CN112348297B - Track data processing method based on plan formulation - Google Patents
Track data processing method based on plan formulation Download PDFInfo
- Publication number
- CN112348297B CN112348297B CN201910725260.8A CN201910725260A CN112348297B CN 112348297 B CN112348297 B CN 112348297B CN 201910725260 A CN201910725260 A CN 201910725260A CN 112348297 B CN112348297 B CN 112348297B
- Authority
- CN
- China
- Prior art keywords
- track
- plan
- police
- data
- alarm
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
Links
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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Alarm Systems (AREA)
Abstract
The invention discloses a track data processing method based on plan making, which comprises the following steps of (1) taking police force coordinate points collected during plan exercise as original data, and combing the data through value constraint; (2) Correcting the simulated plan alarm track by using a normal distribution method; (3) Iterating the track coordinates in the expected plan; (4) And fitting the discrete track points to a warning track curve planned in the plan by an interpolation method. The advantages are that: the accuracy and the high efficiency of the plan making police are improved, the plan making, making and implementing are more intelligent, the obtained track data are subjected to recursion and iteration, namely, each time the plan is implemented in the optimized track database, the optimized result can be directly used in the next plan making, meanwhile, the formed police-out fitting curve can intuitively reflect the police-out situation in the plan, and the plan making combined with the data analysis provides scientific decision basis for the treatment of emergency.
Description
Technical Field
The invention belongs to the technical field of police command control information, and particularly relates to a track data processing method based on planning, which is applied to a police emergency command scheduling platform system.
Background
The existing police plan is formulated into a paper mode and carries out police assessment by virtue of command experience, and particularly the planning of the police track is mainly based on subjective judgment of a complex task execution scene, the defect of the mode is that objective data and criterion support are lacking in the planning of the police track, the accuracy and reusability of the plan formulation are reduced, and the characteristics of actual police scene requirements, police equipment capability and the like are not considered in the conventional track simulation and generation generally by adopting a general path planning algorithm such as a nearby principle, a shortest time principle and the like. After the existing paper plans of public security are electronically modeled, due to the fact that the execution of tasks in the plans faces the scene (streets, gobi, forests, offshore and the like) and the variation of the police resources, the police locus is difficult to establish an effective and accurate mathematical model,
disclosure of Invention
The invention aims to provide a track data processing method based on planning, which can optimize the police outputting track after planning the planning, assist in supporting the planning and implementation of the planning and improve the informatization actual combat level of police command.
The technical scheme of the invention is as follows: a track data processing method based on plan formulation comprises the following steps,
(1) Taking police strength coordinate points collected during scheduled exercise as original data, and combing the data through a value constraint;
(2) Correcting the simulated plan alarm track by using a normal distribution method;
(3) Iterating the track coordinates in the expected plan;
(4) And fitting the discrete track points to a warning track curve planned in the plan by an interpolation method.
Step (1) comprises collecting coordinate positioning of police resources after actual combat police exercise is performed on a traditional paper plan, accumulating original police-outputting track point position data, and adopting a value constraint mode to prepare position constraint conditions [ x ] of different police forces for different application plans min ,y min ]、[x max ,y max ]For the actual alarm point position coordinates (x, y), the position constraint condition should be satisfied, and the track position points outside the constraint condition are removed as invalid data.
The police resources in the step (1) comprise police officers, police cars, picture transfer cars and helicopters.
The step (2) comprises comparing the police outputting track simulated by the plan with the accumulated track data, making a normal distribution curve of the original accumulated data, calculating a standard deviation, analyzing the simulated track data, when the coordinates of the police outputting track point simulated in the plan are more than 3 times of the standard deviation, the data can be regarded as invalid track data, and eliminating,
in the step (2), f (p) is a normal distribution function of the original accumulated data, p is an original track coordinate point x or y coordinate, μ is an expected value of the original track coordinate point, and σ is a standard deviation of the original track coordinate point.
