An intelligent road rescue management platform based on big data analysis

Abstract

The present invention discloses a road intelligent rescue management platform based on big data analysis. The platform includes a bus baseline information acquisition module, a bus power information collection and analysis module, a bus driving parameter collection module, a bus braking information acquisition module, a bus braking safety analysis module, a bus rescue needs assessment and analysis module, a database, and a bus rescue matching analysis and processing module. By assessing the rescue needs of buses and automatically calling for rescue based on the power, driving, and braking information of each bus configured within a designated driving route, the platform effectively addresses the problem of current technologies being unable to reduce the severity of bus accident damage, achieves timely warnings of bus anomalies, and significantly reduces the degree of damage to buses and the severity of accidents, demonstrating a high level of intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of road rescue management and relates to an intelligent road rescue management platform based on big data analysis. Background Art

[0002] With the rapid development of the economy and the improvement of the transportation system, people's travel methods have gradually become more diverse. As a necessary means of transportation for urban travel, the serious harm caused by bus accidents is self-evident. Therefore, road rescue management for its sudden problems is particularly important.

[0003] Current roadside assistance management mainly involves a person calling a roadside assistance hotline when a bus breaks down or is involved in an accident. The roadside assistance management center then dispatches a corresponding vehicle to rescue the bus. However, the current technology still has the following problems:

[0004] First, due to the large passenger capacity of buses, the loss rate caused by their failure is also high. Currently, only rescuing buses after a failure occurs cannot effectively reduce the harm caused by bus accidents. At the same time, the buses have already caused certain damage, which cannot reduce the loss rate of bus failures and cannot ensure the safety of passengers.

[0005] Secondly, bus accidents or breakdowns are often not isolated incidents and often involve certain situations. However, personnel are not sufficiently aware of these situations. The current practice of regular vehicle inspections by personnel has certain limitations and cannot reduce the accident rate of buses, nor can it reduce the difficulty of road rescue, nor can it improve the efficiency of road rescue.

[0006] Third, the current method of conducting road rescue by having people call the rescue hotline has the problem of untimely calls, which cannot improve the timeliness of bus rescue, nor can it reduce the safety hazards of personnel caused by bus failures or accidents, nor can it improve the rescue effect, and the degree of intelligence is low. Summary of the Invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a road intelligent rescue management platform based on big data analysis is proposed;

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] The present invention provides a road intelligent rescue management platform based on big data analysis, the system comprising:

[0010] The bus benchmark information acquisition module is used to obtain the number of buses configured in the specified route, and number each bus in sequence as 1, 2, ...j, ...m;

[0011] The bus power information collection and analysis module is used to collect the current power information corresponding to each power unit through the power collection device carried by each bus, thereby analyzing the current power status of each bus and outputting the power safety assessment index corresponding to each bus;

[0012] The bus driving parameter collection module is used to collect the current corresponding driving parameters of each bus through the driving collection device carried by each bus, where the driving parameters include location, passenger weight and driving speed;

[0013] The bus braking information acquisition module is used to obtain the current braking information of each bus, where the braking information includes the cumulative number of braking times, the braking start time point, braking stop time point and braking distance corresponding to each braking time;

[0014] The bus braking safety analysis module is used to analyze the braking safety of each bus based on its current driving parameters and braking information, and output the braking safety assessment index of each bus;

[0015] The bus rescue demand assessment and analysis module is used to analyze the number of buses that need rescue and the corresponding rescue type for each bus based on the power safety assessment index and brake safety assessment index of each bus, and to construct a rescue report;

[0016] The bus rescue matching analysis and processing module is used to match and analyze the target rescue stations of each bus in need of rescue based on the rescue type corresponding to the bus in need of rescue, and send the rescue report to the target rescue station corresponding to each bus in need of rescue, and make an automatic rescue call at the same time;

[0017] The database is used to store the location of each rescue station and the status of each rescue type of vehicle in each rescue station.

[0018] As a preferred solution, the power collection device includes a liquid level sensor, a temperature sensor, an air tightness detector, an on-board ammeter, an on-board voltmeter, an electric quantity collector and a pressure sensor.

[0019] As a preferred solution, the power units are respectively a water tank unit, an electrical unit and a tire unit; wherein,

[0020] The dynamic information corresponding to the water tank unit is the liquid level, water temperature and tank air density;

[0021] The power information corresponding to the electrical unit is the operating current, operating voltage and remaining power;

[0022] The power information corresponding to the tire unit is the tire pressure corresponding to each tire.

[0023] As a preferred solution, the current power status of each bus is analyzed, and the specific analysis process includes the following steps:

[0024] Step 1: Extract the power information corresponding to the water tank unit from the current power information corresponding to each power unit in each bus, and use the calculation formula to calculate the safety assessment index of the water tank of each bus, and record it as j represents the bus number, j=1,2,......m;

[0025] Step 2: Extract the power information corresponding to the electrical unit from the current power information corresponding to each power unit in each bus, and calculate the electrical safety assessment index of each bus using the calculation formula, and record it as

[0026] Step 3: Extract the power information corresponding to the tire unit from the current power information corresponding to each power unit in each bus, and calculate the tire safety assessment index of each bus using the calculation formula, and record it as

[0027] Step 4: Based on the safety assessment index of each bus water tank Electrical Safety Assessment Index Tire Safety Assessment Index Substitute it into the calculation formula The corresponding power safety evaluation index of each bus is obtained, Q j It is represented as the power safety assessment index corresponding to the j-th bus, and ε1, ε2, and ε3 represent the corresponding weights of the set water tank safety, electrical safety, and tire safety, respectively.

[0028] As a preferred solution, the driving data collection device specifically includes a vehicle speed sensor, a GPS locator, and a weight sensor, wherein the vehicle speed sensor is used to collect the corresponding driving speed of the bus, the GPS locator is used to collect the corresponding position of the bus, and the weight sensor is used to collect the corresponding passenger weight of the bus.

[0029] As a preferred solution, the braking safety analysis of each bus is performed, and the specific analysis process includes the following steps:

[0030] The first step is to set the braking safety impact weight of each bus based on the current driving parameters of each bus, and record it as δ j ;

[0031] Step 2: Based on the current braking information of each bus, the cumulative number of braking times is extracted, and each braking time corresponding to each bus is numbered in order of braking order, and marked as 1, 2, ...d, ...g;

[0032] The third step is to extract the starting time and stopping time of each braking from the braking information of each bus, and then obtain the braking duration of each braking, which is recorded as T jd , d represents the number of each braking, d = 1, 2, ... g;

[0033] Step 4: Based on the braking time and braking distance corresponding to each current braking of each bus, as well as the braking safety impact weight corresponding to each bus, according to the analysis formula The power safety evaluation index corresponding to each bus is obtained by analysis, ζ j represents the power safety evaluation index corresponding to the j-th bus, T′ is the reference braking time corresponding to the set bus, X jd It is expressed as the braking distance corresponding to the jth bus at the dth braking time, X′ is the set reference braking distance corresponding to the bus, ΔT and ΔX are the set allowable braking time difference and allowable braking distance difference, c1 and c2 are the weight factors corresponding to the set braking time and braking distance, respectively.

[0034] As a preferred solution, the braking safety impact weight corresponding to each bus is set, and the specific setting process is as follows:

[0035] Extract the driving speed from the current driving parameters of each bus, and analyze the formula The braking safety weight coefficient μ1 corresponding to each bus speed is obtained by analysis j , v j represents the current speed of the j-th bus, and v′ represents the standard speed of the bus;

[0036] The passenger weight is extracted from the current driving parameters of each bus, and the corresponding passenger weight of the bus is matched and compared with the braking safety weight coefficient corresponding to each load. The braking safety weight coefficient corresponding to the passenger weight of each bus is screened and recorded as μ2 j ;

[0037] Based on the braking safety weight coefficient corresponding to the driving speed of each bus and the braking safety weight coefficient corresponding to the passenger weight, the braking safety impact weight corresponding to each bus is calculated, which has the following calculation formula: f1 and f2 represent the correction factors corresponding to the set driving speed and passenger weight respectively.

[0038] As a preferred solution, the specific analysis process of the number of buses requiring rescue and the rescue type corresponding to each bus requiring rescue configuration is as follows:

[0039] Compare the power safety assessment index corresponding to each bus with the set standard power safety assessment index. If the power safety assessment index corresponding to a bus is less than the standard power safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the power rescue type;

[0040] Compare the braking safety assessment index corresponding to each bus with the set standard braking safety assessment index. If the braking safety assessment index corresponding to a bus is less than the standard braking safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the braking rescue type;

[0041] If the rescue type corresponding to a bus includes both dynamic rescue type and brake rescue type, the rescue type corresponding to the bus is a comprehensive rescue type, so as to count the number of buses that need rescue and the rescue type corresponding to each bus that needs rescue.

[0042] As a preferred solution, the specific process of constructing the rescue report is: obtaining the position corresponding to each bus requiring rescue, integrating the position and rescue type corresponding to each bus requiring rescue, and forming a rescue report corresponding to each bus requiring rescue.

[0043] As a preferred solution, the matching analysis is performed on the target rescue stations corresponding to the buses in need of rescue. The specific analysis process is as follows:

[0044] Extract the corresponding location of each rescue station from the database and number each rescue station;

[0045] Extract the number of each bus that needs rescue and calculate the position matching degree between each bus that needs rescue and each rescue station based on the position of each bus that needs rescue. t←x , t represents the number of each bus that needs rescue, t = 1, 2, ... h, x represents the number of each rescue station, x = 1, 2, ... k;

[0046] The status of each rescue type vehicle in each rescue station is extracted from the database. Based on the rescue type corresponding to each rescue bus, the type matching degree between each rescue bus and each rescue station is obtained and recorded as

[0047] Based on the position matching degree and type matching degree of each rescue bus and each rescue station, the call optimization coefficient of each rescue bus and each rescue station is obtained. The analysis formula is: It is expressed as the call preference coefficient corresponding to the t buses requiring rescue and the x-th rescue station, They are the weights corresponding to the set position and type respectively;

[0048] The call preference coefficients of each bus requiring rescue and each rescue station are sorted from large to small, and the rescue station ranked first is used as the target rescue station corresponding to each bus requiring rescue.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] (1) The present invention provides a road intelligent rescue management platform based on big data analysis. By evaluating the rescue needs of buses and making automatic rescue calls from three levels: power information, driving information and braking information of each bus configured within a specified driving route, on the one hand, it effectively solves the problem that current technology cannot reduce the degree of harm caused by bus accidents, realizes timely warning of bus abnormalities, and greatly reduces the degree of damage to buses and the degree of harm caused by accidents, providing strong protection for the safety of passengers. On the one hand, by evaluating the rescue needs based on the three dimensions of power information, driving information and braking information of buses, it breaks the limitations, one-sidedness and generality of the current bus rescue evaluation model, avoids the subjective errors in personnel detection, improves the response efficiency of bus abnormalities, reduces the accident rate and difficulty of road rescue of buses, and realizes efficient and timely road emergency rescue; on the other hand, through automatic rescue calls, it ensures the timeliness of bus rescue, reduces the safety hazards of personnel caused by bus failures or accidents, and has a high level of intelligence.

[0051] (2) The present invention collects and analyzes the power information, driving information and braking information of each bus, which not only realizes the timely rescue of bus abnormalities, but also improves the comprehensiveness, reliability and rationality of bus driving safety detection. At the same time, through the collection and analysis of multi-dimensional information, the positioning efficiency and processing efficiency of bus abnormal events are guaranteed to the greatest extent, and can effectively reduce the tediousness of bus road rescue, improve the smoothness of road rescue and the management level of road rescue.

[0052] (3) The present invention matches and analyzes the rescue stations based on the rescue types corresponding to the buses that need to be rescued, thereby maximizing the arrival efficiency and rescue effect of rescue vehicles, ensuring the efficiency of handling bus rescue incidents, achieving rapid rescue of buses and rapid clearing of roads, and providing strong guarantees for the smooth operation of road traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 This is a schematic diagram of the connection of various modules of the system of the present invention. DETAILED DESCRIPTION

[0055] The above contents described below in conjunction with the implementation of the present invention are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall fall within the scope of protection of the present invention.

[0056] See also Figure 1 As shown, the present invention provides a road intelligent rescue management platform based on big data analysis, including a bus benchmark information acquisition module, a bus power information acquisition and analysis module, a bus driving parameter acquisition module, a bus braking information acquisition module, a bus braking safety analysis module, a bus rescue demand assessment analysis module, a database and a bus rescue matching analysis and processing module.

[0057] Based on the connection relationship shown in the figure, the bus benchmark information acquisition module is connected to the bus power information acquisition and analysis module, the bus brake safety analysis module is respectively connected to the bus driving parameter acquisition module and the bus brake information acquisition module, the bus rescue demand assessment analysis module is respectively connected to the bus power information acquisition and analysis module and the bus brake safety analysis module, and the bus brake safety analysis module is respectively connected to the bus rescue demand assessment analysis module and the database;

[0058] The bus reference information acquisition module is used to obtain the number of buses configured in a specified driving route, and number each bus in sequence as 1, 2, ...j, ...m;

[0059] The bus power information collection and analysis module is used to collect the current power information corresponding to each power unit through the power collection device on each bus, thereby analyzing the current power status of each bus and outputting the power safety assessment index corresponding to each bus;

[0060] In the above, the power collection device includes a liquid level sensor, a temperature sensor, an air tightness detector, an on-board ammeter, an on-board voltmeter, an electric quantity collector and a pressure sensor;

[0061] The above-mentioned power units are respectively the water tank unit, the electrical unit and the tire unit; among them, the power information corresponding to the water tank unit is the liquid level height, water temperature and tank air density; the power information corresponding to the electrical unit is the operating current, operating voltage and remaining power; the power information corresponding to the tire unit is the tire pressure corresponding to each tire.

[0062] It should be noted that the liquid level sensor is used to collect the liquid level height corresponding to the bus water tank unit, the temperature sensor is used to collect the water temperature corresponding to the bus water tank unit, and the air tightness detector is used to detect the air density of the tank corresponding to the bus water tank unit; the on-board ammeter, on-board voltmeter, and power collector are used to collect the operating current, operating voltage, and remaining power corresponding to the bus electrical unit respectively; the pressure sensor is used to collect the corresponding tire unit of the bus.

[0063] In the above, the current power status of each bus is analyzed, and the specific analysis process includes the following steps:

[0064] Step 1: Extract the power information corresponding to the water tank unit from the current power information corresponding to each power unit in each bus, and use the calculation formula to calculate the safety assessment index of the water tank of each bus, and record it as in, a1, a2, and a3 represent the safety impact weights corresponding to the set water tank level, water tank water temperature, and water tank gas density, respectively. j 、w j ,q j They are respectively represented by the current liquid level height, water temperature, and tank air density corresponding to the j-th bus water tank unit, L′ min 、w 标准 , Δw, q 标准 They represent the minimum required liquid level, standard water temperature, allowable water tank temperature difference, and standard tank air density corresponding to the set bus water tank, respectively. j represents the bus number, j = 1, 2, ... m;

[0065] Step 2: Extract the power information corresponding to the electrical unit from the current power information corresponding to each power unit in each bus, and calculate the electrical safety assessment index of each bus using the calculation formula, and record it as b1, b2, and b3 represent the operating safety impact weights corresponding to the set operating current, operating voltage, and remaining power, respectively. j 、U j 、D jThey are respectively represented by the current operating current, operating voltage and remaining power corresponding to the j-th bus electrical unit, I′, U′ are the set bus standard operating current, bus standard operating voltage, ΔI, ΔU are respectively represented by the set bus permitted operating current difference, permitted operating voltage difference, D min The minimum required power for the bus to travel;

[0066] Step 3: Extract the power information corresponding to the tire unit from the current power information corresponding to each power unit in each bus, and calculate the tire safety assessment index of each bus using the calculation formula, and record it as N jr It is represented as the tire pressure corresponding to the rth tire in the jth bus, where r represents the tire number, r = 1, 2, ... p, N' is the set standard tire pressure corresponding to the bus tire, N' is the set reference bus suitable tire pressure, ΔN is the set bus tire allowable tire pressure difference, and η is the set tire safety correction factor;

[0067] In one specific embodiment, p is 5;

[0068] Step 4: Based on the safety assessment index of each bus water tank Electrical Safety Assessment Index Tire Safety Assessment Index Substitute it into the calculation formula The corresponding power safety evaluation index of each bus is obtained, Q j It is represented as the power safety assessment index corresponding to the j-th bus, and ε1, ε2, and ε3 represent the corresponding weights of the set water tank safety, electrical safety, and tire safety, respectively.

