CN115880844A - Ship security intelligent management system based on multi-source perception - Google Patents
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Abstract
The invention discloses a ship security intelligent management system based on multi-source perception, which comprises: the system comprises a ship monitoring unit, a ship management unit and a background control center; the ship monitoring management unit is used for sensing and monitoring the condition information on and around the ship in real time and processing the condition information; the system comprises a ship management unit used for processing the condition information monitored by the ship monitoring unit, and a background control center used for analyzing and judging the information processed by the ship management unit and making a corresponding strategy. According to the invention, through a multi-Ha-Hi similarity weighting method, people in a ship area are quickly subjected to face recognition verification, and the processing speed is improved, so that non-ship people can be accurately and quickly found out, dangerous attack on a ship by the non-ship people is prevented, and the risk in the ship navigation process is accurately judged through a grey correlation algorithm, so that the safety of the ship in the navigation process and the intelligent management of the ship can be improved.
Description
Technical Field
The invention relates to the technical field of ship monitoring, in particular to a ship security intelligent management system based on multi-source sensing.
Background
With the continuous development of shipping career and the breakthrough of intelligent ship construction, the country has paid high attention to the protection of ship navigation safety and personal safety of ship personnel. However, in the traditional ship safety protection, although the ship can realize all-weather uninterrupted monitoring by installing a high-definition camera, a thermal imaging night vision device and infrared equipment, the requirement for clear and accurate imaging of a monitored area cannot be met in environments such as night, fog and the like or when the imaging distance is long; aiming at articles carried by personnel entering and exiting a ship, a security check instrument is usually adopted for article detection, but a portable device for rapidly and intelligently detecting, identifying and reasonably disposing explosive articles is lacked; aiming at the identification and early warning of an external invading target of a ship, the surrounding environment of the ship is generally sensed by adopting a radar, but an intelligent identification, early warning and tracking system aiming at a suspicious target on water is lacked.
Wherein, chinese patent CN108227606B a boats and ships security protection intelligent management system based on multisource perception includes: the system comprises a ship emergency response unit, a data transfer satellite and a shore-based emergency guidance unit. The ship emergency response unit monitors the in-and-out state information of personnel on the ship, the condition information of whether dangerous goods exist on the ship and the condition information of whether dangerous targets exist on the sea surface in real time, and the shore-based emergency guidance unit receives the data information transmitted by the ship emergency response unit and makes an optimization scheme for avoiding risk navigation of the ship. However, in the process of collecting, transmitting and processing data, the management system may cause problems such as failure of the collecting device and noise interference of the transmission channel, which may cause abnormal data, and the management system does not process the collected data, so that the abnormal data may affect subsequent judgment, and accurate judgment cannot be made.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a ship security intelligent management system based on multi-source perception, and aims to overcome the technical problems in the prior related art.
Therefore, the invention adopts the following specific technical scheme:
a ship security intelligent management system based on multi-source perception comprises: the system comprises a ship monitoring unit, a ship management unit and a background control center, wherein the ship monitoring unit is connected with the background control center through the ship management unit;
the ship monitoring management unit is used for sensing and monitoring the condition information on and around the ship in real time and processing the condition information;
the ship management unit is used for processing the condition information monitored by the ship monitoring unit;
and the background control center is used for analyzing and judging the information processed by the ship management unit and making a corresponding strategy.
Furthermore, the ship monitoring unit comprises a video monitoring module, a smoke detection module, a radar detection module and a weather detection module;
the video monitoring module is used for monitoring the ship area and personnel in the ship area through the monitoring camera to obtain a face image of the personnel in the ship area;
the smoke detection module is used for monitoring whether a fire disaster condition exists in the ship area through the smoke sensor;
the radar detection module is used for monitoring whether other ships and other objects exist in the vicinity of the ship or not through the radar detector;
and the weather detection module is used for monitoring the weather condition of the nearby area of the ship through the ship weather meter.
