CN111161443A - Patrol path setting method based on historical data - Google Patents
Patrol path setting method based on historical data Download PDFInfo
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- CN111161443A CN111161443A CN201910044159.6A CN201910044159A CN111161443A CN 111161443 A CN111161443 A CN 111161443A CN 201910044159 A CN201910044159 A CN 201910044159A CN 111161443 A CN111161443 A CN 111161443A
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Abstract
The invention provides a patrol path setting method based on historical data, which relates to the field of computers and comprises the following steps of 1: clustering analysis is carried out on historical data, an aggregation area of a historical event occurrence place is marked, and an optimal patrol area is selected by utilizing a spatial relationship; step 2: predicting event time of future time periods of each patrol area; and step 3: acquiring the place and time to be patrolled in the future time period; and 4, step 4: and setting an optimal patrol path by using a multi-objective heuristic optimization model based on the time and the place needing patrol. The invention carries out patrol path comprehensive planning by considering the relevance of danger and time, and can reasonably distribute police force in the police force scheduling deployment and patrol process of the police.
Description
Technical Field
The invention relates to the field of computers, in particular to a patrol path setting method based on historical data.
Background
In the police dispatching deployment and patrol process of the police, it is an important requirement to reasonably distribute the police and plan a proper patrol path.
Planning and optimizing a path is a traditional problem in the computer field, and both hamilton path planning for solving the grooming problem and euler path planning for similar stroke problems have been studied for hundreds of years. Path selection, or routing, is widely used in the field of computer network message transmission, and generally plans a routing table according to network transmission on-off information, and forwards a message to a specific router or switch, and the router or switch queries the routing table according to the destination of the message and then forwards the message directly, for example, patent application CN201710090957.3 assists in selecting a path according to the state change of the path.
Police dispatch deployment and path planning based on police officer capability models and historical information analysis are still less studied. Some related researches and inventions relate to police officer path planning and selection, for example, patent application CN201610861306.5 uses a near field communication device or a mobile phone to trace a patrol path of a police officer, stores the collected data in a Hadoop-based big data platform, and uses a deep learning method to generate a patrol model; and the patent application CN201710667868.0 utilizes a deep learning method to identify high-risk areas and then to run through the high-risk areas and the dangerous areas when planning patrol paths.
Furthermore, there are also methods to help patrolmen select patrolling paths in a probabilistic way, i.e. each time a path selection is reached, by which the path selection is assisted, such as the patent applications CN 201210174425.5; the historical researches and inventions do not consider the management relationship between the capability model of the policeman and the capability requirement required by patrol, and emphasize the problem of patrol path selection after patrol is determined; whereas in practice police dispatch consists of two parts: the first part is that reasonably arranged personnel participate in patrol; the second part is to plan the appropriate path for the personnel involved in the patrol and to be able to achieve the appropriate coverage. The ability model of the police officer needs to consider whether the skill of the police officer is matched with the requirement, for example, when the personnel walk patrol and the vehicle patrol are matched, the former group of personnel need to have the gun holding ability, and the latter group of personnel need to have the driving ability; meanwhile, the historical attendance of the personnel needs to be considered, for example, if the police have been on for three consecutive days, the attendance of the police should be reduced within a period of time; in the planning of patrol, the coverage rate is an important factor, and in the patent application CN201710667868.0, mainly aiming at the coverage of high-risk areas, base layer units such as the same are generally dispatched to pay more attention to slight public security cases, and all areas in the jurisdiction need to be covered; finally, not all times in high-risk areas are high-risk, and current research does not consider the relevance of risk and time.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a patrol route setting method based on historical data, which can perform comprehensive patrol route planning in consideration of the relevance between danger and time, and can reasonably distribute police force.
The invention provides a patrol path setting method based on historical data, which comprises the following steps:
step 1: clustering analysis is carried out on historical data, an aggregation area of a historical event occurrence place is marked, and an optimal patrol area is selected by utilizing a spatial relationship;
step 2: predicting event time of future time periods of each patrol area;
and step 3: acquiring the place and time to be patrolled in the future time period;
and 4, step 4: and setting an optimal patrol path by using a multi-objective heuristic optimization model based on the time and the place needing patrol.