[0069] The bus driving parameter collection module is used to collect the current corresponding driving parameters of each bus through the driving collection device carried by each bus, where the driving parameters include location, passenger weight and driving speed;

[0070] It should be noted that the driving data collection device specifically includes a vehicle speed sensor, a GPS locator, and a weight sensor. The vehicle speed sensor is used to collect the corresponding driving speed of the bus, the GPS locator is used to collect the corresponding position of the bus, and the weight sensor is used to collect the corresponding passenger weight of the bus.

[0071] The bus braking information acquisition module is used to obtain the current braking information corresponding to each bus, wherein the braking information includes the cumulative number of braking times, the braking start time point, the braking stop time point and the braking distance corresponding to each braking time;

[0072] The bus braking safety analysis module is used to analyze the braking safety of each bus based on the current driving parameters and braking information of each bus, and output the braking safety evaluation index corresponding to each bus;

[0073] For example, the braking safety analysis of each bus includes the following steps:

[0074] The first step is to set the braking safety impact weight of each bus based on the current driving parameters of each bus, and record it as δ j ;

[0075] Step 2: Based on the current braking information of each bus, the cumulative number of braking times is extracted, and each braking time corresponding to each bus is numbered in order of braking order, and marked as 1, 2, ...d, ...g;

[0076] The third step is to extract the starting time and stopping time of each braking from the braking information of each bus, and then obtain the braking duration of each braking, which is recorded as T jd , d represents the number of each braking, d = 1, 2, ... g;

[0077] Step 4: Based on the braking time and braking distance corresponding to each current braking of each bus, as well as the braking safety impact weight corresponding to each bus, according to the analysis formula The power safety evaluation index corresponding to each bus is obtained by analysis, ζ j represents the power safety evaluation index corresponding to the j-th bus, T′ is the reference braking time corresponding to the set bus, X jd It is expressed as the braking distance corresponding to the jth bus at the dth braking time, X′ is the set reference braking distance corresponding to the bus, ΔT and ΔX are the set allowable braking time difference and allowable braking distance difference, c1 and c2 are the weight factors corresponding to the set braking time and braking distance, respectively.

[0078] Furthermore, the braking safety impact weight corresponding to each bus is set, and the specific setting process is as follows:

[0079] Extract the driving speed from the current driving parameters of each bus, and analyze the formula The braking safety weight coefficient μ1 corresponding to each bus speed is obtained by analysis j , v j represents the current speed of the j-th bus, and v′ represents the standard speed of the bus;

[0080] The passenger weight is extracted from the current driving parameters of each bus, and the corresponding passenger weight of the bus is matched and compared with the braking safety weight coefficient corresponding to each load. The braking safety weight coefficient corresponding to the passenger weight of each bus is screened and recorded as μ2 j ;

[0081] Based on the braking safety weight coefficient corresponding to the driving speed of each bus and the braking safety weight coefficient corresponding to the passenger weight, the braking safety impact weight corresponding to each bus is calculated, which has the following calculation formula: f1 and f2 represent the correction factors corresponding to the set driving speed and passenger weight respectively.

[0082] The embodiment of the present invention collects and analyzes the power information, driving information and braking information of each bus, which not only realizes the timely rescue of bus anomalies, but also improves the comprehensiveness, reliability and rationality of bus driving safety detection. At the same time, through the collection and analysis of multi-dimensional information, the positioning efficiency and processing efficiency of bus anomalies are guaranteed to the greatest extent, and can effectively reduce the tediousness of bus road rescue, improve the smoothness of road rescue and the management level of road rescue.