Further, the ship management unit comprises a data processing module, a personnel identification module and a ship navigation risk evaluation module, and the data processing module is sequentially connected with the personnel identification module, the ship navigation risk evaluation module, the video monitoring module, the smoke detection module, the radar detection module and the weather detection module;
the data processing module is used for analyzing and processing the data transmitted by the ship monitoring unit;
the personnel identification module is used for identifying and verifying the acquired personnel face image of the ship area;
and the ship navigation risk evaluation module is used for evaluating the ship navigation risk through a grey correlation degree analysis method.
Further, the analyzing and processing of the monitoring data transmitted by the ship monitoring unit includes the following steps:
collecting monitoring data transmitted by the ship monitoring unit, and inducing and classifying the monitoring data according to data types to obtain a measurement data set;
calculating the measurement mean value of each group of measurement data
And the residual value->
And a standard deviation estimate>
；
If the residual value of the ith measurement data
Satisfies the formula>
And the ith measurement data->
Belongs to abnormal data, otherwise, the ith measurement data->
Belonging to normal data;
and eliminating the abnormal data and storing the normal data.
Further, the measured mean value
Is calculated byThe formula is as follows:
the above-mentioned
The calculation formula of (a) is as follows:
the standard deviation estimate
The calculation formula of (a) is as follows:
where h represents the number of measurement data.
Further, the identification and verification of the acquired face image of the personnel in the ship area comprises the following steps:
acquiring the face image information of ship personnel on a ship and forming a ship personnel face information database;
acquiring a face image of personnel in a ship area and the characteristics of the face image of the ship personnel in the ship personnel face information database through a neural network to form a characteristic diagram of the personnel in the ship area and a characteristic diagram of the ship personnel;
carrying out similarity calculation on the feature graphs of the ship region personnel and the feature graphs of the ship personnel through a mean hash algorithm and a perception hash algorithm;
respectively using
And &>
Features of weighting coefficients as mean hash and perceptual hash on the ship region personnelWeighting the similarity of the graph and the characteristic graph of the ship personnel to obtain a final similarity value;
when the weighted similarity is larger than or equal to a preset threshold value, judging that the ship region personnel are ship personnel;
and when the weighted similarity is smaller than a preset threshold value, judging that the ship region personnel are not ship personnel.
Further, in the above-mentioned case,
the final similarity value is calculated as follows:
wherein the content of the first and second substances,
representing the final similarity value;
further, the assessment of the risk of ship navigation by using a grey correlation analysis method comprises the following steps:
acquiring risks existing in the historical navigation of the ship;
establishing an evaluation index system according to risks existing in the historical navigation of the ship;
determining a reference data sequence and a comparison sequence of the reference object;
calculating a correlation coefficient and a correlation degree between the evaluation indexes;
and judging the risk of the ship navigation according to the association degree, wherein the higher the association degree is, the safer the ship navigation process is, and on the contrary, the more dangerous the ship navigation process is.
Further, the calculation formula for calculating the correlation coefficient between the evaluation indexes is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing a correlation coefficient;
the calculation formula for calculating the correlation degree between the evaluation indexes is as follows:
wherein the content of the first and second substances,
representing the degree of association;
Further, the background control center comprises a terminal display module and an early warning module, and the terminal display module and the early warning module are sequentially connected with the data processing module;
the terminal display module is used for visually displaying the data processed by the ship management unit;
and the early warning module is used for sending out an early warning signal to prompt ship personnel.
The invention has the beneficial effects that:
1. according to the invention, information acquisition is carried out on a ship area and the vicinity of a ship through a multi-source sensing technology, and people in the ship area are rapidly subjected to face recognition verification through a multi-Haichi similarity weighting method, so that the processing speed is improved, non-ship people can be accurately and rapidly found out, the non-ship people are prevented from carrying out dangerous attack on the ship, the risk in the ship navigation process is accurately judged through a grey correlation algorithm, and the safety of the ship in the ship navigation process and the intelligent management of the ship can be further improved.
2. According to the method and the device, the acquired data are analyzed to be abnormal, and the abnormal data can be accurately removed and cleaned, so that the acquired data are smoothly processed, and further, the follow-up identity verification of personnel in a ship area and the accurate judgment of risks in the ship navigation process can be guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic block diagram of a ship security intelligent management system based on multi-source sensing according to an embodiment of the invention.
In the figure:
1. a vessel monitoring unit; 101. a video monitoring module; 102. a smoke detection module; 103. a radar detection module; 104. a weather detection module; 2. a ship management unit; 201. a data processing module; 202. a person identification module; 203. a ship navigation risk evaluation module; 3. a background control center; 301. a terminal display module; 302. and the early warning module.
Detailed Description
For further explanation of the various embodiments, the drawings which form a part of the disclosure and which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of operation of the embodiments, and to enable others of ordinary skill in the art to understand the various embodiments and advantages of the invention, and, by reference to these figures, reference is made to the accompanying drawings, which are not to scale and wherein like reference numerals generally refer to like elements.
According to the embodiment of the invention, a ship security intelligent management system based on multi-source perception is provided.
Referring to the drawings and the detailed description, the invention will be further explained, as shown in fig. 1, according to the intelligent management system for ship security based on multi-source sensing of the embodiment of the invention, the system includes: the system comprises a ship monitoring unit 1, a ship management unit 2 and a background control center 3, wherein the ship monitoring unit 1 is connected with the background control center 3 through the ship management unit 2;
the ship monitoring and managing unit 1 is used for sensing and monitoring the information of the conditions on and around the ship in real time and processing the information;
specifically, the ship monitoring unit 1 includes a video monitoring module 101, a smoke detection module 102, a radar detection module 103, and a weather detection module 104;
the video monitoring module 101 is used for monitoring the ship area and personnel in the ship area through the monitoring camera to obtain a face image of the personnel in the ship area;
the smoke detection module 102 is configured to monitor whether a fire condition exists in a ship region through a smoke sensor;
the radar detection module 103 is configured to monitor whether other ships and other objects exist in the vicinity of the ship through a radar detector;
the weather detection module 104 is configured to monitor weather conditions in a vicinity of the ship through a ship weather meter.
The ship management unit 2 is used for processing the condition information monitored by the ship monitoring unit 1;
specifically, the ship management unit 2 includes a data processing module 201, a personnel identification module 202 and a ship navigation risk assessment module 203, and the data processing module 201 is sequentially connected to the personnel identification module 202, the ship navigation risk assessment module 203, the video monitoring module 101, the smoke detection module 102, the radar detection module 103 and the weather detection module 104;
the data processing module 201 is configured to analyze and process data transmitted by the ship monitoring unit 1;
specifically, boats and ships may be by the trouble that has collection equipment, transmission channel's noise interference scheduling problem in the operation process to can cause the unusual condition to appear in data, through carrying out unusual analysis to the data of gathering, and can be accurate reject the washing with unusual data, thereby realized the smooth processing to the data of gathering, and then can provide the guarantee for follow-up verification of the regional personnel of boats and ships and carry out accurate judgement to the risk in the boats and ships navigation process.
Wherein, the analyzing and processing of the monitoring data transmitted by the ship monitoring unit 1 comprises the following steps:
collecting monitoring data transmitted by the ship monitoring unit 1, and inducing and classifying the monitoring data according to data types to obtain a measurement data set;
calculating the measurement mean value of each group of measurement data
And the residual value->
And a standard deviation estimate>
；
Wherein the measured mean value
The calculation formula of (c) is as follows:
the above-mentioned
The calculation formula of (a) is as follows:
the standard deviation estimate
The calculation formula of (a) is as follows:
where h represents the number of measurement data.
If the residual value of the ith measurement data
Satisfies the formula->
And the ith measurement data->
Belonging to anomalous data, or else the ith measurement data>
Belonging to normal data;
and eliminating the abnormal data and storing the normal data.
The personnel identification module 202 is used for identifying and verifying the acquired personnel face image in the ship area;
the method for identifying and verifying the acquired face image of the personnel in the ship area comprises the following steps:
acquiring the face image information of ship personnel on a ship and forming a ship personnel face information database;
acquiring a face image of personnel in a ship area and the characteristics of the face image of the ship personnel in the ship personnel face information database through a neural network to form a characteristic diagram of the personnel in the ship area and a characteristic diagram of the ship personnel;
carrying out similarity calculation on the feature graphs of the ship region personnel and the feature graphs of the ship personnel through a mean hash algorithm and a perception hash algorithm;
specifically, the mean hash algorithm includes the steps of:
1) Reducing the feature map to 8 × 8, and obtaining 64 pixels in total;
2) Converting the characteristic diagram with the size of 8 multiplied by 8 into a gray scale diagram;
3) Calculating the gray level average value of all 64 pixels;
4) Comparing the gray levels of the pixels, recording the result as 1 if the gray level of each pixel is greater than or equal to the average gray level, otherwise recording the result as 0, and stringing the results to form a binary number;
5) Taking 64 binary numbers as the hash value of the characteristic image, and calculating the Hamming distance of the hash value of the characteristic image to obtain a similarity value;
specifically, the step of the perceptual hash algorithm includes:
1) Reducing the feature map to a size of 32 × 32;
2) Converting a feature map with the size of 32 multiplied by 32 into a gray scale map;
3) Performing discrete cosine transform on the grayed characteristic graph to obtain a 32 multiplied by 32 discrete cosine transform coefficient matrix;
4) Extracting an 8 multiplied by 8 matrix at the upper left corner in the discrete cosine transform coefficient matrix, and calculating the average value of the 8 multiplied by 8 matrix;
5) Comparing the gray levels of the pixels, recording the result as 1 if the gray level of each pixel in the extracted 8 x 8 matrix is greater than or equal to the average value, otherwise recording the result as 0, and stringing the results to form a binary number;
6) Taking 64 binary numbers as the hash value of the characteristic image, and calculating the Hamming distance of the hash value of the characteristic image to obtain a similarity value;
respectively using
And &>
Weighting the similarity of the characteristic graphs of the ship region personnel and the characteristic graphs of the ship personnel by using the weighted coefficients of the mean hash and the weighted coefficients of the perceptual hash to obtain a final similarity value;
wherein the final similarity value is calculated according to the following formula:
wherein the content of the first and second substances,
representing the final similarity value;
when the weighted similarity is larger than or equal to a preset threshold value, judging that the ship region personnel are ship personnel;
and when the weighted similarity is smaller than a preset threshold value, judging that the ship region personnel is not ship personnel.
The ship navigation risk evaluation module 203 is configured to evaluate a ship navigation risk through a grey correlation analysis method;
wherein, the assessment of the risk of ship navigation by the grey correlation analysis method comprises the following steps:
acquiring risks existing in the historical navigation of a ship;
establishing an evaluation index system according to risks existing in the historical navigation of the ship;
determining a reference data sequence and a comparison sequence of the reference object;
specifically, in order to evaluate the evaluation target data sequence, an evaluation reference data sequence is first determined, which is generally expressed as:
hypothetical reference data sequence
If there are m data index sequences to be compared and each data sequence has n indexes, the comparison sequence can be recorded as:
when the reference sequence is determined, a reference value is selected according to the index type of the comparison sequence;
calculating a correlation coefficient and a correlation degree between the evaluation indexes;
wherein, the calculation formula for calculating the correlation coefficient between the evaluation indexes is as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing a correlation coefficient;
the calculation formula for calculating the correlation degree between the evaluation indexes is as follows:
wherein the content of the first and second substances,
representing the degree of association;
And judging the risk of the ship navigation according to the association degree, wherein the higher the association degree is, the safer the ship navigation process is, and on the contrary, the more dangerous the ship navigation process is.
The background control center 3 is used for analyzing and judging the information processed by the ship management unit 2 and making a corresponding strategy;
the background control center 3 comprises a terminal display module 301 and an early warning module 302, and the terminal display module 301 and the early warning module 302 are sequentially connected with the data processing module 201;
the terminal display module 301 is configured to perform visual display on the data processed by the ship management unit 2;
the terminal display module comprises a display screen, a mobile phone, a PC tablet, a PC computer and the like.