Further, the step 1 specifically comprises the following steps:
step 1.1: the position information of the historical event is taken out, the gathering area of the historical event occurrence place is marked, and the optimal patrol area is obtained through cluster analysis by a cluster analysis model according to the set cluster quantity proportion;
step 1.2: calculating the interval distance between patrol areas obtained by adopting each clustering analysis model, and selecting the clustering analysis model with the best clustering effect;
step 1.3: calculating the distance between the central points of the patrol areas obtained by each cluster analysis model, and adjusting the number of the gathering areas according to the distance;
step 1.4: and classifying all historical events according to the cluster analysis model with the best effect, and classifying the historical events into the most appropriate patrol area.
Further, the cluster analysis model comprises Kmeans, Kmeans + +, Affinity Propagation.
Further, the step 2 specifically comprises the following steps:
step 2.1: taking out the number of historical events in each patrol area, summarizing according to time periods, and filling zero in unrecorded time periods;
step 2.2: generating a training set and a test set from the historical events and the corresponding time data by a sliding window method;
step 2.3: training and predicting a time sequence model by using a grid search, a random search or a Bayesian optimization algorithm in combination with test set and training set data, and selecting an optimal time sequence model and parameters;
step 2.4: and predicting the events of the future time period by using the obtained time series model.
Further, the time series model comprises AR, ARIMA and LSTM.
Further, the step 3 specifically comprises the following steps:
step 3.1: selecting the mass center of each patrol area as a place to be patrolled;
step 3.2: and setting a certain threshold value according to a prediction result obtained by the time series model, wherein the time periods larger than the threshold value need to be patrolled, and further obtaining the time periods needing to be patrolled.
Further, the step 4 specifically includes:
step 4.1: setting an optimal patrol path through a multi-objective heuristic optimization model according to the time period and the place needing patrol, namely acquiring the shortest path corresponding to each hour in the future time period, the shortest connecting path corresponding to the starting point and the ending point of each hour, and the shortest total path from the patrol starting point to the patrol ending point;
further, the multi-objective heuristic optimization model comprises a genetic algorithm GA, a differential evolution algorithm DE and NSGA-III.
As described above, the patrol route setting method based on historical data according to the present invention has the following beneficial effects: the patrol path planning is carried out by comprehensively considering the relevance of the risk and the time, and the police force can be reasonably distributed in the police force scheduling deployment and patrol processes of the police.
Drawings
Fig. 1 is a flowchart showing a patrol route setting method disclosed in the embodiment of the present invention;
fig. 2 is a block diagram of a patrol path setting system disclosed in the embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
As shown in fig. 1, the present invention provides a patrol path setting method based on historical data, the method comprising the steps of:
step 1: clustering analysis is carried out on historical data, an aggregation area of a historical event occurrence place is marked, and an optimal patrol area is selected by utilizing a spatial relationship;
the method comprises the following specific steps:
step 1.1: taking out position information (such as longitude and latitude) of the historical event, marking a gathering area of a historical event occurrence place, and carrying out cluster analysis through a cluster analysis model according to a set cluster quantity proportion to obtain an optimal patrol area, wherein the cluster analysis model comprises Kmeans, Kmeans + +, Affinity prediction and the like;
step 1.2: calculating the interval distance between patrol areas obtained by adopting each clustering analysis algorithm model, and selecting a clustering analysis model with the best clustering effect;
step 1.3: calculating the distance between the central points of the patrol areas obtained by each cluster analysis model, wherein the minimum value of the obtained distance is larger than a set value, if the minimum value is too large, the number of the gathering areas is reduced, if the minimum value is too small, the number of the gathering areas is increased until the condition is met, and the set value is an adjustable parameter;
step 1.4: classifying all historical events according to the clustering analysis model with the best effect, and classifying the historical events into the most appropriate patrol area;
the historical events are information concerned by patrols, for example, for police, the historical events are form cases or public security cases.