And step (3) comprises the step of evaluating the task execution effect or efficiency when the task is executed according to the formulated plan, and when the effect eta is more than or equal to E, putting the coordinates of the current alarm as a trusted value into a database to be accumulated as original data so as to realize the iteration of the data.
E in the step (3) is an expected value set for the task.
And step (4) comprises integrating the optimized alarm track points, and fitting to obtain an alarm curve to be displayed on a situation map or map.
And (3) in the step (4), fitting a curve by using a Lagrange interpolation method.
The step (4) obtains N alarm point positions, and the coordinates of the N alarm point positions are obtained from (x) 1 ,y 1 ) To (x) n ,y n ) Obtaining a warning curve track P N (x)，
Wherein P is N (x) To fit a polynomial of the alarm trace, x k Is the abscissa value, y of the kth point in the total N warning track points of the plan k The process function L is the ordinate value of the kth point in the total N warning track points of the plan n,k (x) Is a polynomial calculated by the abscissa values of all track points around the kth point.
The invention has the beneficial effects that: the accuracy and the high efficiency of the plan making police are improved, the plan making, making and implementing are more intelligent, the obtained track data are subjected to recursion and iteration, namely, each time the plan is implemented in the optimized track database, the optimized result can be directly used in the next plan making, meanwhile, the formed police-out fitting curve can intuitively reflect the police-out situation in the plan, and the plan making combined with the data analysis provides scientific decision basis for the treatment of emergency.
Detailed Description
The present invention will be described in further detail with reference to specific examples.
And accumulating original police-outputting position data through coordinate positioning after conducting exercise or actual combat command on the traditional paper plan, and combing and correcting the historical police-outputting track data to remove invalid data. And comparing the alarm-outputting track subjected to deduction simulation with the accumulated original track data in the process of planning, removing the deviated or wrong data to form an optimal alarm-outputting track, and iterating the alarm-outputting track as the initial data of the next planning.
A trajectory data processing method based on planning is characterized in that cleaning optimization iteration is carried out on acquired data through boundary constraint and a statistical algorithm. The method specifically comprises the following steps:
(1) And taking police strength coordinate points collected during the scheduled exercise as original data, and combing the data through a value constraint. After actual combat police exercise is performed on a traditional paper plan, coordinate positioning of police resources (police officers, police cars, image transfer cars, helicopters and the like) is collected, original police-output track point position data are accumulated, and position constraint conditions [ x ] of different police forces are made for the plans of different applications in a value constraint mode min ,y min ]、[x max ,y max ]For the coordinates (x, y) of the actual alarm point position, the position constraint condition should be met, and the track position points outside the constraint condition are removed as invalid data;
(2) And correcting the simulated plan alarm track by using a normal distribution method. Comparing the police outputting track simulated by the plan with the accumulated track data, making a normal distribution curve of the original accumulated data, calculating a standard deviation, analyzing the simulated track data, and eliminating the data as invalid track data when the police outputting track point coordinate simulated in the plan is more than 3 times of the standard deviation (in the formula, f (p) is a normal distribution function of the original accumulated data, p is an original track coordinate point x or y coordinate, mu is an expected value of the original track coordinate point, and sigma is an original track coordinate point standard deviation);
(3) The trajectory coordinates in the expected protocol are iterated. When the task is executed according to the formulated plan, the task execution effect or efficiency is evaluated, when the effect eta is more than or equal to E (E is the expected value set by the task), the coordinates of the current alarm are taken as the credible value to be put into a database and are taken as the original data to be accumulated, and the iteration of the data is realized;
(4) And fitting the discrete track points to a warning track curve planned in the plan by an interpolation method. Integrating the optimized alarm-outputting track points, and fitting to obtain an alarm-outputting curve by using a Lagrange interpolation method, wherein the alarm-outputting curve is displayed on a situation map or map. For example, N alarm points are obtained, and the coordinates of the alarm points are calculated from (x 1 ,y 1 ) To (x) n ,y n ) Obtaining a warning curve track P N (x) (in the formula, P N (x) To fit a polynomial of the alarm trace, x k Is the abscissa value, y of the kth point in the total N warning track points of the plan k The process function L is the ordinate value of the kth point in the total N warning track points of the plan n,k (x) A polynomial calculated for the abscissa values of all the trace points around the kth point).
