WO2019227709A1 - 服务器、查勘网格的优化方法及存储介质 - Google Patents

服务器、查勘网格的优化方法及存储介质 Download PDF

Info

Publication number
WO2019227709A1
WO2019227709A1 PCT/CN2018/102106 CN2018102106W WO2019227709A1 WO 2019227709 A1 WO2019227709 A1 WO 2019227709A1 CN 2018102106 W CN2018102106 W CN 2018102106W WO 2019227709 A1 WO2019227709 A1 WO 2019227709A1
Authority
WO
WIPO (PCT)
Prior art keywords
intersection
auto insurance
cases
survey
survey point
Prior art date
Application number
PCT/CN2018/102106
Other languages
English (en)
French (fr)
Inventor
吴育人
邱鹏飞
荣兴汉
庄伯金
肖京
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2019227709A1 publication Critical patent/WO2019227709A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present application relates to the field of data processing technology, and in particular, to a server, an optimization method for a survey grid, and a storage medium.
  • the automobile insurance survey grid is generally based on the experience of surveyors or directly divided by administrative area. For example, a survey point is set at a certain location and a surveyor is assigned. The surveyor uses the survey point as the center to survey the area within a certain range. In the case of automobile insurance, the area surveyed by each surveyor forms the automobile insurance survey grid. This method is too simple and solidified. For areas with more complicated road conditions and more traffic accidents, and areas with simpler road conditions and fewer traffic accidents, the situation of auto insurance surveys is completely different. The existing grid distribution of auto insurance surveys cannot Give full play to the initiative of surveyors, resulting in low efficiency of vehicle insurance surveys.
  • the purpose of the present application is to provide a server, an optimization method of a survey grid, and a storage medium, which are aimed at optimizing the distribution of a car insurance survey grid to improve the efficiency of a car insurance survey.
  • the present application provides a server.
  • the server includes a memory and a processor connected to the memory.
  • the memory stores a processing system that can run on the processor.
  • the processing system is When the processor executes, the following steps are implemented:
  • each intersection in the area where no surveyor is equipped the first road length corresponding to the predetermined road connected to each intersection, the number of first auto insurance cases mapped to each intersection, and the corresponding distances of each intersection to each survey point, based on The first road length, the number of first car insurance cases, and the distance are used to calculate the sub-gravity of each intersection at each survey point, and the total gravitation of each intersection at each survey point is calculated based on each sub-gravity;
  • each survey point Before moving each survey point according to the direction of the corresponding total gravity and a preset distance, in the survey area where each survey point is located, obtain a driving center that is centered on the location of the survey point within a predetermined weighted driving time
  • the total length of the second road in the area covered, the number of second car insurance cases in the covered area is obtained, and the weighted energy of the survey point is calculated based on the length of the second road and the number of second car insurance cases. Adding the weighted energy to obtain a first weighted energy sum;
  • the weighted energy of the survey point is calculated based on the position of the survey point after the move, and the weighted energy of the survey point after the move is added to the weighted energy of each survey point that has not moved to obtain a second Weighted energy sum
  • the optimization method for a survey grid includes:
  • each survey point Before moving each survey point according to the direction of the corresponding total gravity and a preset distance, in the survey area where each survey point is located, obtain the survey point location as the center and within a predetermined weighted driving time.
  • the total length of the second road in the area covered by the car obtain the number of second car insurance cases in the area covered by the calculation, and calculate the weighted energy of the survey point based on the length of the second road and the number of second car insurance cases. Add the weighted energy of the points to obtain the first weighted energy sum;
  • the weighted energy of the survey point is calculated based on the position of the survey point after the movement, and the weighted energy of the survey point after the movement is added to the weighted energy of each survey point that has not moved. Second weighted energy sum;
  • the present application also provides a computer-readable storage medium on which a processing system is stored.
  • a processing system is executed by a processor, the steps of the optimization method of the above-mentioned survey grid are implemented.
  • the beneficial effect of the present application is that the present application is optimized based on the existing grid distribution of auto insurance surveys and surveys, which can cover a larger road length as much as possible under limited manpower conditions, taking into account the high incidence area of auto insurance cases, and giving full play to The initiative of surveyors makes the size of each auto insurance survey grid more reasonable, and the number of auto insurance cases is more evenly distributed, which greatly improves the efficiency of auto insurance surveys.
  • FIG. 1 is a schematic diagram of a hardware architecture of an embodiment of a server of the present application
  • Figure 2 is a schematic diagram of the sub-gravity and total gravitation of a certain survey point at each intersection;
  • FIG. 3 is a schematic flowchart of an embodiment of an optimization method for survey grids in this application.
  • the server 1 is a device capable of automatically performing numerical calculation and / or information processing according to an instruction set or stored in advance.
  • the server 1 may be a single network server, a server group composed of multiple network servers, or a cloud based on a cloud computing composed of a large number of hosts or network servers, where the cloud computing is a type of distributed computing and consists of a group of loosely coupled computers Set consisting of a super virtual computer.
  • the server 1 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13 that can be communicatively connected to each other through a system bus.
  • the memory 11 stores a processing system that can run on the processor 12. It should be noted that FIG. 1 only shows the server 1 with components 11-13, but it should be understood that it is not required to implement all the illustrated components, and more or fewer components may be implemented instead.
  • the memory 11 includes a memory and at least one type of readable storage medium.
  • the memory provides a cache for the operation of the server 1;
  • the readable storage medium may be, for example, a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), a random access memory (RAM), and a static random access memory (SRAM) ,
  • Non-volatile storage media such as read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc.
  • the readable storage medium may be an internal storage unit of the server 1, such as a hard disk of the server 1.
  • the non-volatile storage medium may also be an external storage device of the server 1, For example, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card (Flash card), etc. provided on the server 1.
  • the readable storage medium of the memory 11 is generally used to store an operating system and various types of application software installed on the server 1, for example, to store program code of a processing system in an embodiment of the present application.
  • the memory 11 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or another data processing chip.
  • the processor 12 is generally used to control the overall operations of the server 1, such as performing control and processing related to data interaction or communication with the other devices.
  • the processor 12 is configured to run program code or process data stored in the memory 11, for example, to run a processing system.
  • the network interface 13 may include a wireless network interface or a wired network interface.
  • the network interface 13 is generally used to establish a communication connection between the server 1 and other electronic devices.
  • the processing system is stored in the memory 11 and includes at least one computer-readable instruction stored in the memory 11, which can be executed by the processor 12 to implement the methods of the embodiments of the present application; and
  • the at least one computer-readable instruction may be divided into different logic modules according to different functions implemented by each part thereof.
  • each intersection in the area where no surveyor is equipped the first road length corresponding to the predetermined road connected to each intersection, the number of first auto insurance cases mapped to each intersection, and the corresponding distances of each intersection to each survey point, based on The first road length, the number of first car insurance cases, and the distance are used to calculate the sub-gravity of each intersection at each survey point, and the total gravitation of each intersection at each survey point is calculated based on each sub-gravity;
  • Each administrative district is equipped with a fixed survey point, and each survey point is equipped with an automobile insurance surveyor, so that the surveyor can investigate the automobile insurance cases in the administrative district.
  • an automobile insurance surveyor can also be used, for example, one auto insurance surveyor is configured in two administrative regions, and it is not too limited here.
  • each intersection In the area not equipped with a surveyor, that is, the area not covered by the surveyor, obtain each intersection and obtain the road length of the predetermined road connected by each intersection as the first road length, and the first road length can be directly through a map APP or the like. Get it.
  • intersections are, for example, three-way intersections, crossroads, etc.
  • the three-way intersections connect 3 or more roads (for example, there are overpasses to connect more than 3 roads), and the intersections connect 4 or more roads (for example, there are overpasses to connect 4 More than roads).
  • the predetermined roads connected by each intersection are: taking the intersection as the center, driving through a predetermined time (for example, 5 minutes) on each of the connected roads as the predetermined road; or, between two intersections At each intersection, one half of the length of the road is connected to itself as a part of the planned road.
  • obtaining the number of first auto insurance cases mapped to each intersection includes: obtaining the occurrence locations of historical auto insurance cases in the area where no surveyor is equipped, and mapping each historical auto insurance cases to Corresponding intersections, and the number of auto insurance cases at each intersection after mapping is calculated to obtain the number of first auto insurance cases.
  • the auto insurance case is mapped to the nearest intersection. For example, if the location of the auto insurance case is on a road between two intersections, the auto insurance case is mapped to the nearest intersection, and eventually to each intersection Car insurance cases are counted and accumulated to obtain the number of first car insurance cases at each intersection; or, the total number of car insurance cases within the area formed by the road that the car passes through at a predetermined time can be taken as the center of each intersection as the first of the intersection
  • the number of auto insurance cases is not limited here.
  • the distance between each intersection and each survey point can be directly obtained through a map app, etc.
  • the gravitational attraction of an intersection and an survey point reflects: the first road length corresponding to the predetermined road connected by the intersection, the first car insurance case Based on the number and the distance between the intersection and the survey point, the ability of the intersection to attract the survey point.
  • the first road length of the predetermined road connected by the intersection is longer, the number of first auto insurance cases is larger, and the distance from the survey point is smaller, the corresponding sub-gravity will be greater; the greater the sub-gravity, the survey point will be The stronger the tendency of the intersection to move, that is, after the survey point moves in the direction of the intersection, the survey scope and survey efficiency of the surveyor can be taken into account to optimize the survey.
  • the direction of the sub-gravity is the direction from the survey point to the intersection. The magnitude and direction of the sub-gravity of each intersection received by a survey point are different.
  • k1 is the first harmonic parameter, which is a value for reconciling the length of the road and the number of car insurance cases, and can be taken according to the actual conditions of the area.
  • the value of k1 ranges from 5 to 10.
  • the survey points are respectively subjected to the gravitational pull of intersections A, B, and C.
  • the gravitational pulls of the survey points at each intersection are added to obtain The magnitude and direction of the total gravitational force at the survey site.
  • the direction of total gravity is the direction of optimizing the position of the survey point, that is, after the survey point moves to the direction of the total gravity, the survey point can be placed in a better position, thereby improving the survey efficiency of the surveyor.
  • each survey point Before moving each survey point according to the direction of the corresponding total gravity and a preset distance, in the survey area where each survey point is located, obtain a driving center that is centered on the location of the survey point within a predetermined weighted driving time
  • the total length of the second road in the area covered, the number of second car insurance cases in the covered area is obtained, and the weighted energy of the survey point is calculated based on the length of the second road and the number of second car insurance cases. Adding the weighted energy to obtain a first weighted energy sum;
  • the value of the quantity can be determined according to the actual conditions of the area.
  • the value of k2 ranges from 200 to 300
  • the driving time is the time spent driving on the road in the survey area where the survey point is located.
  • the third auto insurance case The number is the number of auto insurance cases mapped to the road through which the car was driven.
  • Using the concept of weighted driving time it is possible to accurately assess the length of the roads surveyed by surveyors in a dense area of auto insurance cases within a specific time.
  • the number of third car insurance cases is obtained by allocating the number of car insurance cases at each intersection to a road connected to the intersection according to a preset allocation rule. Specifically, according to the occurrence location of historical auto insurance cases in the survey area where the survey point is located, map the historical auto insurance cases in the survey area to the nearest intersection, calculate the number of auto insurance cases at each intersection after mapping, and then average the number of auto insurance cases at the intersection. The number of third car insurance cases assigned to the road connected to the intersection is obtained.
  • the weighted energy of the survey point the length of the second road + k1 * the number of second car insurance cases
  • k1 is the above-mentioned first harmonic parameter
  • the weighted energy of the survey point can accurately assess the survey scope of the surveyor, which is related to the weighted driving time-consuming
  • the difference is that the weighted driving time is used to evaluate the survey scope of the surveyor online (road), and the weighted energy is used to assess the survey scope of the surveyor on the surface (area).
  • the weighted energies of all survey points are added to obtain a first weighted energy sum.
  • the weighted energy of the survey point is calculated based on the position of the survey point after the move, and the weighted energy of the survey point after the move is added to the weighted energy of each survey point that has not moved to obtain a second Weighted energy sum
  • the length of the road within the area covered by the driving within the predetermined weighted driving time is obtained, the number of auto insurance cases in the covered area is obtained, and based on the road length and The number of auto insurance cases calculates the weighted energy of the survey point.
  • the predetermined algorithm may be a simulated annealing algorithm, a genetic algorithm, or the like.
  • the simulated annealing algorithm is adopted to confirm whether the survey point is moved.
  • a survey point is randomly selected, and the survey point moves according to the direction of total gravity.
  • the number of times the survey point moves and the distance of each move are set in advance. For example, the number of times of the move is 10 times, and the distance of each move is 200 meters. .
  • the survey points that have not moved will be calculated in the same way as above.
  • the corresponding weighted energy is calculated, and then the weighted energy of the survey point that has not moved and the weighted energy of the survey point that has moved are added to obtain a second weighted energy sum.
  • the process of using the simulated annealing algorithm is an iterative process, and an iteration is generated after each survey point is moved.
  • the difference Q2 between the first weighted energy sum Q1 and the second weighted energy sum is calculated, a random number is generated, and an iteration result is calculated: M is the termination temperature of the simulated annealing algorithm, and preferably, M is 100.
  • M is the termination temperature of the simulated annealing algorithm, and preferably, M is 100.
  • the optimal position of the survey points can be obtained from the movement of multiple survey points, that is, to obtain the optimal solution. After confirming that the survey points have been moved to the optimal position, the grid distribution of the auto insurance survey is optimized.
  • FIG. 3 is a schematic flowchart of an embodiment of an optimization method for a survey grid of the present application.
  • the optimization method for a survey grid includes the following steps:
  • Step S1 Obtain each intersection in an area not equipped with a surveyor, the first road length corresponding to a predetermined road connected to each intersection, the number of first auto insurance cases mapped to each intersection, and the corresponding correspondence between each intersection and each survey point.
  • the distance based on the length of the first road, the number of first car insurance cases, and the distance, calculate the sub-gravity of each intersection at each survey point, and calculate the total gravitation of each intersection at each survey point based on each sub-gravity;
  • Each administrative district is equipped with a fixed survey point, and each survey point is equipped with an automobile insurance surveyor, so that the surveyor can investigate the automobile insurance cases in the administrative district.
  • an automobile insurance surveyor can also be used, for example, one auto insurance surveyor is configured in two administrative regions, and it is not too limited here.
  • each intersection In the area not equipped with a surveyor, that is, the area not covered by the surveyor, obtain each intersection and obtain the road length of the predetermined road connected by each intersection as the first road length, and the first road length can be directly through a map APP or the like. Get it.
  • intersections are, for example, three-way intersections, crossroads, etc.
  • the three-way intersections connect 3 or more roads (for example, there are overpasses to connect more than 3 roads), and the intersections connect 4 or more roads (for example, there are overpasses to connect 4 More than roads).
  • the predetermined roads connected by each intersection are: taking the intersection as the center, driving through a predetermined time (for example, 5 minutes) on each of the connected roads as the predetermined road; or, between two intersections At each intersection, one half of the length of the road is connected to itself as a part of the planned road.
  • obtaining the number of first auto insurance cases mapped to each intersection includes: obtaining the occurrence locations of historical auto insurance cases in the area where no surveyor is equipped, and mapping each historical auto insurance cases to Corresponding intersections, and the number of auto insurance cases at each intersection after mapping is calculated to obtain the number of first auto insurance cases.
  • the auto insurance case is mapped to the nearest intersection. For example, if the location of the auto insurance case is on a road between two intersections, the auto insurance case is mapped to the nearest intersection, and eventually to each intersection Car insurance cases are counted and accumulated to obtain the number of first car insurance cases at each intersection; or, the total number of car insurance cases within the area formed by the road that the car passes through at a predetermined time can be taken as the center of each intersection as the first of the intersection
  • the number of auto insurance cases is not limited here.
  • the distance between each intersection and each survey point can be directly obtained through a map app, etc.
  • the gravitational attraction of an intersection and an survey point reflects: the first road length corresponding to the predetermined road connected by the intersection, the first car insurance case Based on the number and the distance between the intersection and the survey point, the ability of the intersection to attract the survey point.
  • the first road length of the predetermined road connected by the intersection is longer, the number of first auto insurance cases is larger, and the distance from the survey point is smaller, the corresponding sub-gravity will be greater; the greater the sub-gravity, the survey point will be The stronger the tendency of the intersection to move, that is, after the survey point moves in the direction of the intersection, the survey scope and survey efficiency of the surveyor can be taken into account to optimize the survey.
  • the direction of the sub-gravity is the direction from the survey point to the intersection. The magnitude and direction of the sub-gravity of each intersection received by a survey point are different.
  • k1 is the first harmonic parameter, which is a value for reconciling the length of the road and the number of car insurance cases, and can be taken according to the actual conditions of the area.
  • the value of k1 ranges from 5 to 10.
  • the sub-gravity of each survey point at each intersection is added to obtain the magnitude and direction of the total gravity that the survey point receives.
  • the direction of total gravity is the direction of optimizing the position of the survey point, that is, after the survey point moves to the direction of the total gravity, the survey point can be placed in a better position, thereby improving the survey efficiency of the surveyor.
  • Step S2 Before moving each survey point according to the direction of the corresponding total gravity and a preset distance, in the survey area where each survey point is located, obtain a predetermined weighted driving time centered on the location of the survey point. The total length of the second road in the area covered by the internal driving, obtain the number of second car insurance cases in the area covered, and calculate the weighted energy of the survey point based on the length of the second road and the number of second car insurance cases. Sum the weighted energy of the survey points to obtain the first weighted energy sum;
  • the value of the quantity can be determined according to the actual conditions of the area.
  • the value of k2 ranges from 200 to 300
  • the driving time is the time spent driving on the road in the survey area where the survey point is located.
  • the third auto insurance case The number is the number of auto insurance cases mapped to the road through which the car was driven.
  • Using the concept of weighted driving time it is possible to accurately assess the length of the roads surveyed by surveyors in a dense area of auto insurance cases within a specific time.
  • the number of third car insurance cases is obtained by allocating the number of car insurance cases at each intersection to a road connected to the intersection according to a preset allocation rule. Specifically, according to the occurrence location of historical auto insurance cases in the survey area where the survey point is located, map the historical auto insurance cases in the survey area to the nearest intersection, calculate the number of auto insurance cases at each intersection after mapping, and then average the number of auto insurance cases at the intersection. The number of third car insurance cases assigned to the road connected to the intersection is obtained.
  • the weighted energy of the survey point the length of the second road + k1 * the number of second car insurance cases
  • k1 is the above-mentioned first harmonic parameter
  • the weighted energy of the survey point can accurately assess the survey scope of the surveyor, which is related to the weighted driving time-consuming
  • the difference is that the weighted driving time is used to evaluate the survey scope of the surveyor online (road), and the weighted energy is used to assess the survey scope of the surveyor on the surface (area).
  • the weighted energies of all survey points are added to obtain a first weighted energy sum.
  • Step S3 After a survey point moves according to a predetermined distance, calculate the weighted energy of the survey point based on the position of the survey point after the movement, and add the weighted energy of the survey point after the movement to the weighted energy of each survey point that has not moved. Get the second weighted energy sum;
  • step S4 based on the first weighted energy sum and the second weighted energy sum, and using a predetermined algorithm, it is confirmed whether to move the survey point as the new position of the survey point to optimize the grid distribution of the vehicle insurance survey.
  • the length of the road within the area covered by the driving within the predetermined weighted driving time is obtained, the number of auto insurance cases in the covered area is obtained, and based on the road length and The number of auto insurance cases calculates the weighted energy of the survey point.
  • the predetermined algorithm may be a simulated annealing algorithm, a genetic algorithm, or the like.
  • the simulated annealing algorithm is adopted to confirm whether the survey point is moved.
  • a survey point is randomly selected, and the survey point moves according to the direction of total gravity.
  • the number of times the survey point moves and the distance of each move are set in advance. For example, the number of times of the move is 10 times, and the distance of each move is 200 meters. .
  • the survey points that have not moved will be calculated in the same way as above.
  • the corresponding weighted energy is calculated, and then the weighted energy of the survey point that has not moved and the weighted energy of the survey point that has moved are added to obtain a second weighted energy sum.
  • the process of using the simulated annealing algorithm is an iterative process, and an iteration is generated after each survey point is moved.
  • the difference Q2 between the first weighted energy sum Q1 and the second weighted energy sum is calculated, a random number is generated, and an iteration result is calculated: M is the termination temperature of the simulated annealing algorithm, and preferably, M is 100.
  • M is the termination temperature of the simulated annealing algorithm, and preferably, M is 100.
  • the optimal position of the survey points can be obtained from the movement of multiple survey points, that is, to obtain the optimal solution. After confirming that the survey points have been moved to the optimal position, the grid distribution of the auto insurance survey is optimized.
  • the present application first calculates each intersection based on the predetermined road length, the number of auto insurance cases mapped to each intersection, and the distance between the intersection and the survey point.
  • the total gravity of the survey points and then each survey point moves in the direction of the total gravity, and calculates the number of survey points for each survey point based on the road length of the area covered by the driving within the predetermined weighted driving time and the number of auto insurance cases in the covered area.
  • Weighted energy and calculate the weighted energy sum of all survey points before moving and the weighted energy sum of after moving, use a predetermined algorithm to confirm whether to use the moved position as the new position of the survey point.
  • This application distributes the grid in the existing auto insurance survey It can be optimized on the basis of the optimization, which can cover as much road length as possible under the condition of limited manpower, and take into account the high incidence area of auto insurance cases, give full play to the initiative of investigators, and make the size of each auto insurance survey grid more reasonable. The number of cases is more evenly distributed, which greatly improves the efficiency of automobile insurance investigation.
  • the present application also provides a computer-readable storage medium on which a processing system is stored.
  • a processing system is executed by a processor, the steps of the optimization method of the above-mentioned survey grid are implemented.
  • the methods in the above embodiments can be implemented by means of software plus a necessary universal hardware platform, and of course, also by hardware, but in many cases the former is better.
  • Implementation Based on such an understanding, the technical solution of this application that is essentially or contributes to the existing technology can be embodied in the form of a software product that is stored in a storage medium (such as ROM / RAM, magnetic disk, The optical disc) includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the embodiments of the present application.
  • a terminal device which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.

Landscapes

  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

本申请涉及一种服务器、查勘网格的优化方法及存储介质，该方法包括：获取未配备查勘员的区域中的各路口、各路口连通的预定道路长度、映射至各路口的车险案件数量及各路口分别与各查勘点的对应的距离，计算未配备查勘员的路口分别对各查勘点的分引力及总引力；在移动查勘点前，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的道路长度，获取该覆盖的区域中的车险案件数量，并计算该查勘点的加权能量及第一加权能量总和；在有查勘点移动后，计算第二加权能量总和；利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置。本申请能够优化车险查勘网格的分布，提高车险查勘效率。

Description

服务器、查勘网格的优化方法及存储介质
优先权申明
本申请基于巴黎公约申明享有2018年06月01日递交的申请号为CN201810554261.6、名称为“服务器、查勘网格的优化方法及存储介质”中国专利申请的优先权，该中国专利申请的整体内容以参考的方式结合在本申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种服务器、查勘网格的优化方法及存储介质。
背景技术
目前，车险查勘网格一般基于查勘员经验，或者直接以行政区进行划分，例如，在某个位置设置查勘点并分配查勘员，该查勘员以该查勘点为中心，查勘一定范围内的区域的车险案件，各个查勘员查勘的区域形成车险查勘网格。这种方式过于简单固化，对于交通路况较复杂、交通事故较多的区域，以及交通路况较简单、交通事故较少的区域，车险查勘的情况完全不相同，现有的车险查勘网格分布不能充分发挥查勘员的能动性，导致车险查勘的效率低下。
发明内容
本申请的目的在于提供一种服务器、查勘网格的优化方法及存储介质，旨在优化车险查勘网格的分布，以提高车险查勘效率。
为实现上述目的，本申请提供一种服务器，所述服务器包括存储器及与所述存储器连接的处理器，所述存储器中存储有可在所述处理器上运行的处理系统，所述处理系统被所述处理器执行时实现如下步骤：
获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
为实现上述目的，本申请还提供一种查勘网格的优化方法，所述查勘网格的优化方法包括：
S1，获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
S2，在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权 驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
S3，在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
S4，基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有处理系统，所述处理系统被处理器执行时实现上述的查勘网格的优化方法的步骤。
本申请的有益效果是：本申请在现有的车险查勘网格分布的基础上进行优化，能够在有限的人力条件下，尽可能覆盖更大的道路长度，并兼顾车险案件高发区域，充分发挥查勘员的能动性，使得每个车险查勘网格的大小更合理，车险案件数量分布更均匀，极大提升了车险查勘效率。
附图说明
图1为本申请服务器一实施例的硬件架构的示意图；
图2为各路口对某一查勘点的分引力及总引力的示意图；
图3为本申请查勘网格的优化方法一实施例的流程示意图。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施 例仅用以解释本申请，并不用于限定本申请。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
需要说明的是，在本申请中涉及“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。
参阅图1所示，是本申请服务器一实施例的硬件架构的示意图，服务器1是一种能够按照事先设定或者存储的指令，自动进行数值计算和/或信息处理的设备。所述服务器1可以是单个网络服务器、多个网络服务器组成的服务器组或者基于云计算的由大量主机或者网络服务器构成的云，其中云计算是分布式计算的一种，由一群松散耦合的计算机集组成的一个超级虚拟计算机。
在本实施例中，服务器1可包括，但不仅限于，可通过系统总线相互通信连接的存储器11、处理器12、网络接口13，存储器11存储有可在处理器12上运行的处理系统。需要指出的是，图1仅示出了具有组件11-13的服务器1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。
其中，存储器11包括内存及至少一种类型的可读存储介质。内存为服务器1的运行提供缓存；可读存储介质可为如闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器 (EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等的非易失性存储介质。在一些实施例中，可读存储介质可以是服务器1的内部存储单元，例如该服务器1的硬盘；在另一些实施例中，该非易失性存储介质也可以是服务器1的外部存储设备，例如服务器1上配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。本实施例中，存储器11的可读存储介质通常用于存储安装于服务器1的操作系统和各类应用软件，例如存储本申请一实施例中的处理系统的程序代码等。此外，存储器11还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器12在一些实施例中可以是中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器12通常用于控制所述服务器1的总体操作，例如执行与所述其他设备进行数据交互或者通信相关的控制和处理等。本实施例中，所述处理器12用于运行所述存储器11中存储的程序代码或者处理数据，例如运行处理系统等。
所述网络接口13可包括无线网络接口或有线网络接口，该网络接口13通常用于在所述服务器1与其他电子设备之间建立通信连接。
所述处理系统存储在存储器11中，包括至少一个存储在存储器11中的计算机可读指令，该至少一个计算机可读指令可被处理器器12执行，以实现本申请各实施例的方法；以及，该至少一个计算机可读指令依据其各部分所实现的功能不同，可被划为不同的逻辑模块。
在一实施例中，上述处理系统被所述处理器12执行时实现如下步骤：
获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路 口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
在车险查勘的配置中，可以以行政区为基本单位，每一行政区配置一个固定的查勘点，每个查勘点配置一个车险查勘员，以便该查勘员对该行政区中发生的车险案件进行查勘。当然，也可以采用其他的区域划分及查勘员配备的方法，例如两个行政区配置一个车险查勘员等，此处不做过多限定。
在未配备查勘员的区域中，即查勘员未覆盖的区域，获取各个路口并获取各个路口所连通的预定道路的道路长度作为第一道路长度，该第一道路长度可以通过地图APP等方式直接获取到。
其中，路口例如为三叉路口、十字路口等，三叉路口连通3条及以上的道路(例如有立交桥情况连通3条以上的道路)、十字路口连通4条及以上的道路(例如有立交桥情况连通4条以上的道路)。每个路口所连通的预定道路为：以该路口为中心，在其每一所连通的道路上驾车预设时间(例如5分钟)所通过的道路，作为预定道路；或者，两两路口之间的道路，每一路口各取该道路一半长度的、与自身连通的该道路，作为一部分预定道路。
在一实施例中，获取映射至各路口的第一车险案件数量包括：获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量，得到第一车险案件数量。
具体地，将该车险案件映射至距离最近的路口，例如，如果车险案件的发生位置处于两个路口之间的道路上，则该车险案件映射至距离最近的路口，最终将映射至每一路口的车险案件进行统计累加，得到每一路口的第一车险案件数量；或者，可以以每个路口为中心，预定时间驾车所通过的道路所形成区域范围内的车险案件的总数作为路口的第一车险案件数量等，此处不做过多限定。
其中，各路口与每一查勘点的距离可以直接通过地图APP等方式获取到，一个路口与一个查勘点的分引力体现了：以路口连通的预定道路对应的第一道路长度、第一车险案件数量及路口与查勘点之间的距离为基础，该路口对该查勘点的吸引能力。若该路口连通的预定道路的第一道路长度越长、第一车险案件数量越大、其与查勘点的距离越小，则对应的分引力越大；分引力越大，则该查勘点向该路口的方向移动的趋势越强，即该查勘点向该路口的方向移动后，能够兼顾查勘员的查勘范围及查勘效率，以得到查勘的优化。分引力的方向为由查勘点指向路口的方向，一个查勘点所受到的各个路口的分引力的大小、方向各不相同。
在一实施例中，基于该第一道路长度、第一车险案件数量及距离计算各路口对各查勘点的分引力包括：分引力f＝(第一道路长度+k1*第一车险案件数量)/距离。其中，k1为第一调和参数，为调和道路长度和车险案件数量的数值，可根据区域的实际情况取值，优选地，k1的取值范围为5至10。
对于其中的某一查勘点，如图2所示，查勘点分别受到路口A、B、C的分引力，基于向量相加的原理，将各个路口对该查勘点的分引力进行相加，得到该查勘点受到的总引力的大小及方向。总引力的方向为优化查勘点的位置的方向，即查勘点向总引力的方向移动后，能够使得查勘点处于较优位置，从而提高查勘员的查勘效率。
在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
在一实施例中，加权驾车耗时为以查勘点的位置为中心、在该查勘点所在的查勘区的道路上驾车耗时及映射至驾车所经的道路的第三车险案件数 量计算得到，反映了在该查勘区发生的车险案件对在查勘区驾车所用时间的影响，加权驾车耗时＝驾车耗时+k2*第三车险案件数量，k2为第二调和参数，为调和时间和车险案件数量的数值，可根据区域的实际情况取值，优选地，k2的取值范围为200至300，驾车耗时为在该查勘点所在的查勘区域的道路驾车所消耗的时间，第三车险案件数量为映射至驾车所经的该道路的车险案件的数量。加权驾车耗时越大则道路越密集且发生的车险案件数量越大，利用加权驾车耗时这一概念，能够准确评估查勘员在车险案件密集区域在特定时间内的查勘的道路长度。
其中，第三车险案件数量为按照预设的分配规则将映射后各路口的车险案件数量分配至与该路口相连的道路得到。具体地，按照该查勘点所在的查勘区的历史车险案件的发生位置将该查勘区的历史车险案件映射至最近的路口，统计得到映射后各路口的车险案件数量，然后将路口车险案件数量平均分配给该路口连通的道路，得到该道路的第三车险案件数量。
其中，查勘点的加权能量＝第二道路长度+k1*第二车险案件数量，k1为上述的第一调和参数，查勘点的加权能量可以准确评估查勘员的查勘范围，其与加权驾车耗时不同的是：加权驾车耗时用以评估查勘员在线上(道路)的查勘范围，加权能量用以评估查勘员在面上(区域)的查勘范围。将所有查勘点的加权能量相加得到第一加权能量总和。
在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
对于移动后的查勘点，以移动后的位置为中心，获取在预定的加权驾车 耗时内驾车所覆盖的区域内道路长度，获取该覆盖的区域中的车险案件数量，并基于该道路长度及车险案件数量计算该查勘点的加权能量。
本实施例中，预定算法可以是模拟退火算法、遗传算法等，优选地，采用模拟退火算法确认是否移动查勘点。
具体地，随机选择一个查勘点，该查勘点按照总引力方向进行移动，预先设定查勘点移动的次数和每次移动的距离，例如移动的次数为10次，每次移动的距离为200米。在有查勘点移动一次后，未发生移动的查勘点，加权能量的计算方式和上述方式一样。对于发生移动的那个查勘点，计算其对应的加权能量，然后将未发生移动的查勘点的加权能量与发生移动的查勘点的加权能量相加得到第二加权能量总和。
其中，利用模拟退火算法的过程就是一个不断迭代的过程，查勘点每次移动后就产生一次迭代。具体地，计算第一加权能量总和Q1及第二加权能量总和的差值Q2，生成随机数，计算一个迭代结果：
Figure PCTCN2018102106-appb-000001
M为模拟退火算法的终止温度，优选地，M为100。将该迭代结果与生成的随机数比较，如果迭代结果大于该随机数，则确认将该查勘点移动后的位置作为该查勘点新的位置；如果迭代结果不大于该随机数，则确认该查勘点不进行移动，仍位于原来的位置。从整体上来看，所有的查勘点都有往未配备查勘员的区域移动的趋势。
由于查勘点的数量有多个，且每个查勘点移动的次数为多次，因此基于模拟退火算法能够从多个查勘点的移动过程中获取到查勘点的最优位置，即获取最优解的方式，确认将查勘点移动至最优的位置后，以优化车险查勘网格分布。
如图3所示，图3为本申请查勘网格的优化方法一实施例的流程示意图，该查勘网格的优化方法包括以下步骤：
步骤S1，获取未配备查勘员的区域中的各路口、各路口所连通的预定 道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
在车险查勘的配置中，可以以行政区为基本单位，每一行政区配置一个固定的查勘点，每个查勘点配置一个车险查勘员，以便该查勘员对该行政区中发生的车险案件进行查勘。当然，也可以采用其他的区域划分及查勘员配备的方法，例如两个行政区配置一个车险查勘员等，此处不做过多限定。
在未配备查勘员的区域中，即查勘员未覆盖的区域，获取各个路口并获取各个路口所连通的预定道路的道路长度作为第一道路长度，该第一道路长度可以通过地图APP等方式直接获取到。
其中，路口例如为三叉路口、十字路口等，三叉路口连通3条及以上的道路(例如有立交桥情况连通3条以上的道路)、十字路口连通4条及以上的道路(例如有立交桥情况连通4条以上的道路)。每个路口所连通的预定道路为：以该路口为中心，在其每一所连通的道路上驾车预设时间(例如5分钟)所通过的道路，作为预定道路；或者，两两路口之间的道路，每一路口各取该道路一半长度的、与自身连通的该道路，作为一部分预定道路。