The early warning module 302 is used for sending an early warning signal to prompt ship personnel;
specifically, when the ship personnel receive the early warning sent by the early warning module 302, the ship personnel can make corresponding judgment according to the early warning information, so that unnecessary loss of the ship in the operation process is avoided.
In summary, according to the technical scheme of the invention, the information acquisition is carried out on the ship area and the vicinity of the ship through the multi-source sensing technology, the face recognition verification is rapidly carried out on the personnel in the ship area through the multi-Hash similarity weighting method, the processing speed is improved, so that the non-ship personnel can be accurately and rapidly found out, the dangerous attack of the non-ship personnel on the ship is prevented, the risk in the ship sailing process is accurately judged through the grey correlation algorithm, and the safety of the ship in the sailing process and the intelligent management of the ship can be improved; according to the invention, the acquired data are analyzed in an abnormal manner, and the abnormal data can be accurately removed and cleaned, so that the acquired data are smoothly processed, and further, the identity verification of personnel in a ship area and the accurate judgment of risks in the ship navigation process can be guaranteed.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. The utility model provides a boats and ships security protection intelligent management system based on multisource perception which characterized in that, this system includes: the system comprises a ship monitoring unit, a ship management unit and a background control center, wherein the ship monitoring unit is connected with the background control center through the ship management unit;
the ship monitoring management unit is used for sensing and monitoring the condition information on and around the ship in real time and processing the condition information;
the ship management unit is used for processing the condition information monitored by the ship monitoring unit;
the ship management unit comprises a data processing module, a personnel identification module and a ship navigation risk evaluation module, and the data processing module is sequentially connected with the personnel identification module, the ship navigation risk evaluation module, the video monitoring module, the smoke detection module, the radar detection module and the weather detection module;
the data processing module is used for analyzing and processing the data transmitted by the ship monitoring unit;
wherein, the analysis and processing of the monitoring data transmitted by the ship monitoring unit comprises the following steps:
collecting monitoring data transmitted by the ship monitoring unit, and summarizing and classifying the monitoring data according to data types to obtain a measurement data set;
calculating the measurement mean value of each group of measurement data
And the residual value->
And a standard deviation estimate>
；
If the residual value of the ith measurement data
Satisfies the formula->
Then the ith measurement data>
Belongs to abnormal data, otherwise, the ith measurement data->
Belonging to normal data;
rejecting the abnormal data and storing the normal data;
the personnel identification module is used for identifying and verifying the acquired personnel face image of the ship area;
the ship navigation risk evaluation module is used for evaluating the ship navigation risk through a grey correlation degree analysis method;
and the background control center is used for analyzing and judging the information processed by the ship management unit and making a corresponding strategy.
2. The intelligent ship security management system based on multi-source sensing of claim 1, wherein the ship monitoring unit comprises a video monitoring module, a smoke detection module, a radar detection module and a weather detection module;
the video monitoring module is used for monitoring the ship area and personnel in the ship area through the monitoring camera to obtain a face image of the personnel in the ship area;
the smoke detection module is used for monitoring whether a fire disaster condition exists in the ship area through the smoke sensor;
the radar detection module is used for monitoring whether other ships and other objects exist in the vicinity of the ship or not through the radar detector;
and the weather detection module is used for monitoring the weather condition of the nearby area of the ship through the ship weather meter.
3. The intelligent ship security management system based on multisource perception according to claim 2, wherein the measured mean value is
The calculation formula of (c) is as follows:
the described
The calculation formula of (a) is as follows:
the standard deviation estimate
The calculation formula of (a) is as follows:
where h represents the number of measurement data.