Claims (8)
1. A track data processing method based on plan formulation is characterized in that: comprises the following steps of the method,
(1) Taking police strength coordinate points collected during scheduled exercise as original data, and combing the data through a value constraint;
(2) Correcting the simulated plan alarm track by using a normal distribution method;
(3) Iterating the track coordinates in the expected plan;
(4) Fitting discrete track points to a warning track curve planned in a plan by an interpolation method;
step (1) comprises collecting coordinate positioning of police resources after actual combat police exercise is performed on a traditional paper plan, accumulating original police-outputting track point position data, and adopting a value constraint mode to prepare position constraint conditions [ x ] of different police forces for different application plans min ,y min ]、[x max ,y max ]For the coordinates (x, y) of the actual alarm point position, the position constraint condition should be met, and the track position points outside the constraint condition are removed as invalid data;
the step (2) comprises comparing the police outputting track simulated by the plan with the accumulated track data, making a normal distribution curve of the original accumulated data, calculating a standard deviation, analyzing the simulated track data, when the coordinates of the police outputting track point simulated in the plan are more than 3 times of the standard deviation, the data can be regarded as invalid track data, and eliminating,
2. a trajectory data processing method based on a planning scheme as claimed in claim 1, characterized in that: the police resources in the step (1) comprise police officers, police cars, picture transfer cars and helicopters.
3. A trajectory data processing method based on a planning scheme as claimed in claim 1, characterized in that: in the step (2), f (p) is a normal distribution function of the original accumulated data, p is an original track coordinate point x or y coordinate, μ is an expected value of the original track coordinate point, and σ is a standard deviation of the original track coordinate point.
4. A trajectory data processing method based on a planning scheme as claimed in claim 1, characterized in that: and step (3) comprises the step of evaluating the task execution effect or efficiency when the task is executed according to the formulated plan, and when the effect eta is more than or equal to E, putting the coordinates of the current alarm as a trusted value into a database to be accumulated as original data so as to realize the iteration of the data.
5. The trajectory data processing method based on the planning of claim 4, wherein: e in the step (3) is an expected value set for the task.
6. A trajectory data processing method based on a planning scheme as claimed in claim 1, characterized in that: and step (4) comprises integrating the optimized alarm track points, and fitting to obtain an alarm curve to be displayed on a situation map or map.
7. The trajectory data processing method based on planning of claim 6, wherein: and (3) in the step (4), fitting a curve by using a Lagrange interpolation method.