在一实施例中，获取映射至各路口的第一车险案件数量包括：获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量，得到第一车险案件数量。
具体地，将该车险案件映射至距离最近的路口，例如，如果车险案件的发生位置处于两个路口之间的道路上，则该车险案件映射至距离最近的路口，最终将映射至每一路口的车险案件进行统计累加，得到每一路口的第一车险案件数量；或者，可以以每个路口为中心，预定时间驾车所通过的道路 所形成区域范围内的车险案件的总数作为路口的第一车险案件数量等，此处不做过多限定。
其中，各路口与每一查勘点的距离可以直接通过地图APP等方式获取到，一个路口与一个查勘点的分引力体现了：以路口连通的预定道路对应的第一道路长度、第一车险案件数量及路口与查勘点之间的距离为基础，该路口对该查勘点的吸引能力。若该路口连通的预定道路的第一道路长度越长、第一车险案件数量越大、其与查勘点的距离越小，则对应的分引力越大；分引力越大，则该查勘点向该路口的方向移动的趋势越强，即该查勘点向该路口的方向移动后，能够兼顾查勘员的查勘范围及查勘效率，以得到查勘的优化。分引力的方向为由查勘点指向路口的方向，一个查勘点所受到的各个路口的分引力的大小、方向各不相同。
在一实施例中，基于该第一道路长度、第一车险案件数量及距离计算各路口对各查勘点的分引力包括：分引力f＝(第一道路长度+k1*第一车险案件数量)/距离。其中，k1为第一调和参数，为调和道路长度和车险案件数量的数值，可根据区域的实际情况取值，优选地，k1的取值范围为5至10。
对于其中的某一查勘点，如图2所示，基于向量相加的原理，将各个路口对该查勘点的分引力进行相加，得到该查勘点受到的总引力的大小及方向。总引力的方向为优化查勘点的位置的方向，即查勘点向总引力的方向移动后，能够使得查勘点处于较优位置，从而提高查勘员的查勘效率。
步骤S2，在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
在一实施例中，加权驾车耗时为以查勘点的位置为中心、在该查勘点所在的查勘区的道路上驾车耗时及映射至驾车所经的道路的第三车险案件数量计算得到，反映了在该查勘区发生的车险案件对在查勘区驾车所用时间的影响，加权驾车耗时＝驾车耗时+k2*第三车险案件数量，k2为第二调和参数，为调和时间和车险案件数量的数值，可根据区域的实际情况取值，优选地，k2的取值范围为200至300，驾车耗时为在该查勘点所在的查勘区域的道路驾车所消耗的时间，第三车险案件数量为映射至驾车所经的该道路的车险案件的数量。加权驾车耗时越大则道路越密集且发生的车险案件数量越大，利用加权驾车耗时这一概念，能够准确评估查勘员在车险案件密集区域在特定时间内的查勘的道路长度。
其中，第三车险案件数量为按照预设的分配规则将映射后各路口的车险案件数量分配至与该路口相连的道路得到。具体地，按照该查勘点所在的查勘区的历史车险案件的发生位置将该查勘区的历史车险案件映射至最近的路口，统计得到映射后各路口的车险案件数量，然后将路口车险案件数量平均分配给该路口连通的道路，得到该道路的第三车险案件数量。
其中，查勘点的加权能量＝第二道路长度+k1*第二车险案件数量，k1为上述的第一调和参数，查勘点的加权能量可以准确评估查勘员的查勘范围，其与加权驾车耗时不同的是：加权驾车耗时用以评估查勘员在线上(道路)的查勘范围，加权能量用以评估查勘员在面上(区域)的查勘范围。将所有查勘点的加权能量相加得到第一加权能量总和。
步骤S3，在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
步骤S4，基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化 车险查勘网格分布。
对于移动后的查勘点，以移动后的位置为中心，获取在预定的加权驾车耗时内驾车所覆盖的区域内道路长度，获取该覆盖的区域中的车险案件数量，并基于该道路长度及车险案件数量计算该查勘点的加权能量。
本实施例中，预定算法可以是模拟退火算法、遗传算法等，优选地，采用模拟退火算法确认是否移动查勘点。
具体地，随机选择一个查勘点，该查勘点按照总引力方向进行移动，预先设定查勘点移动的次数和每次移动的距离，例如移动的次数为10次，每次移动的距离为200米。在有查勘点移动一次后，未发生移动的查勘点，加权能量的计算方式和上述方式一样。对于发生移动的那个查勘点，计算其对应的加权能量，然后将未发生移动的查勘点的加权能量与发生移动的查勘点的加权能量相加得到第二加权能量总和。
其中，利用模拟退火算法的过程就是一个不断迭代的过程，查勘点每次移动后就产生一次迭代。具体地，计算第一加权能量总和Q1及第二加权能量总和的差值Q2，生成随机数，计算一个迭代结果：
Figure PCTCN2018102106-appb-000002
M为模拟退火算法的终止温度，优选地，M为100。将该迭代结果与生成的随机数比较，如果迭代结果大于该随机数，则确认将该查勘点移动后的位置作为该查勘点新的位置；如果迭代结果不大于该随机数，则确认该查勘点不进行移动，仍位于原来的位置。从整体上来看，所有的查勘点都有往未配备查勘员的区域移动的趋势。
由于查勘点的数量有多个，且每个查勘点移动的次数为多次，因此基于模拟退火算法能够从多个查勘点的移动过程中获取到查勘点的最优位置，即获取最优解的方式，确认将查勘点移动至最优的位置后，以优化车险查勘网格分布。
与现有技术相比，本申请首先基于未配备查勘员的区域中的各路口连通 的预定道路长度、映射至各路口的车险案件数量、路口与查勘点的距离，来计算各路口分别对各查勘点的总引力，然后，各查勘点按照总引力的方向进行移动，基于预定的加权驾车耗时内驾车所覆盖的区域的道路长度、覆盖的区域中的车险案件数量计算每一查勘点的加权能量，并计算移动前所有查勘点的加权能量总和及移动后的加权能量总和，利用预定算法确认是否将移动后的位置作为查勘点新的位置，本申请在现有的车险查勘网格分布的基础上进行优化，能够在有限的人力条件下，尽可能覆盖更大的道路长度，并兼顾车险案件高发区域，充分发挥查勘员的能动性，使得每个车险查勘网格的大小更合理，车险案件数量分布更均匀，极大提升了车险查勘效率。
本申请还提供一种计算机可读存储介质，所述计算机可读存储介质上存储有处理系统，所述处理系统被处理器执行时实现上述的查勘网格的优化方法的步骤。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种服务器，其特征在于，所述服务器包括存储器及与所述存储器连接的处理器，所述存储器中存储有可在所述处理器上运行的处理系统，所述处理系统被所述处理器执行时实现如下步骤：
    获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
    在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
    在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
    基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