Step 2: predicting event time of future time periods of each patrol area;
the method comprises the following specific steps:
step 2.1: taking out the number of historical events in each patrol area, summarizing according to time periods, and filling zero in unrecorded time periods;
step 2.2: generating a training set and a test set from the historical events and the corresponding time data by a sliding window method;
step 2.3: training and predicting a time sequence model by using a grid search, a random search or a Bayesian optimization algorithm in combination with test set and training set data, and selecting an optimal time sequence model and parameters, wherein the time sequence model comprises AR, ARIMA, LSTM and the like;
step 2.4: predicting events of a future time period, such as 24 hours in the future, by using the obtained time series model;
when the historical events and the corresponding time data are less, the AIC or BIC indexes are directly used for selecting the time series model, the sliding window method aims at more historical data, for example, from T0-T9, the window size can be 3, then T0-T2 are data, T1-T3 are data, and so on, the data can be used for training from T6-T8, then whether the data of T7-T9 are matched with the data trained in the previous step is judged, if the data are too few, the training set and the test set cannot exist, and then the AIC or BIC indexes are directly judged to determine which time series model is most matched.
And step 3: acquiring the place and time to be patrolled in the future time period;
step 3.1: selecting the mass center of each patrol area as a place to be patrolled;
step 3.2: setting a certain threshold value according to a prediction result obtained by the time series model, wherein the time periods larger than the threshold value need to be patrolled, and further obtaining the time periods needing to be patrolled;
and 4, step 4: and setting the optimal patrol path by using the multi-objective heuristic optimization model.
Setting an optimal patrol path through a multi-objective heuristic optimization model according to the time period and the place needing patrol, namely acquiring the shortest path corresponding to each hour in the future time period, the shortest connecting path corresponding to the starting point and the ending point of each hour, and the shortest total path from the patrol starting point to the patrol ending point;
the multi-target heuristic optimization model can be a genetic algorithm GA, a differential evolution algorithm DE, NSGA-III and the like.
As shown in fig. 2, the invention provides a system corresponding to a patrol route setting method based on historical data, and the system includes a cluster analysis model, a time series model and a multi-objective heuristic optimization model.
In conclusion, the patrol path comprehensive planning method and the patrol path comprehensive planning system take the relevance of the risk and the time into consideration, and can reasonably distribute the police force. Therefore, the invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (8)
1. A patrol path setting method based on historical data is characterized by comprising the following steps:
step 1: clustering analysis is carried out on historical data, an aggregation area of a historical event occurrence place is marked, and an optimal patrol area is selected by utilizing a spatial relationship;
step 2: predicting event time of future time periods of each patrol area;
and step 3: acquiring the place and time to be patrolled in the future time period;
and 4, step 4: and setting an optimal patrol path by using a multi-objective heuristic optimization model based on the time and the place needing patrol.
2. The patrol route setting method based on historical data according to claim 1, wherein the step 1 is specifically as follows:
step 1.1: the position information of the historical event is taken out, the gathering area of the historical event occurrence place is marked, and the optimal patrol area is obtained through cluster analysis by a cluster analysis model according to the set cluster quantity proportion;
step 1.2: calculating the interval distance between patrol areas obtained by adopting each clustering analysis model, and selecting the clustering analysis model with the best clustering effect;
step 1.3: calculating the distance between the central points of the patrol areas obtained by each cluster analysis model, and adjusting the number of the gathering areas according to the distance;
step 1.4: and classifying all historical events according to the cluster analysis model with the best effect, and classifying the historical events into the most appropriate patrol area.
3. A patrol path setting method based on historical data according to claim 2, wherein the cluster analysis model includes Kmeans, Kmeans + +, Affinity prediction.
4. The patrol route setting method based on historical data according to claim 1, wherein the step 2 is specifically as follows:
step 2.1: taking out the number of historical events in each patrol area, summarizing according to time periods, and filling zero in unrecorded time periods;
step 2.2: generating a training set and a test set from the historical events and the corresponding time data by a sliding window method;
step 2.3: training and predicting a time sequence model by using a grid search, a random search or a Bayesian optimization algorithm in combination with test set and training set data, and selecting an optimal time sequence model and parameters;
step 2.4: and predicting the events of the future time period by using the obtained time series model.
5. The patrol path setting method based on historical data according to claim 4, wherein: the time series model comprises AR, ARIMA and LSTM.