8. The trajectory data processing method based on planning of claim 7, wherein: the step (4) obtains N alarm point positions, and the coordinates of the N alarm point positions are obtained from (x) 1 ,y 1 ) To (x) n ,y n ) Obtaining a warning curve track P N (x)，
Wherein P is N (x) To fit a polynomial of the alarm trace, x k Is the abscissa value, y of the kth point in the total N warning track points of the plan k The process function L is the ordinate value of the kth point in the total N warning track points of the plan n,k (x) Is a polynomial calculated by the abscissa values of all track points around the kth point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910725260.8A CN112348297B (en) | 2019-08-07 | 2019-08-07 | Track data processing method based on plan formulation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910725260.8A CN112348297B (en) | 2019-08-07 | 2019-08-07 | Track data processing method based on plan formulation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112348297A CN112348297A (en) | 2021-02-09 |
| CN112348297B true CN112348297B (en) | 2023-08-15 |
Family
ID=74366647
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910725260.8A Active CN112348297B (en) | 2019-08-07 | 2019-08-07 | Track data processing method based on plan formulation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112348297B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10117340A (en) * | 1996-10-11 | 1998-05-06 | Yazaki Corp | Vehicle periphery monitoring device, alarm display method to be used for device and medium-storing display method |
| CN103680127A (en) * | 2013-08-29 | 2014-03-26 | 中国科学院地理科学与资源研究所 | A method for calculating signal lamp control road intersection delays through the utilization of low sampling rate floating vehicle data |
| CN108260076A (en) * | 2016-12-28 | 2018-07-06 | 中国电信股份有限公司 | Method, platform and the system of unmanned plane running orbit monitoring |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9061775B2 (en) * | 2012-06-22 | 2015-06-23 | Isaac M. Ross | Method and apparatus for spacecraft attitude control using polynomial interpolation |
-
2019
- 2019-08-07 CN CN201910725260.8A patent/CN112348297B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10117340A (en) * | 1996-10-11 | 1998-05-06 | Yazaki Corp | Vehicle periphery monitoring device, alarm display method to be used for device and medium-storing display method |
| CN103680127A (en) * | 2013-08-29 | 2014-03-26 | 中国科学院地理科学与资源研究所 | A method for calculating signal lamp control road intersection delays through the utilization of low sampling rate floating vehicle data |
| CN108260076A (en) * | 2016-12-28 | 2018-07-06 | 中国电信股份有限公司 | Method, platform and the system of unmanned plane running orbit monitoring |
Non-Patent Citations (1)
| Title |
|---|
| IP-ISC关键技术及在合成作战中的应用研究;周川;贺国强;黄剑宇;;计算机仿真(第09期);第26-30，42页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112348297A (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107018493B (en) | Geographic position prediction method based on continuous time sequence Markov model | |
| CN111415010B (en) | Bayesian neural network-based wind turbine generator parameter identification method | |
| WO2022077767A1 (en) | Traffic flow prediction method and apparatus, computer device, and readable storage medium | |
| CN113327417B (en) | Traffic flow prediction method based on 3D dynamic space-time residual convolution associated network | |
| CN105913658B (en) | A kind of method that traffic flow speculates OD positions and OD matrixes | |
| CN115019510B (en) | Traffic data restoration method based on dynamic self-adaptive generation countermeasure network | |
| CN112907970B (en) | Variable lane steering control method based on vehicle queuing length change rate | |
| CN112711083B (en) | Multi-source precipitation data dynamic fusion method and system based on adaptive weight characteristics | |
| CN113379168B (en) | Time series prediction processing method, device and equipment | |
| CN112201037B (en) | Intersection arrival rate estimation method based on sampling trajectory data | |
| CN111191832A (en) | Typhoon disaster power distribution network tower fault prediction method and system | |
| CN110910440A (en) | Power transmission line length determination method and system based on power image data | |
| CN113837148A (en) | Pedestrian trajectory prediction method based on self-adjusting sparse graph transform | |
| CN116739147A (en) | BIM-based intelligent energy consumption management and dynamic carbon emission calculation combined method and system | |
| KR20170069046A (en) | System for forcasting power demand based on weather infromation | |
| CN112348297B (en) | Track data processing method based on plan formulation | |
| CN109255948B (en) | Lane-dividing traffic flow proportion prediction method based on Kalman filtering | |
| CN114118528A (en) | Ship track real-time forecasting method based on combination of linear filter and nonlinear filter | |
| CN114236480A (en) | Airborne platform sensor system error registration algorithm | |
| CN111145548B (en) | Important intersection identification and subregion division method based on data field and node compression | |
| CN107273692B (en) | Distributed fusion method of random set theory with limited sensor sensing capability | |
| CN115685128B (en) | Radar target tracking algorithm and electronic equipment under maneuvering target scene | |
| CN109685242B (en) | Photovoltaic ultra-short term combined prediction method based on Adaboost algorithm | |
| Shabri | A novel hybrid ensemble learning paradigm for tourism forecasting | |
| CN110765420A (en) | PSO-FI-based ground automatic meteorological station air temperature observation data quality control method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |