CN111798127B - Chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent assessment - Google Patents
Chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent assessment Download PDFInfo
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Abstract
The invention discloses a chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation, which comprises a real-time monitoring module, a dynamic fire risk intelligent evaluation module and a real-time path optimization module which are connected through a local area network; the real-time monitoring module is used for collecting safety state data of the inspection position, the dynamic fire risk intelligent evaluation module is used for evaluating comprehensive fire risk levels of the inspection position, and the real-time path optimization module is used for optimizing paths of the inspection robot. And finally, a real-time path optimization module plans a routing inspection path by using the obtained risk level and the park specific coordinate information to guide the routing inspection robot to carry out safety inspection. The method realizes the safety routing inspection path planning of the chemical industry park with the side focus and the efficiency, and further ensures the safety of the chemical industry park to a greater extent.
Description
Technical Field
The invention relates to a safety and inspection technology, in particular to a chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation.
Background
With the rapid development of the chemical industry, chemical enterprises are continuously concentrated to chemical parks, and parkerizing becomes the main trend of the development of the chemical industry. Enterprises in the chemical industry park are concentrated, the quantity of dangerous chemicals stored and used is large, casualties and economic losses can be caused once accidents happen, people can also be scared, and in addition, fire and explosion accidents caused by the dangerous chemicals are prone to happening and are huge in harm. The management level of dangerous chemicals is particularly important in the safe production and safe operation of chemical enterprises, and not only directly influences the benefits of the enterprises, but also influences the public safety and social stability.
The fire inspection is one of important contents of safety inspection, inspects equipment facilities and positions where fire easily occurs in a chemical industry park, detects whether potential fire hazards easily occur or fire occurs, is an important guarantee link for safety production of enterprises in the chemical industry park, and is also an important part in safety management of dangerous chemicals.
At present, the fire safety inspection is mainly manual inspection and robot inspection. The area of chemical industry park is big usually, and the conflagration hidden danger is more and comparatively dispersed, adopts the manual work to patrol and examine, and is higher to patrolling and examining personnel self quality requirement, makes the misjudgement easily under fatigue state as the people, leads to the problem inspection not in place or omits to patrol and examine the efficiency simultaneously and lower. The ordinary robot patrols and examines and compare the manual work and patrols and examines efficiency higher, but ordinary robot patrols and examines and generally adopts the fixed route of patrolling and examining, and the ordinary robot of patrolling and examining can not in time plan out the optimum route of patrolling and examining when the production technology, raw materials type, memory space, temperature, combustible gas concentration etc. that wait to patrol and examine the position change and make conflagration risk level dynamic change, can not carry out the priority to the object that the risk is the highest and patrol and examine, leads to patrolling and examining and can not reach the requirement of "danger first, ampere times, the route is short, the high efficiency".
With the rapid development of technologies, such as artificial intelligence and the internet of things, technologies are widely applied, but in the chemical industry, such technologies are not yet applied in spring, and technologies such as artificial intelligence are not applied in the chemical industry, so that the establishment of intelligent construction such as the intelligent assessment and the auxiliary decision-making system for the dynamic risk of hazardous chemicals is the future of the modernization of the safety management of hazardous chemicals.
In the prior art:
application No. 201811033980.X "dynamic fire risk assessment method, apparatus, server and storage medium" patent discloses a dynamic fire risk assessment method, but has the following disadvantages:
the machine learning model trained by the method only depends on single moment state data when evaluating the fire risk level, and the wrong fire risk level caused by the situation that the safety state data is instantaneously and rapidly increased and instantaneously recovered due to abnormal reasons cannot be avoided.
The patent publication No. CN109397236A entitled "full-function inspection robot for petroleum depot" discloses a full-function inspection robot for petroleum depot, but has the following disadvantages:
the inspection path of the inspection robot is fixed, and the inspection path cannot be dynamically updated according to the change of the importance or the danger of each inspection position.
The patent publication No. CN107270921A entitled "a method and device for planning a routing for routing inspection for generation of dimension" discloses a method and device for planning a routing for routing inspection for generation of dimension, but has the following disadvantages:
after the inspection resources (the equipment to be inspected) are determined, the equipment hidden danger value of the inspection resources is calculated according to the equipment coverage scene grade, the equipment alarm grade and the equipment power failure time length information, and planning the routing inspection path according to the hidden danger value from high to low, if the resource to be inspected has no equipment alarm and equipment power failure, the equipment hidden danger value is uniquely determined by the equipment coverage scene grade, which is fixed, namely, the hidden danger values of the same equipment covering scene level are the same (the patrol priority is the same), but even if the equipment is in a safe state, the specific safe state data, the equipment volume and the environment condition of the equipment all affect the hidden danger degree of the equipment, therefore, when the equipment alarm and the equipment power failure do not occur, the hidden danger values of the same equipment covering the scene level cannot be or should not be the same;
the patent fails to divide the risk degree of the equipment in the safe state, fails to carry out targeted inspection on the equipment in the safe state from high risk degree corresponding to the real-time state to the bottom, and may cause the risk of the network equipment to be low, but the method is not applicable to the inspection object with high risk.
Disclosure of Invention
The invention aims to provide a chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent assessment.
The purpose of the invention is realized by the following technical scheme:
the chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation comprises a real-time monitoring module, a dynamic fire risk intelligent evaluation module and a real-time path optimization module which are connected through a local area network;
the real-time monitoring module is used for collecting safety state data of the inspection position, the dynamic fire risk intelligent evaluation module is used for evaluating comprehensive fire risk levels of the inspection position, and the real-time path optimization module is used for optimizing paths of the inspection robot.
According to the technical scheme provided by the invention, the chemical industry park inspection robot path optimization system based on dynamic intelligent fire risk assessment provided by the embodiment of the invention carries out comprehensive fire risk assessment aiming at inspection positions in a safe state, considers the risk change trend of each inspection point and further guides path optimization of each inspection point.
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Fig. 1 is a schematic structural diagram of a chemical industry park inspection robot path optimization system based on dynamic intelligent fire risk assessment according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a path update mechanism according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail below. Details which are not described in detail in the embodiments of the invention belong to the prior art which is known to a person skilled in the art.
The invention discloses a chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation, which has the preferred specific implementation mode that:
the system comprises a real-time monitoring module, a dynamic fire risk intelligent evaluation module and a real-time path optimization module which are connected through a local area network;
the real-time monitoring module is used for collecting safety state data of the inspection position, the dynamic fire risk intelligent evaluation module is used for evaluating comprehensive fire risk levels of the inspection position, and the real-time path optimization module is used for optimizing paths of the inspection robot.
The real-time monitoring module comprises a storehouse environment monitoring module, a workshop monitoring module and a public area environment monitoring module;
the storehouse environment monitoring module is mainly responsible for collecting the types and the number of dangerous chemicals in the storehouse, the specific numerical values of temperature, humidity and combustible gas concentration and transmitting the data to a storehouse sub-database in the real-time database;
the workshop monitoring module is mainly responsible for acquiring specific numerical values of safety indexes in a workshop and transmitting data to a workshop sub-database in the real-time database;
the public area environment monitoring module is mainly responsible for acquiring specific numerical values of temperature and smoke concentration in public areas including office areas and canteens and transmitting the data to a public area sub-database in a real-time database;
the environment monitoring module in the workshop monitoring module is mainly responsible for collecting specific numerical values of workshop temperature, workshop humidity, workshop combustible gas concentration, material types and quantity;
and an equipment monitoring module in the workshop monitoring module is mainly responsible for acquiring specific numerical values of equipment temperature, equipment pressure, equipment operation time, equipment liquid level and equipment liquid flow.
