether the vehicle is moving within its route. If not, a route deviation warning is issued for the vehicle, and the route deviation warning data of the vehicle is uploaded to Kafka. If yes, proceed to step A3.

[0042] A3. Compare the GPS positioning data of the vehicle with the electronic fence information of the vehicle obtained from the Redis database; determine whether the vehicle is within the range of its electronic fence. If not, give the vehicle electronic fence deviation warning and upload the vehicle electronic fence deviation warning to Kafka. If not, proceed to step A4;

[0043] A4. Calculate whether the vehicle's speed is equal to 0 within a preset time period based on the vehicle's GPS trajectory. If so, give the vehicle an overtime parking warning and upload the overtime parking warning to Kafka; if not, it indicates that the vehicle is driving normally.

[0044] The preset time period can be set according to actual conditions to better meet actual needs, for example, it can be set to 5 minutes.

[0045] S5. Based on the microservice architecture, distributed real-time subscription is implemented for the processed GPS positioning data, checkpoint vehicle passing data, checkpoint warning data and GPS warning data in Kafka, and the GPS positioning data and checkpoint vehicle passing data are stored in Hbase with the corresponding license plate number as the name; GPS warning data and checkpoint warning data are stored in Solr using the thread pool.

[0046] Embodiment 2

[0047] In this embodiment, a system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles is provided. The system is used to implement a method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles. The system includes:

[0048] Business data synchronization module: used to synchronize business data from the business database to the Redis database of the big database component at regular intervals;

[0049] Data access module; used to connect the vehicle's GPS device through the JTT808 standard distributed platform, subscribe to the vehicle's GPS positioning data and store it in Kafka; used to store the real-time checkpoint vehicle passing data pushed by the front-end perception device management platform into Kafka

[0050] Data processing module: used to implement distributed real-time subscription of GPS positioning data and checkpoint vehicle passing data in Kafka based on microservice architecture, and pass the subscribed data to the thread pool one by one; the data entering the thread pool queries the corresponding vehicle information, checkpoint information, pass information, route reporting information, and cargo status information from the Redis database according to key fields, and performs data correlation comparison and aggregation, and stores the aggregated processed data in Kafka;

[0051] Warning calculation module: used to implement distributed real-time subscription of processed GPS positioning data and checkpoint vehicle data in Kafka based on microservice architecture, and pass the subscribed data into the thread pool one by one; compare the checkpoint vehicle data and GPS positioning data entering the thread pool with the business data, associate the corresponding vehicles, and then issue checkpoint warnings and GPS warnings to the corresponding vehicles, and store the checkpoint warning data and GPS warning data in Kafka;

[0052] Data storage module; used to implement distributed real-time subscription of processed GPS positioning data, checkpoint vehicle passing data and checkpoint warning data in Kafka based on microservice architecture, and store GPS positioning data and checkpoint vehicle passing data in Hbase with the corresponding license plate number as the name; store GPS warning data and checkpoint warning data in Solr using thread pool;

[0053] Data query module; used to query business data, checkpoint vehicle passing data, GPS positioning data, GPS warning data and checkpoint warning data based on the microservice architecture.

[0054] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0055] The present invention provides a method and system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemical vehicles. Based on the GPS positioning information of the vehicle, it is possible to detect in real time whether the vehicle has been illegally parked for a long time. The police can command the road police to accurately investigate and deal with illegally parked vehicles according to the overtime parking warning prompted by the system in the monitoring room, and eliminate safety hazards in time. Based on the reception, storage and calculation of massive real-time GPS positioning data and checkpoint passing data, the vehicle unlicensed warning, vehicle route deviation warning, electronic fence deviation warning, overtime parking warning and GPS equipment abnormality warning are realized, so that relevant personnel can know the status of hazardous chemical vehicles in time, and provide protection for the transportation safety of hazardous chemical vehicles.

[0056] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles, characterized by: The following steps are included S1. Synchronize business data from the business database to the Redis database of the big database component on a regular basis; S2. Subscribe to the vehicle’s GPS positioning data and store it in Kafka by connecting to the vehicle’s GPS device; store the real-time checkpoint vehicle passing data pushed by the front-end sensing device management platform in Kafka; S3, subscribes to the GPS positioning data and checkpoint passing data in Kafka in real time, and transfers the subscribed data to the thread pool one by one; The data entering the thread pool queries the corresponding business data from the Redis database based on the key fields, performs correlation comparison and aggregation on the data, and stores the aggregated processed data in Kafka; S4: Subscribe to the processed GPS positioning data and checkpoint vehicle data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle data and GPS positioning data entering the thread pool with the business data, associate the corresponding vehicles, and then issue checkpoint warnings and GPS warnings to the corresponding vehicles, and store the checkpoint warning data and GPS warning data in Kafka; The specific process of issuing checkpoint warning to the corresponding vehicle in step S4 is to subscribe to the checkpoint vehicle passing data processed in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle passing data entering the thread pool with the vehicle GPS track obtained from the Redis database to determine whether the vehicle GPS track is consistent with the corresponding checkpoint vehicle passing data. If not, an abnormal warning of the vehicle GPS device is given; if yes, a GPS warning is issued to the corresponding vehicle; S5. Subscribe to the processed GPS positioning data, checkpoint vehicle passing data, checkpoint warning data and GPS warning data in Kafka in real time, store the GPS positioning data and checkpoint vehicle passing data in Hbase with the corresponding license plate number as the name; store the GPS warning data and checkpoint warning data in Solr using the thread pool.

2. The method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles according to claim 1 is characterized by: The business data includes vehicle information, checkpoint equipment information, electronic fence information, pass information, route reporting information, cargo status information, vehicle GPS track information and warning rules.

3. The method for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles according to claim 1 is characterized by: The specific process of performing GPS warning on the corresponding vehicle in step S4 is as follows: A1. Subscribe to the processed GPS positioning data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the GPS positioning data entering the thread pool with the pass information obtained from the Redis database, associate the cargo status of the corresponding vehicle, determine whether the corresponding vehicle has a pass, and give the results of the corresponding vehicle having obtained a pass, having been registered, not having obtained a pass, and not having been registered according to the warning rules; for vehicles that have not obtained a pass or have not been registered, give a warning of not having obtained a pass, and upload the warning data of not having obtained a pass to Kafka; for vehicles that have obtained a pass or have been registered, proceed to step A2; A2. For vehicles that have obtained a license or have been registered, the GPS positioning data of the vehicle is compared with the route data of the vehicle obtained from the Redis database to determine whether the vehicle is moving within its route. If not, a route deviation warning is issued for the vehicle, and the route deviation warning data of the vehicle is uploaded to Kafka. If yes, proceed to step A3. A3. Compare the vehicle's GPS positioning data with the vehicle's electronic fence information obtained from the Redis database; determine whether the vehicle is within its electronic fence. If not, issue an electronic fence deviation warning for the vehicle, and upload the electronic fence deviation warning to Kafka. If yes, proceed to step A4. A4. Calculate whether the vehicle's speed is equal to 0 within a preset time period based on the vehicle's GPS trajectory. If so, give the vehicle an overtime parking warning and upload the overtime parking warning to Kafka; if not, it indicates that the vehicle is driving normally.

4. A system for real-time monitoring and early warning of the spatiotemporal trajectory of hazardous chemicals vehicles, characterized by: The system is used to implement the method described in any one of claims 1 to 3, and the system includes: Business data synchronization module: used to synchronize business data from the business database to the Redis database of the big database component at regular intervals; Data access module; It is used to subscribe to the vehicle's GPS positioning data and store it in Kafka by connecting to the vehicle's GPS device; it is used to store the real-time checkpoint vehicle passing data pushed by the front-end perception device management platform into Kafka; Data processing module: used to subscribe to GPS positioning data and checkpoint passing data in Kafka in real time, and pass the subscribed data to the thread pool one by one; The data entering the thread pool queries the corresponding business data from the Redis database based on the key fields, performs correlation comparison and aggregation on the data, and stores the aggregated processed data in Kafka; Early warning calculation module; It is used to subscribe to the processed GPS positioning data and checkpoint vehicle data in Kafka in real time, and pass the subscribed data to the thread pool one by one; compare the checkpoint vehicle data and GPS positioning data entering the thread pool with the business data, associate the corresponding vehicles, and then issue checkpoint warnings and GPS warnings to the corresponding vehicles, and store the checkpoint warning data and GPS warning data in Kafka; Data storage module; It is used to subscribe to the processed GPS positioning data, checkpoint vehicle passing data, checkpoint warning data and GPS warning data in Kafka in real time, and store the GPS positioning data and checkpoint vehicle passing data in Hbase with the corresponding license plate number as the name; store the GPS warning data and checkpoint warning data in Solr using the thread pool; Data query module; Used to query business data, checkpoint vehicle passing data, GPS positioning data, GPS warning data and checkpoint warning data.

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