4. The multi-source-perception-based intelligent management system for ship security and protection according to claim 1, wherein the step of identifying and verifying the acquired face images of the personnel in the ship area comprises the following steps:
acquiring face image information of ship personnel on a ship and forming a ship personnel face information database;
acquiring a face image of personnel in a ship area and the characteristics of the face image of the ship personnel in the ship personnel face information database through a neural network to form a characteristic diagram of the personnel in the ship area and a characteristic diagram of the ship personnel;
carrying out similarity calculation on the feature graphs of the ship region personnel and the feature graphs of the ship personnel through a mean hash algorithm and a perception hash algorithm;
respectively using
And &>
Weighting the similarity of the characteristic graphs of the ship region personnel and the characteristic graphs of the ship personnel by using the weighted coefficients of the mean hash and the weighted coefficients of the perceptual hash to obtain a final similarity value;
when the weighted similarity is larger than or equal to a preset threshold value, judging that the ship region personnel are ship personnel;
and when the weighted similarity is smaller than a preset threshold value, judging that the ship region personnel is not ship personnel.
5. The intelligent ship security management system based on multisource perception according to claim 4, wherein a calculation formula of the final similarity value is as follows:
wherein the content of the first and second substances,
representing the final similarity value;
6. The intelligent ship security management system based on multi-source perception according to claim 1, wherein the assessment of the risk of ship navigation through a grey correlation analysis method comprises the following steps:
acquiring risks existing in the historical navigation of a ship;
establishing an evaluation index system according to risks existing in the historical navigation of the ship;
determining a reference data sequence and a comparison sequence of the reference object;
calculating a correlation coefficient and a correlation degree between the evaluation indexes;
and judging the risk of the ship navigation according to the association degree, wherein the higher the association degree is, the safer the ship navigation process is, and otherwise, the more dangerous the ship navigation process is.
7. The intelligent ship security management system based on multi-source perception according to claim 6, wherein a calculation formula for calculating the correlation coefficient between the evaluation indexes is as follows:
wherein the content of the first and second substances,
representing a correlation coefficient;
the calculation formula for calculating the correlation degree between the evaluation indexes is as follows:
wherein the content of the first and second substances,
representing the degree of association;
8. The intelligent ship security management system based on multi-source perception according to claim 1, wherein the background control center comprises a terminal display module and an early warning module, and the terminal display module and the early warning module are sequentially connected with the data processing module;
the terminal display module is used for visually displaying the data processed by the ship management unit;
and the early warning module is used for sending out an early warning signal to prompt ship personnel.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108227606A (en) * | 2018-01-29 | 2018-06-29 | 李颖 | A kind of ship security intelligent management system perceived based on multi-source |
| CN109883496A (en) * | 2019-03-12 | 2019-06-14 | 上海卯瑞船舶设备有限公司 | A kind of monitoring of marine fuel oil and opposite bank management system for internet of things |
| CN111091641A (en) * | 2018-10-24 | 2020-05-01 | 大连永航科技有限公司 | Ship security system based on face recognition |
| CN111160195A (en) * | 2019-12-23 | 2020-05-15 | 哈尔滨工程大学 | Ship personnel management system based on multi-biometric feature recognition technology |
| CN111340343A (en) * | 2020-02-18 | 2020-06-26 | 广东省标准化研究院 | Unmanned ship safety risk grey correlation degree evaluation method |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108227606A (en) * | 2018-01-29 | 2018-06-29 | 李颖 | A kind of ship security intelligent management system perceived based on multi-source |
| CN111091641A (en) * | 2018-10-24 | 2020-05-01 | 大连永航科技有限公司 | Ship security system based on face recognition |
| CN109883496A (en) * | 2019-03-12 | 2019-06-14 | 上海卯瑞船舶设备有限公司 | A kind of monitoring of marine fuel oil and opposite bank management system for internet of things |
| CN111160195A (en) * | 2019-12-23 | 2020-05-15 | 哈尔滨工程大学 | Ship personnel management system based on multi-biometric feature recognition technology |
| CN111340343A (en) * | 2020-02-18 | 2020-06-26 | 广东省标准化研究院 | Unmanned ship safety risk grey correlation degree evaluation method |
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