6. The patrol route setting method based on historical data according to claim 1, wherein the step 3 is specifically as follows:
step 3.1: selecting the mass center of each patrol area as a place to be patrolled;
step 3.2: and setting a certain threshold value according to a prediction result obtained by the time series model, wherein the time periods larger than the threshold value need to be patrolled, and further obtaining the time periods needing to be patrolled.
7. The patrol route setting method based on historical data according to claim 1, wherein the step 4 is specifically as follows:
step 4.1: according to the time period and the place needing patrolling, an optimal patrol route is set through a multi-objective heuristic optimization model, namely, the shortest route corresponding to each hour in the future time period is obtained, the shortest connecting route corresponding to the starting point and the ending point of each hour is obtained, and the shortest total route from the patrol starting point to the patrol ending point is obtained.
8. The patrol path setting method based on historical data according to claim 7, wherein: the multi-target heuristic optimization model comprises a genetic algorithm GA, a differential evolution algorithm DE and NSGA-III.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016735A (en) * | 2020-07-17 | 2020-12-01 | 厦门大学 | A patrol route planning method, system and readable storage medium based on traffic violation hotspot prediction |
| CN112308372A (en) * | 2020-09-22 | 2021-02-02 | 合肥工业大学 | Data and model combined driven air-ground patrol resource dynamic scheduling method and system |
| CN113850400A (en) * | 2021-05-26 | 2021-12-28 | 浪潮软件科技有限公司 | A kind of inspection assistant judgment method and system |
| CN113985879A (en) * | 2021-10-28 | 2022-01-28 | 安徽安宠宠物用品有限公司 | Intelligent mobile inspection system and method based on dynamic optimization of historical data |
| CN114577227A (en) * | 2020-12-02 | 2022-06-03 | 浙江宇视科技有限公司 | Patrol route planning method, device, medium and electronic equipment |
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102402729A (en) * | 2010-09-13 | 2012-04-04 | 方正国际软件有限公司 | Method and system for dispatching resources based on road network data |
| US20130198122A1 (en) * | 2012-01-31 | 2013-08-01 | International Business Machines Corporation | System and method for optimizing teams |
| CN103336995A (en) * | 2013-04-19 | 2013-10-02 | 国家电网公司 | Method for constructing real-time light metering network of million kilowatt level photovoltaic power generation base |
| CN103455847A (en) * | 2012-05-30 | 2013-12-18 | 国际商业机器公司 | Method and device for determining paths |
| CN104166895A (en) * | 2014-07-28 | 2014-11-26 | 浙江工业大学 | Public bike scheduling area dividing method |
| CN106384161A (en) * | 2016-08-31 | 2017-02-08 | 天津大学 | Optimization algorithm for regional division of spaceflight tour-inspection plan |
| CN107392389A (en) * | 2017-08-03 | 2017-11-24 | 重庆大学 | Taxi dispatching processing method based on ARIMA models |
| CN107392390A (en) * | 2017-08-07 | 2017-11-24 | 江苏警官学院 | A kind of community policy patrol path optimization design method based on deep learning algorithm |
| CN107451693A (en) * | 2017-08-02 | 2017-12-08 | 南京工业大学 | Multipoint and multi-target dangerous chemical transport path optimization method |
| CN107657412A (en) * | 2017-10-23 | 2018-02-02 | 天津职业技术师范大学 | Towards the unmanned plane and automobile combined type delivery system and allocator of remote districts |
| CN107784387A (en) * | 2017-09-18 | 2018-03-09 | 国家计算机网络与信息安全管理中心 | The continuous dynamic prediction method that a kind of microblogging event information is propagated |
| CN108108859A (en) * | 2018-01-29 | 2018-06-01 | 北京易华录信息技术股份有限公司 | A kind of traffic administration duties optimization method based on big data analysis |
| CN108876136A (en) * | 2018-06-11 | 2018-11-23 | 北京工商大学 | Recommend the attack of terrorism methods of risk assessment of innovatory algorithm based on position |
-
2019