The dynamic fire risk intelligent evaluation module comprises a primary fire risk intelligent evaluation module, a data processing unit, an early warning module and a database module, and the primary fire risk intelligent evaluation module, the early warning module and the database module are connected with the data processing unit and are in two-way communication;
the database module comprises a real-time database, a primary fire risk grade database and a comprehensive fire risk grade database;
the real-time database comprises a storehouse sub-database, a workshop sub-database and a public area sub-database, and each sub-database stores real-time data collected by a corresponding monitoring module;
the primary fire risk grade database stores intelligent primary fire risk grades of routing inspection positions of storehouses, workshops and the like evaluated by the primary fire risk intelligent evaluation module;
the comprehensive risk grade database stores the comprehensive fire risk grade of the inspection position evaluated by the early warning module;
the primary fire risk intelligent evaluation module is mainly responsible for evaluating intelligent primary fire risk levels of all positions according to specific safety state data of the inspection positions, including temperature and combustible gas concentration, and comprises a storehouse fire risk intelligent evaluation model, a workshop fire risk intelligent evaluation model and a public area fire risk intelligent evaluation model, and the three models respectively obtain the safety state data of the inspection positions from corresponding sub-databases and finish intelligent primary fire risk evaluation;
the early warning module is mainly responsible for comprehensive fire risk assessment, the early warning module acquires a plurality of intelligent primary fire risk assessment results at each inspection position continuously from a primary fire risk grade database, the results are divided into two parts according to a certain proportion in a time sequence, the change trend of the results of the front part and the rear part is comprehensively analyzed and judged, the comprehensive fire risk grade is determined according to the highest assessment result in the two assessment results determined respectively at the front part and the rear part, the assessment result is stored in the comprehensive risk grade database, and the early warning module completes one-time comprehensive risk grade assessment according to a certain time;
and the data processing unit in the dynamic intelligent fire risk assessment module is mainly responsible for data calling and transmission among the primary intelligent fire risk assessment module, the early warning module and the database module.
The real-time path optimization module comprises a data processing unit, an inspection process monitoring module, a path planning module and a fire risk grade change monitoring module, wherein the inspection process monitoring module, the path planning module, the fire risk grade change monitoring module and the data processing module are connected and in two-way communication;
the patrol inspection point and park information module mainly stores patrol inspection position coordinates and a park map, and when a situation that a road is blocked due to construction occurs in a park, the park map is updated by the patrol inspection point and park information module, so that the real-time accuracy of the park map is ensured;
the fire risk grade change monitoring module is mainly responsible for real-time tracking and comparison of comprehensive fire risk grades of all inspection positions, and the data processing module is immediately called to analyze whether a path needs to be updated or not once the comprehensive fire risk grade changes;
the inspection path monitoring module is mainly responsible for inspection progress recording and inspection robot position positioning;
the path planning module carries out path planning according to the comprehensive fire risk level, the coordinate and the specific layout of the garden of each patrol position by taking the high-priority patrol of the fire risk level as a first principle and taking the total path distance as a second principle;
and a data processing unit in the real-time path optimization module is mainly responsible for information acquisition, module calling, analysis and judgment of whether the current path meets a path planning principle or not when the fire disaster grade is changed comprehensively and selection of a position to be patrolled and examined when the path needs to be updated.
The specific path planning process is as follows:
the route planning module finishes the first route planning according to the specific information, the inspection robot starts to inspect according to the planned route, when the inspection robot finishes inspecting the current position, the fire risk grade change monitoring module is called to judge whether the comprehensive fire risk grade of each inspection position changes, and if the comprehensive fire risk grade of each inspection position does not change, the inspection robot goes to the next inspection position according to the original route;
if the fire risk level change monitoring module sends a signal to the data processing unit, the data processing unit calls the inspection process module and the fire risk level change monitoring module to acquire comprehensive risk level change information of the inspection process and the inspection point, analyzes whether the inspection path needs to be updated or not, and if the inspection path needs to be updated, the path planning module acquires information of the relevant inspection point to plan the path again and updates the inspection path.
When the comprehensive fire risk level of the inspection point changes, the path updating mechanism is as follows:
the data processing unit reads the current inspection process and the comprehensive risk grade change condition of the inspection point for analysis:
(1) if the inspection points with the changed comprehensive risk levels are inspected completely, judging the comprehensive risk level change condition of the inspection points, and if the levels are all increased, transmitting information of inspection points with the increased levels and inspection points without inspection to a path planning module to generate a new path; if the risk levels of the routing inspection points with the changed levels are all reduced, keeping the original routing inspection unchanged; if the inspection points with the increased risk level and the inspection points with the decreased risk level are in the inspection points with the changed levels, transmitting the information of the inspection points with the increased risk level and the inspection points without inspection to a path planning module to generate a new path;
(2) if the routing inspection points which are subjected to comprehensive risk level change comprise routing inspection points which are subjected to routing inspection and routing inspection points which are not subjected to routing inspection, judging the risk level change conditions of the routing inspection points which are subjected to routing inspection, and if the risk levels are all reduced, transmitting the information of the routing inspection points which are not subjected to routing inspection to a path planning module to generate a new path; if the grade change inspection point is the condition that the risk grade is all increased and the risk grade is increased or reduced, transmitting the information of the inspection point with the higher grade and the inspection points which are not subjected to inspection to a path planning module to generate a new path;
(3) and if all the inspection points with the changed grades are not inspected, transmitting the information of the inspection points which are not inspected to the path planning module to generate a new path.
The chemical industry park patrols and examines robot path optimizing system based on intelligence of the risk of dynamic fire of the invention, the system is often in the dynamic change to the characteristics that storing, using chemicals kind, quantity and workshop state of the chemical industry park, the fire risk grade is not fixed, have realized the comprehensive fire risk grade of each patrolling and examining the dynamic assessment of the grade, compare the machine learning model fire risk grade result that only is divided according to the static safety state data more accurate in addition, the ones that effectively excluded the safety state data caused by abnormal reason increase the instant recovery situation produces inaccurate intelligent primary fire risk grade result, have guaranteed the accurate and effective comprehensive fire risk grade obtained, have given sufficient attention to probably having hidden danger and patrolling and examining the position that the hidden danger is developing gradually at the same time; aiming at the problems that a fixed routing inspection path has no side emphasis and is poor in adaptability, the real-time path planning module realizes dynamic path planning, once the comprehensive fire disaster grade of the routing inspection position changes, the real-time path optimization module analyzes and judges whether the current path meets the requirements or not, and if the comprehensive fire disaster grade of the routing inspection position does not meet the requirements, the path is updated immediately, so that the safety routing inspection is always performed with the side emphasis and the efficiency. Compared with CN107270921A, the system performs comprehensive fire risk assessment for inspection positions in a safe state, considers the risk change trend of each inspection point, and further guides the path optimization of each inspection point.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the comprehensive fire risk grade evaluation is completed by analyzing a plurality of continuous intelligent primary fire risk grade results through the early warning module, compared with the fire risk grade result evaluated only according to the static safety state data by the machine learning model, the comprehensive fire risk grade evaluation method is more accurate, and the inaccurate intelligent primary fire risk grade result generated under the condition of instantaneous sudden increase and instantaneous recovery of the safety state data caused by abnormal reasons is effectively eliminated, so that the accuracy and effectiveness of the obtained comprehensive fire risk grade are ensured, meanwhile, enough attention is given to the inspection position which possibly has hidden dangers and is gradually developed, further, the occurrence of accidents can be effectively avoided, and the safety of a chemical industry park is ensured.
Compared with manual inspection, the inspection robot has global path planning capability, high inspection efficiency, accurate inspection result and manpower and material resource saving; the routing inspection path of the common robot is fixed or is only updated regularly according to the risk level, and the condition that the fire risk level changes frequently cannot be flexibly adapted. Compared with the situation, the invention plans the global routing inspection path according to the comprehensive fire risk level, the routing inspection point position and the park layout information, immediately analyzes whether the routing inspection path needs to be updated or not when the comprehensive fire risk level of the routing inspection point changes, and immediately updates the routing inspection path if the comprehensive fire risk level of the routing inspection point needs to be updated, thereby ensuring that routing inspection is performed with a focus and high efficiency.
The invention carries out comprehensive fire risk assessment aiming at the inspection position in a safe state, considers the risk change trend of each inspection point and further guides the path optimization of each inspection point.
The specific embodiment is as shown in fig. 1 and fig. 2:
it will be understood by those skilled in the art that the following examples are illustrative only and should not be taken as limiting the scope of the invention, and that conditions not specifically noted in the examples are performed according to conventional conditions or conditions suggested by the manufacturer. All instruments are not indicated by manufacturers, and are conventional products available on the market.
The establishment of three evaluation models in the primary fire risk intelligent evaluation module mainly comprises the following steps: take storehouse fire risk intelligent assessment model as an example, select actual hazardous chemicals kind and quantity in the chemical industry garden storehouse, temperature, humidity and combustible gas concentration data record as sample attribute part, select 100 this type of sample, and guarantee to select the sample and have universal representativeness, later invite the expert, adopt expert's scoring method to confirm every sample attribute and the elementary fire risk grade corresponding relation of intelligence, form the sample set of 100 samples, with the help of machine learning algorithm, utilize this sample set training model, confirm storehouse fire risk intelligent assessment model that meets the requirements at last. The steps of establishing the workshop fire risk intelligent evaluation model and the public area fire risk intelligent evaluation model are consistent with those of the storehouse fire risk intelligent evaluation model. The intelligent primary fire risk level is divided into 6 levels, which are respectively very safe, relatively safe, dangerous, relatively dangerous and very dangerous.
The comprehensive fire risk level early warning module comprises the following evaluation steps: and acquiring a plurality of intelligent primary fire risk grade results at each inspection position by a primary fire risk grade database, dividing the results into two parts according to a certain proportion in a time sequence, comprehensively analyzing and judging the result change trend of the front part and the rear part, and determining the comprehensive fire risk grade according to the highest evaluation result in the two evaluation results respectively determined by the front part and the rear part. For example: and acquiring current continuous 30 intelligent primary fire risk grade results of the inspection position by a primary fire risk grade database, selecting a repeated result from the first 26 results as grade 1, selecting a highest grade result from the last four results as grade 2, if the grade 2 is higher than the grade 1, taking the grade 2 as a comprehensive fire risk grade of the inspection position, and if the grade 2 is lower than the grade 1, taking the grade 1 as a comprehensive fire risk grade of the inspection position. The integrated fire risk rating is divided into 6 levels, very safe, relatively safe, dangerous, relatively dangerous and very dangerous respectively. The comprehensive fire risk level evaluation is completed every 30 minutes, and the result is stored in a comprehensive fire risk database.
The chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent evaluation has the following specific implementation modes: firstly, real-time data of inspection positions of storehouses, workshops, office areas and the like acquired by a real-time monitoring module are transmitted to a real-time database through a local area network, safety state data of each inspection position are acquired by a primary fire risk intelligent evaluation module from the real-time database every 1 minute, the data are sent to a corresponding fire risk grade evaluation model according to the type of the inspection position, intelligent primary risk evaluation is completed by means of a machine learning algorithm, and evaluation results are stored in the real-time risk grade database. And then the early warning module acquires a plurality of continuous intelligent primary fire risk level results of each inspection position from the primary fire risk level database to comprehensively evaluate the comprehensive fire risk level of each inspection position and stores the results into the comprehensive fire risk database (the early warning module divides the comprehensive risk level once every 30 minutes according to a certain time), and finally the data processing module transmits the corresponding information in the inspection point and park information module and the risk level of each inspection point in the comprehensive fire risk database to the path planning module to plan the current optimal inspection path. Note that: and when the comprehensive fire risk database is empty, the latest intelligent primary fire risk level in the primary fire risk level database is adopted for routing inspection path planning.
The inspection robot starts inspection according to the planned path, when the inspection robot reaches inspection, a fire risk grade change monitoring module is called to monitor whether the comprehensive fire risk grade of each inspection point changes, if not, the inspection robot starts inspection work at the inspection position and goes to the next inspection point according to the original path; if the fire risk grade changes, the fire risk grade change monitoring module sends signals to the data processing unit, the data processing unit calls the inspection progress module and the fire risk grade change monitoring module, inspection progress and inspection point risk grade change information is obtained, whether an inspection path needs to be updated or not is analyzed, if the inspection path needs to be updated, the path planning module obtains relevant inspection point information and plans the path again, and the inspection path is updated.
When the comprehensive fire risk level of the inspection point changes, the path updating mechanism is as follows: the data processing unit reads the current inspection process and the comprehensive risk grade change condition of the inspection point for analysis: (1) and if all the inspection points with the changed comprehensive risk levels are inspected, judging the comprehensive risk level change condition of the inspection points. If the grades are all increased, transmitting the information of the inspection points with the grades being increased and the inspection points which are not inspected to a path planning module to generate a new path; if the risk levels of the routing inspection points with the changed levels are all reduced, keeping the original routing inspection unchanged; if the inspection points with the increased risk level and the inspection points with the decreased risk level are in the inspection points with the changed levels, the information of the inspection points with the increased risk level and the inspection points without inspection is transmitted to the path planning module to generate a new path. (2) And if the patrol points with the comprehensive risk level change have patrol points which are patrolled and not patrolled, judging the risk level change condition of the patrol points which are patrolled. If the risk level is reduced, transmitting the inspection point information which is not inspected to a path planning module to generate a new path; and if the grade change inspection point is the condition that the risk grade is totally increased and the risk grade is increased or reduced, transmitting the information of the inspection point with the higher grade and the inspection points which are not subjected to inspection to a path planning module to generate a new path. (3) And if all the grade change inspection points are not inspected, transmitting the inspection point information which is not inspected to the path planning module to generate a new path.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (1)
1. A chemical industry park inspection robot path optimization system based on dynamic fire risk intelligent assessment is characterized by comprising a real-time monitoring module, a dynamic fire risk intelligent assessment module and a real-time path optimization module which are connected through a local area network;
the real-time monitoring module is used for acquiring safety state data of the inspection position, the dynamic fire risk intelligent evaluation module is used for evaluating comprehensive fire risk level of the inspection position, and the real-time path optimization module is used for optimizing a path of the inspection robot;
the real-time monitoring module comprises a storehouse environment monitoring module, a workshop monitoring module and a public area environment monitoring module;
the storehouse environment monitoring module is mainly responsible for collecting the types and the number of dangerous chemicals in the storehouse, the specific numerical values of temperature, humidity and combustible gas concentration and transmitting the data to a storehouse sub-database in the real-time database;
the workshop monitoring module is mainly responsible for acquiring specific numerical values of safety indexes in a workshop and transmitting data to a workshop sub-database in the real-time database;
the public area environment monitoring module is mainly responsible for acquiring specific numerical values of temperature and smoke concentration in public areas including office areas and canteens and transmitting the data to a public area sub-database in a real-time database;
the environment monitoring module in the workshop monitoring module is mainly responsible for collecting specific numerical values of workshop temperature, workshop humidity, workshop combustible gas concentration, material types and quantity;
the equipment monitoring module in the workshop monitoring module is mainly responsible for acquiring specific numerical values of equipment temperature, equipment pressure, equipment running time, equipment liquid level and equipment liquid flow;
the dynamic fire risk intelligent evaluation module comprises a primary fire risk intelligent evaluation module, a data processing unit, an early warning module and a database module, and the primary fire risk intelligent evaluation module, the early warning module and the database module are connected with the data processing unit and are in two-way communication;
the database module comprises a real-time database, a primary fire risk grade database and a comprehensive fire risk grade database;
the real-time database comprises a storehouse sub-database, a workshop sub-database and a public area sub-database, and each sub-database stores real-time data collected by a corresponding monitoring module;
the primary fire risk grade database stores intelligent primary fire risk grades of routing inspection positions of storehouses and workshops, which are evaluated by the primary fire risk intelligent evaluation module;
the comprehensive fire risk grade database stores the comprehensive fire risk grade of the inspection position evaluated by the early warning module;
the primary fire risk intelligent evaluation module is mainly responsible for evaluating intelligent primary fire risk levels of all positions according to specific safety state data of the inspection positions, including temperature and combustible gas concentration, and comprises a storehouse fire risk intelligent evaluation model, a workshop fire risk intelligent evaluation model and a public area fire risk intelligent evaluation model, and the three models respectively obtain the safety state data of the inspection positions from corresponding sub-databases and finish intelligent primary fire risk evaluation;
the early warning module is mainly responsible for comprehensive fire risk assessment, the early warning module acquires a plurality of continuous intelligent primary fire risk assessment results of each inspection position from a primary fire risk grade database, the results are divided into two parts according to a certain proportion in time sequence, the change trend of the results of the front part and the rear part is comprehensively analyzed and judged, the comprehensive fire risk grade is determined according to the highest assessment result in the two assessment results respectively determined by the front part and the rear part, the assessment result is stored in the comprehensive fire risk grade database, and the early warning module completes one-time comprehensive risk grade assessment according to a certain time;
the data processing unit in the dynamic fire risk intelligent evaluation module is mainly responsible for data calling and transmission among the primary fire risk intelligent evaluation module, the early warning module and the database module;
the real-time path optimization module comprises a data processing unit, a patrol point and park information module, a patrol process monitoring module, a path planning module and a fire risk level change monitoring module, wherein the patrol point and park information module, the patrol process monitoring module, the path planning module and the fire risk level change monitoring module are connected with the data processing module and are in two-way communication;
the patrol inspection point and park information module mainly stores patrol inspection position coordinates and a park map, and when a situation that a road is blocked due to construction occurs in a park, the park map is updated by the patrol inspection point and park information module, so that the real-time accuracy of the park map is ensured;
the fire risk level change monitoring module is mainly responsible for real-time tracking and comparison of comprehensive fire risk levels of all inspection positions, and once the comprehensive fire risk level changes, the data processing module is immediately called to analyze whether a path needs to be updated or not;
the inspection progress monitoring module is mainly responsible for inspection progress recording and inspection robot position positioning;
the path planning module carries out path planning according to the comprehensive fire risk level, the coordinate and the specific layout of the garden of each patrol position by taking the high-priority patrol of the fire risk level as a first principle and taking the total path distance as a second principle;
the data processing unit is mainly responsible for acquiring information, calling each module, analyzing and judging whether a current path accords with a path planning principle or not when the fire disaster grade is changed comprehensively and selecting a position to be inspected when the path needs to be updated;
the specific path planning process is as follows:
the route planning module finishes the first route planning according to the specific information, the inspection robot starts to inspect according to the planned route, when the inspection robot finishes inspecting the current position, the fire risk grade change monitoring module is called to judge whether the comprehensive fire risk grade of each inspection position changes, and if the comprehensive fire risk grade does not change, the inspection robot goes to the next inspection position according to the original route;
if the fire risk level change monitoring module changes, the fire risk level change monitoring module sends a signal to the data processing unit, the data processing unit calls the inspection process module and the fire risk level change monitoring module to acquire comprehensive risk level change information of an inspection process and an inspection point and analyze whether an inspection path needs to be updated or not, and if the inspection path needs to be updated, the path planning module acquires information of the relevant inspection point and plans the path again to update the inspection path;
when the comprehensive fire risk level of the inspection point changes, the path updating mechanism is as follows:
the data processing unit reads the current inspection process and the comprehensive risk grade change condition of the inspection point for analysis:
(1) if the inspection points with the changed comprehensive risk levels are inspected completely, judging the comprehensive risk level change condition of the inspection points, and if the levels are all increased, transmitting information of inspection points with the increased levels and inspection points without inspection to a path planning module to generate a new path; if the risk levels of the routing inspection points with the changed levels are all reduced, keeping the original routing inspection unchanged; if the inspection points with the increased risk level and the inspection points with the decreased risk level are in the inspection points with the changed levels, transmitting the information of the inspection points with the increased risk level and the inspection points without inspection to a path planning module to generate a new path;
(2) if the inspection points integrating the risk level changes comprise inspection points which are inspected and inspection points which are not inspected, judging the risk level change conditions of the inspection points which are inspected, if the risk levels are reduced, transmitting the information of the inspection points which are not inspected to a path planning module, and generating a new path; if the grade change inspection point is the condition that the risk grade is all increased and the risk grade is increased or reduced, transmitting the information of the inspection point with the higher grade and the inspection points which are not subjected to inspection to a path planning module to generate a new path;
(3) and if all the grade change inspection points are not inspected, transmitting the inspection point information which is not inspected to the path planning module to generate a new path.
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| CN116993166A (en) * | 2023-09-25 | 2023-11-03 | ***安迪信息科技股份有限公司 | Park safety risk monitoring method |
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106843240A (en) * | 2017-04-18 | 2017-06-13 | 哈尔滨理工大学 | System is coordinated by fire prevention robot and multimachine based on intelligent video |
| CN107270921A (en) * | 2016-04-08 | 2017-10-20 | ***通信集团四川有限公司 | A kind of generation dimension polling path method and device for planning |
| WO2018107916A1 (en) * | 2016-12-14 | 2018-06-21 | 南京阿凡达机器人科技有限公司 | Robot and ambient map-based security patrolling method employing same |
| CN109799799A (en) * | 2019-02-22 | 2019-05-24 | 赛飞特工程技术集团有限公司 | Chemical industrial park big data analysis platform |
| CN110097727A (en) * | 2019-04-30 | 2019-08-06 | 暨南大学 | Forest Fire Alarm method and system based on fuzzy Bayesian network |
| CN110766179A (en) * | 2019-11-21 | 2020-02-07 | 中冶华天工程技术有限公司 | Multi-site routing inspection path design method adopting risk priority strategy |
| CN111311085A (en) * | 2020-02-10 | 2020-06-19 | 清华大学合肥公共安全研究院 | Dynamic risk assessment method and device for building fire based on Internet of things monitoring |
-
2020
- 2020-07-02 CN CN202010633779.6A patent/CN111798127B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107270921A (en) * | 2016-04-08 | 2017-10-20 | ***通信集团四川有限公司 | A kind of generation dimension polling path method and device for planning |
| WO2018107916A1 (en) * | 2016-12-14 | 2018-06-21 | 南京阿凡达机器人科技有限公司 | Robot and ambient map-based security patrolling method employing same |
| CN106843240A (en) * | 2017-04-18 | 2017-06-13 | 哈尔滨理工大学 | System is coordinated by fire prevention robot and multimachine based on intelligent video |
| CN109799799A (en) * | 2019-02-22 | 2019-05-24 | 赛飞特工程技术集团有限公司 | Chemical industrial park big data analysis platform |
| CN110097727A (en) * | 2019-04-30 | 2019-08-06 | 暨南大学 | Forest Fire Alarm method and system based on fuzzy Bayesian network |
| CN110766179A (en) * | 2019-11-21 | 2020-02-07 | 中冶华天工程技术有限公司 | Multi-site routing inspection path design method adopting risk priority strategy |
| CN111311085A (en) * | 2020-02-10 | 2020-06-19 | 清华大学合肥公共安全研究院 | Dynamic risk assessment method and device for building fire based on Internet of things monitoring |
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