[0083] The bus rescue demand assessment and analysis module is used to analyze the number of buses that need rescue and the rescue type corresponding to each bus that needs rescue based on the power safety assessment index and brake safety assessment index corresponding to each bus, and to construct a rescue report;

[0084] For example, the specific analysis process of the number of buses requiring rescue and the rescue type corresponding to each bus requiring rescue configuration is as follows:

[0085] Compare the power safety assessment index corresponding to each bus with the set standard power safety assessment index. If the power safety assessment index corresponding to a bus is less than the standard power safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the power rescue type;

[0086] Compare the braking safety assessment index corresponding to each bus with the set standard braking safety assessment index. If the braking safety assessment index corresponding to a bus is less than the standard braking safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the braking rescue type;

[0087] If the rescue type corresponding to a bus includes both dynamic rescue type and brake rescue type, the rescue type corresponding to the bus is a comprehensive rescue type, and the number of buses that need rescue and the rescue type corresponding to each bus that needs rescue are counted.

[0088] Furthermore, the specific process of constructing the rescue report is: obtaining the position corresponding to each bus that needs rescue, integrating the position and rescue type corresponding to each bus that needs rescue, and forming a rescue report corresponding to each bus that needs rescue.

[0089] The bus rescue matching analysis and processing module is used to perform matching analysis on the target rescue station of each bus requiring rescue based on the rescue type corresponding to the bus requiring rescue, and send a rescue report to the target rescue station corresponding to each bus requiring rescue, while making an automatic rescue call;

[0090] Specifically, the matching analysis is performed on the target rescue stations corresponding to the buses in need of rescue. The specific analysis process is as follows:

[0091] 1) Extract the corresponding location of each rescue station from the database and number each rescue station;

[0092] 2) Extract the number of each bus that needs rescue and calculate the position matching degree between each bus that needs rescue and each rescue station based on the position of each bus that needs rescue. t←x , t represents the number of each bus that needs rescue, t = 1, 2, ... h, x represents the number of each rescue station, x = 1, 2, ... k;

[0093] in, It represents the distance between the location of the t-th bus that needs rescue and the location of the x-th rescue station, and S′ is the set rescue preferred reference distance.

[0094] 3) Extract the status of each rescue type vehicle in each rescue station from the database, and based on the rescue type corresponding to each rescue bus, analyze and obtain the type matching degree between each rescue bus and each rescue station, and record it as

[0095] It should be noted that the analysis process of the type matching degree between each rescue bus and each rescue station is as follows: based on the rescue type corresponding to each rescue bus, the type of rescue vehicle required corresponding to each rescue bus is obtained. If the type of rescue vehicle required corresponding to a certain rescue bus in a certain rescue station is in an occupied state, the type matching degree between the rescue bus and the rescue station is recorded as σ1. If the type of rescue vehicle required corresponding to a certain rescue bus in a certain rescue station is in an idle state, the type matching degree between the rescue bus and the rescue station is recorded as σ2. The type matching degree between each rescue bus and each rescue station is obtained by analysis. The value is σ1 or σ2, σ2>σ1;

[0096] 4) Based on the position matching degree and type matching degree of each rescue bus and each rescue station, the call optimization coefficient of each rescue bus and each rescue station is obtained by analysis. The analysis formula is: It is expressed as the call preference coefficient corresponding to the t buses requiring rescue and the x-th rescue station, They are the weights corresponding to the set position and type respectively;

[0097] 5) Sort the call priority coefficients of each rescue bus and each rescue station from large to small, and use the rescue station ranked first as the target rescue station corresponding to each rescue bus.

[0098] The embodiment of the present invention matches and analyzes the rescue stations based on the rescue type corresponding to the bus that needs to be rescued, thereby maximizing the arrival efficiency and rescue effect of the rescue vehicles, ensuring the efficiency of handling bus rescue incidents, achieving rapid rescue of buses and rapid clearing of roads, and providing strong guarantees for the smooth operation of road traffic.

[0099] The database is used to store the location corresponding to each rescue station and the status corresponding to each rescue type of vehicle in each rescue station.

[0100] The embodiment of the present invention evaluates the rescue needs of buses and automatically calls for rescue based on the three levels of power information, driving information and braking information of each bus configured within a specified driving route. On the one hand, it effectively solves the problem that current technology cannot reduce the degree of harm caused by bus accidents, realizes timely warning of bus abnormalities, and greatly reduces the degree of damage to the bus and the degree of harm caused by accidents, providing strong protection for the safety of passengers. On the one hand, by evaluating the rescue needs based on the three dimensions of power information, driving information and braking information of the bus, it breaks the limitations, one-sidedness and generality of the current bus rescue assessment model, avoids the subjective errors in personnel detection, improves the response efficiency of bus abnormalities, reduces the accident rate and difficulty of road rescue for buses, and realizes efficient and timely road emergency rescue; on the other hand, through automatic rescue calls, the timeliness of bus rescue is guaranteed, the personal safety hazards caused by bus failures or accidents are reduced, and the intelligence level is relatively high.

[0101] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A road intelligent rescue management platform based on big data analysis, characterized by: include: The bus benchmark information acquisition module is used to obtain the number of buses configured in the specified route, and number each bus in sequence as 1, 2, ...j, ...m; The bus power information collection and analysis module is used to collect the current power information corresponding to each power unit through the power collection device carried by each bus, thereby analyzing the current power status of each bus and outputting the power safety assessment index corresponding to each bus; The bus driving parameter collection module is used to collect the current corresponding driving parameters of each bus through the driving collection device carried by each bus, where the driving parameters include location, passenger weight and driving speed; The bus braking information acquisition module is used to obtain the current braking information of each bus, where the braking information includes the cumulative number of braking times, the braking start time point, braking stop time point and braking distance corresponding to each braking time; The bus braking safety analysis module is used to analyze the braking safety of each bus based on its current driving parameters and braking information, and output the braking safety assessment index of each bus; The bus rescue demand assessment and analysis module is used to analyze the number of buses that need rescue and the corresponding rescue type for each bus based on the power safety assessment index and brake safety assessment index of each bus, and to construct a rescue report; The bus rescue matching analysis and processing module is used to match and analyze the target rescue stations of each bus in need of rescue based on the rescue type corresponding to the bus in need of rescue, and send the rescue report to the target rescue station corresponding to each bus in need of rescue, and make an automatic rescue call at the same time; The database is used to store the location of each rescue station and the status of each rescue type of vehicle in each rescue station.

2. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The power collection device includes a liquid level sensor, a temperature sensor, an air tightness detector, an on-board ammeter, an on-board voltmeter, an electric quantity collector and a pressure sensor.

3. The intelligent road rescue management platform based on big data analysis according to claim 2, characterized in that: The power units are respectively a water tank unit, an electrical unit and a tire unit; wherein, The dynamic information corresponding to the water tank unit is the liquid level, water temperature and tank air density; The power information corresponding to the electrical unit is the operating current, operating voltage and remaining power; The power information corresponding to the tire unit is the tire pressure corresponding to each tire.

4. The intelligent road rescue management platform based on big data analysis according to claim 3 is characterized by: The current power state of each bus is analyzed, and the specific analysis process includes the following steps: Step 1: Extract the power information corresponding to the water tank unit from the current power information corresponding to each power unit in each bus, and use the calculation formula to calculate the safety assessment index of the water tank of each bus, and record it as j represents the bus number, j=1,2,......m; Step 2: Extract the power information corresponding to the electrical unit from the current power information corresponding to each power unit in each bus, and calculate the electrical safety assessment index of each bus using the calculation formula, and record it as Step 3: Extract the power information corresponding to the tire unit from the current power information corresponding to each power unit in each bus, and calculate the tire safety assessment index of each bus using the calculation formula, and record it as Step 4: Based on the safety assessment index of each bus water tank Electrical Safety Assessment Index Tire Safety Assessment Index Substitute it into the calculation formula The corresponding power safety evaluation index of each bus is obtained, Q j It is represented as the power safety assessment index corresponding to the j-th bus, and ε1, ε2, and ε3 represent the corresponding weights of the set water tank safety, electrical safety, and tire safety, respectively.

5. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The driving data collection device specifically includes a vehicle speed sensor, a GPS locator, and a weight sensor. The vehicle speed sensor is used to collect the corresponding driving speed of the bus, the GPS locator is used to collect the corresponding position of the bus, and the weight sensor is used to collect the corresponding passenger weight of the bus.

6. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The braking safety analysis of each bus includes the following steps: The first step is to set the braking safety impact weight of each bus based on the current driving parameters of each bus, and record it as δ j ; Step 2: Based on the current braking information of each bus, the cumulative number of braking times is extracted, and each braking time corresponding to each bus is numbered in order of braking order, and marked as 1, 2, ...d, ...g; The third step is to extract the starting time and stopping time of each braking from the braking information of each bus, and then obtain the braking duration of each braking, which is recorded as T jd , d represents the number of each braking, d = 1, 2, ... g; Step 4: Based on the braking time and braking distance corresponding to each current braking of each bus, as well as the braking safety impact weight corresponding to each bus, according to the analysis formula The power safety evaluation index corresponding to each bus is obtained by analysis, ζ j represents the power safety evaluation index corresponding to the j-th bus, T′ is the reference braking time corresponding to the set bus, X jd It is expressed as the braking distance corresponding to the jth bus at the dth braking time, X′ is the set reference braking distance corresponding to the bus, ΔT and ΔX are the set allowable braking time difference and allowable braking distance difference, c1 and c2 are the weight factors corresponding to the set braking time and braking distance, respectively.

7. The intelligent road rescue management platform based on big data analysis according to claim 6, characterized in that: The braking safety impact weight corresponding to each bus is set, and the specific setting process is as follows: Extract the driving speed from the current driving parameters of each bus, and analyze the formula The braking safety weight coefficient μ1 corresponding to each bus speed is obtained by analysis j , v j represents the current speed of the j-th bus, and v′ represents the standard speed of the bus; The passenger weight is extracted from the current driving parameters of each bus, and the corresponding passenger weight of the bus is matched and compared with the braking safety weight coefficient corresponding to each load. The braking safety weight coefficient corresponding to the passenger weight of each bus is screened and recorded as μ2 j ; Based on the braking safety weight coefficient corresponding to the driving speed of each bus and the braking safety weight coefficient corresponding to the passenger weight, the braking safety impact weight corresponding to each bus is calculated, which has the following calculation formula: f1 and f2 represent the correction factors corresponding to the set driving speed and passenger weight respectively.

8. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The specific analysis process of the number of buses that need rescue and the rescue type corresponding to each bus that needs rescue configuration is as follows: Compare the power safety assessment index corresponding to each bus with the set standard power safety assessment index. If the power safety assessment index corresponding to a bus is less than the standard power safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the power rescue type; Compare the braking safety assessment index corresponding to each bus with the set standard braking safety assessment index. If the braking safety assessment index corresponding to a bus is less than the standard braking safety assessment index, it is determined that the bus needs rescue, and the rescue type corresponding to the bus is recorded as the braking rescue type; If the rescue type corresponding to a bus includes both dynamic rescue type and brake rescue type, the rescue type corresponding to the bus is a comprehensive rescue type, so as to count the number of buses that need rescue and the rescue type corresponding to each bus that needs rescue.

9. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The specific process of constructing the rescue report is: obtaining the position corresponding to each bus that needs rescue, integrating the position and rescue type corresponding to each bus that needs rescue, and forming a rescue report corresponding to each bus that needs rescue.

10. The intelligent road rescue management platform based on big data analysis according to claim 1, characterized in that: The matching analysis of the target rescue stations corresponding to the buses in need of rescue is performed as follows: Extract the corresponding location of each rescue station from the database and number each rescue station; Extract the number of each bus that needs rescue and calculate the position matching degree between each bus that needs rescue and each rescue station based on the position of each bus that needs rescue. t←x , t represents the number of each bus that needs rescue, t = 1, 2, ... h, x represents the number of each rescue station, x = 1, 2, ... k; The status of each rescue type vehicle in each rescue station is extracted from the database. Based on the rescue type corresponding to each rescue bus, the type matching degree between each rescue bus and each rescue station is obtained and recorded as Based on the position matching degree and type matching degree of each rescue bus and each rescue station, the call optimization coefficient of each rescue bus and each rescue station is obtained. The analysis formula is: It is expressed as the call preference coefficient corresponding to the t buses requiring rescue and the x-th rescue station, They are the weights corresponding to the set position and type respectively; The call preference coefficients of each bus requiring rescue and each rescue station are sorted from large to small, and the rescue station ranked first is used as the target rescue station corresponding to each bus requiring rescue.

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