  2. 根据权利要求1所述的服务器，其特征在于，所述查勘点的分引力＝(第一道路长度+k1*第一车险案件数量)/距离，k1为第一调和参数；
    所述加权驾车耗时＝驾车耗时+k2*第三车险案件数量，k2为第二调和参数，所述驾车耗时为在该查勘点所在区域的道路驾车所消耗的时间，所述第三车险案件数量为映射至驾车所经的该道路的车险案件的数量；
    所述查勘点的加权能量＝第二道路长度+k1*第二车险案件数量。
  3. 根据权利要求1所述的服务器，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  4. 根据权利要求3所述的服务器，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
  5. 根据权利要求2所述的服务器，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  6. 根据权利要求5所述的服务器，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
  7. 根据权利要求2所述的查勘网格的优化方法，其特征在于，所述预设的分配规则包括：按照查勘点所在的查勘区的历史车险案件的发生位置将该各历史车险案件映射至最近的路口，统计得到映射后各路口的车险案件数量，将每一路口车险案件数量平均分配给该路口连通的道路，得到该第三车险案件数量
  8. 一种查勘网格的优化方法，其特征在于，所述查勘网格的优化方法包括：
    S1，获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
    S2，在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
    S3，在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
    S4，基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
  9. 根据权利要求8所述的查勘网格的优化方法，其特征在于，所述查勘点的分引力＝(第一道路长度+k1*第一车险案件数量)/距离，k1为第一调和参 数；
    所述加权驾车耗时＝驾车耗时+k2*第三车险案件数量，k2为第二调和参数，所述驾车耗时为在该查勘点所在区域的道路驾车所消耗的时间，所述第三车险案件数量为按照预设的规则映射至驾车所经的该道路的车险案件的数量；
    所述查勘点的加权能量＝第二道路长度+k1*第二车险案件数量。
  10. 根据权利要求8所述的查勘网格的优化方法，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  11. 根据权利要求10所述的查勘网格的优化方法，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
  12. 根据权利要求9所述的查勘网格的优化方法，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  13. 根据权利要求12所述的查勘网格的优化方法，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
  14. 根据权利要求9所述的查勘网格的优化方法，其特征在于，所述预设的分配规则包括：按照查勘点所在的查勘区的历史车险案件的发生位置将该各历史车险案件映射至最近的路口，统计得到映射后各路口的车险案件数量，将每一路口车险案件数量平均分配给该路口连通的道路，得到该第三车险案件数量。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有处理系统，所述处理系统被处理器执行时实现步骤：
    获取未配备查勘员的区域中的各路口、各路口所连通的预定道路对应的第一道路长度、映射至各路口的第一车险案件数量以及各路口分别与各查勘点的对应的距离，基于该第一道路长度、第一车险案件数量及距离计算各路口分别对各查勘点的分引力，基于各分引力计算各路口对各查勘点的总引力；
    在将各查勘点按照对应的总引力的方向及预设距离进行移动前，在每一查勘点所在的查勘区域中，获取以查勘点的位置为中心、在预定的加权驾车耗时内驾车所覆盖的区域内总的第二道路长度，获取该覆盖的区域中的第二车险案件数量，并基于该第二道路长度及第二车险案件数量计算该查勘点的加权能量，将所有查勘点的加权能量相加得到第一加权能量总和；
    在有查勘点按照预定距离移动后，基于移动后查勘点的位置计算该查勘点的加权能量，将移动后的查勘点的加权能量与未发生移动的各查勘点的加权能量相加得到第二加权能量总和；
    基于所述第一加权能量总和、所述第二加权能量总和并利用预定算法确认是否将该查勘点移动后的位置作为该查勘点新的位置，以优化车险查勘网格分布。
  16. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述查勘点的分引力＝(第一道路长度+k1*第一车险案件数量)/距离，k1为第一调和参数；
    所述加权驾车耗时＝驾车耗时+k2*第三车险案件数量，k2为第二调和参数，所述驾车耗时为在该查勘点所在区域的道路驾车所消耗的时间，所述第三车险案件数量为映射至驾车所经的该道路的车险案件的数量；
    所述查勘点的加权能量＝第二道路长度+k1*第二车险案件数量。
  17. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  18. 根据权利要求17所述的计算机可读存储介质，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
  19. 根据权利要求16所述的计算机可读存储介质，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤包括：
    获取未配备查勘员的区域中的历史车险案件的发生位置，基于各历史车 险案件的发生位置将各历史车险案件映射至对应的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量。
  20. 根据权利要求19所述的计算机可读存储介质，其特征在于，所述获取映射至各路口的第一车险案件数量的步骤进一步包括：
    基于各历史车险案件的发生位置将各历史车险案件映射至距离最近的路口，并统计得到映射后各路口的车险案件数量作为第一车险案件数量；或者
    以路口为中心，获取在预定时间内驾车通过的道路所形成的区域范围内车险案件数量作为第一车险案件数量。
PCT/CN2018/102106 2018-06-01 2018-08-24 服务器、查勘网格的优化方法及存储介质 WO2019227709A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201810554261.6A CN108959389B (zh) 2018-06-01 2018-06-01 服务器、查勘网格的优化方法及存储介质
CN201810554261.6 2018-06-01