- 2019-01-17 CN CN201910044159.6A patent/CN111161443A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102402729A (en) * | 2010-09-13 | 2012-04-04 | 方正国际软件有限公司 | Method and system for dispatching resources based on road network data |
| US20130198122A1 (en) * | 2012-01-31 | 2013-08-01 | International Business Machines Corporation | System and method for optimizing teams |
| CN103455847A (en) * | 2012-05-30 | 2013-12-18 | 国际商业机器公司 | Method and device for determining paths |
| CN103336995A (en) * | 2013-04-19 | 2013-10-02 | 国家电网公司 | Method for constructing real-time light metering network of million kilowatt level photovoltaic power generation base |
| CN104166895A (en) * | 2014-07-28 | 2014-11-26 | 浙江工业大学 | Public bike scheduling area dividing method |
| CN106384161A (en) * | 2016-08-31 | 2017-02-08 | 天津大学 | Optimization algorithm for regional division of spaceflight tour-inspection plan |
| CN107451693A (en) * | 2017-08-02 | 2017-12-08 | 南京工业大学 | Multipoint and multi-target dangerous chemical transport path optimization method |
| CN107392389A (en) * | 2017-08-03 | 2017-11-24 | 重庆大学 | Taxi dispatching processing method based on ARIMA models |
| CN107392390A (en) * | 2017-08-07 | 2017-11-24 | 江苏警官学院 | A kind of community policy patrol path optimization design method based on deep learning algorithm |
| CN107784387A (en) * | 2017-09-18 | 2018-03-09 | 国家计算机网络与信息安全管理中心 | The continuous dynamic prediction method that a kind of microblogging event information is propagated |
| CN107657412A (en) * | 2017-10-23 | 2018-02-02 | 天津职业技术师范大学 | Towards the unmanned plane and automobile combined type delivery system and allocator of remote districts |
| CN108108859A (en) * | 2018-01-29 | 2018-06-01 | 北京易华录信息技术股份有限公司 | A kind of traffic administration duties optimization method based on big data analysis |
| CN108876136A (en) * | 2018-06-11 | 2018-11-23 | 北京工商大学 | Recommend the attack of terrorism methods of risk assessment of innovatory algorithm based on position |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112016735B (en) * | 2020-07-17 | 2023-03-28 | 厦门大学 | Patrol route planning method and system based on traffic violation hotspot prediction and readable storage medium |
| CN112016735A (en) * | 2020-07-17 | 2020-12-01 | 厦门大学 | A patrol route planning method, system and readable storage medium based on traffic violation hotspot prediction |
| CN112308372A (en) * | 2020-09-22 | 2021-02-02 | 合肥工业大学 | Data and model combined driven air-ground patrol resource dynamic scheduling method and system |
| CN114577227B (en) * | 2020-12-02 | 2024-10-01 | 浙江宇视科技有限公司 | Patrol route planning method, patrol route planning device, medium and electronic equipment |
| CN114577227A (en) * | 2020-12-02 | 2022-06-03 | 浙江宇视科技有限公司 | Patrol route planning method, device, medium and electronic equipment |
| CN113850400A (en) * | 2021-05-26 | 2021-12-28 | 浪潮软件科技有限公司 | A kind of inspection assistant judgment method and system |
| CN113985879B (en) * | 2021-10-28 | 2024-02-02 | 安徽安宠宠物用品有限公司 | Intelligent mobile inspection method based on historical data dynamic optimization |
| CN113985879A (en) * | 2021-10-28 | 2022-01-28 | 安徽安宠宠物用品有限公司 | Intelligent mobile inspection system and method based on dynamic optimization of historical data |
| CN114593733A (en) * | 2021-11-19 | 2022-06-07 | 海纳云物联科技有限公司 | Patrol route setting method and device |
| CN114593733B (en) * | 2021-11-19 | 2025-03-07 | 海纳云物联科技有限公司 | A patrol route setting method and device |
| CN115730751A (en) * | 2022-05-31 | 2023-03-03 | 海纳云物联科技有限公司 | Generation method of police patrol route, electronic device and readable storage medium |
| CN115188158A (en) * | 2022-06-22 | 2022-10-14 | 中国电子科技集团公司电子科学研究院 | Intelligent safety early warning method and device based on historical case space distribution |
| CN118095795A (en) * | 2024-04-24 | 2024-05-28 | 山东晟厚网络科技有限公司 | Police service guarantee comprehensive management and control system and management and control method |
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