Publications (1)

Publication Number Publication Date
WO2019227709A1 true WO2019227709A1 (zh) 2019-12-05

Family

ID=64492857

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/102106 WO2019227709A1 (zh) 2018-06-01 2018-08-24 服务器、查勘网格的优化方法及存储介质

Country Status (2)

Country Link
CN (1) CN108959389B (zh)
WO (1) WO2019227709A1 (zh)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160171521A1 (en) * 2007-05-10 2016-06-16 Allstate Insurance Company Road segment safety rating system
US20160189307A1 (en) * 2007-05-10 2016-06-30 Allstate Insurance Company Route Risk Mitigation
CN106408147A (zh) * 2016-01-25 2017-02-15 平安科技（深圳）有限公司 车险查勘任务的派发方法及系统
CN106997501A (zh) * 2016-01-25 2017-08-01 平安科技（深圳）有限公司 基于车险查勘的位置获取方法及系统
CN107798619A (zh) * 2017-05-05 2018-03-13 平安科技（深圳）有限公司 查勘任务分配方法、系统、服务器和介质
CN107844851A (zh) * 2017-09-30 2018-03-27 平安科技（深圳）有限公司 查勘网格优化方法、电子设备及计算机可读存储介质

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1505547B1 (en) * 2003-08-05 2010-12-22 Geotechnos S.R.L. Method for surveying, processing and making use of data concerning the topographic conformation and the morphology of land and road network
US20080114612A1 (en) * 2006-11-13 2008-05-15 Needham Frank A Responder route and site-specific critical data system
CN107862030B (zh) * 2017-11-01 2020-07-31 平安科技（深圳）有限公司 出险待命位置确定方法、装置、计算机设备和存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160171521A1 (en) * 2007-05-10 2016-06-16 Allstate Insurance Company Road segment safety rating system
US20160189307A1 (en) * 2007-05-10 2016-06-30 Allstate Insurance Company Route Risk Mitigation
CN106408147A (zh) * 2016-01-25 2017-02-15 平安科技（深圳）有限公司 车险查勘任务的派发方法及系统
CN106997501A (zh) * 2016-01-25 2017-08-01 平安科技（深圳）有限公司 基于车险查勘的位置获取方法及系统
CN107798619A (zh) * 2017-05-05 2018-03-13 平安科技（深圳）有限公司 查勘任务分配方法、系统、服务器和介质
CN107844851A (zh) * 2017-09-30 2018-03-27 平安科技（深圳）有限公司 查勘网格优化方法、电子设备及计算机可读存储介质

Also Published As

Publication number Publication date
CN108959389A (zh) 2018-12-07
CN108959389B (zh) 2023-03-14

Similar Documents

Publication Publication Date Title
US11592305B2 (en) Method, system, terminal, and storage medium for rapid generation of reference lines
CN104683405B (zh) 在车联网中集群服务器分发地图匹配任务的方法和装置
CN103250031B (zh) 路线选择系统、路线选择方法和路线选择程序
CN110111599B (zh) 一种基于大数据的停车诱导方法、终端设备及存储介质
US20160314554A1 (en) Evacuation plan design
WO2020024406A1 (zh) 电子装置、基于路况因子的车险查勘调度方法及存储介质
CN112329789B (zh) 点云的提取方法、装置、计算机设备和存储介质
CN108052279A (zh) 一种提升闪存性能的方法、装置、设备及存储介质
CN111445472A (zh) 激光点云地面分割方法、装置、计算设备及存储介质
CN111707279A (zh) 一种激光点云与地图的匹配评价方法、介质、终端和装置
CN109920269A (zh) 一种停车场虚拟车位管理系统和装置
CN110991710A (zh) 为能源补给站确定布置位置的方法及相关设备
WO2019227709A1 (zh) 服务器、查勘网格的优化方法及存储介质
CN112215864A (zh) 电子地图的轮廓处理方法、装置及电子设备
JP2017049954A (ja) 推定装置、推定方法及びプログラム
CN113886413A (zh) 地图更新方法及装置
CN111586160B (zh) 数据处理方法、装置、数据处理设备及存储介质
CN113807555B (zh) 分拨中心的选址方法、装置、电子设备及存储介质
CN111352924B (zh) 解决数据倾斜问题的方法及装置
CN113859029A (zh) 一种运营商充电设施接入运行情况的监控方法
CN112000611A (zh) 图数据划分方法、处理方法及电子设备
CN109615187A (zh) 一种od矩阵的评估方法、公交负载模拟方法及装置
CN111861757A (zh) 一种融资匹配方法、系统、设备和存储介质
CN110675648B (zh) 停车场数据源采集及数据去重采集的方法及系统、服务器
Andersen et al. An open-source ITS platform

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18920469

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 29.03.2021.)

122 Ep: pct application non-entry in european phase

Ref document number: 18920469

Country of ref document: EP

Kind code of ref document: A1