CN119832404A

CN119832404A - Monitoring method, system, device and equipment based on information acquisition FSU

Info

Publication number: CN119832404A
Application number: CN202411770362.9A
Authority: CN
Inventors: 杨文生; 黄玉华; 陈垚文
Original assignee: Shenzhen ZNV Technology Co Ltd
Current assignee: Shenzhen ZNV Technology Co Ltd
Priority date: 2024-12-04
Filing date: 2024-12-04
Publication date: 2025-04-15

Abstract

The application relates to the technical field of intelligent control, and discloses a monitoring method, a monitoring system, a monitoring device and monitoring equipment based on information acquisition FSU, wherein the monitoring method comprises the following steps: based on the image pickup equipment layout point positions and the infrared detection equipment layout point positions, a three-dimensional space model is constructed, when the image pickup equipment and/or the infrared detection equipment detect signal changes, abnormal signal data are acquired through an information acquisition FSU, based on the three-dimensional space model, edge calculation analysis is carried out on the abnormal signal data through an image recognition algorithm, a scene recognition result is obtained, and a monitoring control strategy is triggered according to the scene recognition result. According to the application, the three-dimensional space model and the image recognition algorithm are used for carrying out edge calculation analysis on the abnormal signal data, so that the monitoring control strategy is triggered, the monitoring operation efficiency is improved, and the accuracy and stability of the monitoring data are ensured.

Description

Monitoring method, system, device and equipment based on information acquisition FSU

Technical Field

The present application relates to the field of control technologies, and in particular, to a monitoring method, system, device, and equipment based on an information acquisition FSU.

Background

The traditional movable ring machine room monitoring equipment monitors through equipment such as independent processing access control equipment and infrared equipment, generally only can report relevant alarm information to a platform for processing, and the machine room camera equipment is accessed to the platform through traditional movable ring gateway equipment, all information is gathered and processed on the platform, operation steps are complex, and personnel work tasks are heavy. In a large-scale platform, the information quantity is too large, so that the accuracy and the real-time performance of the received monitoring data information are poor, and the problem of false alarm is also caused.

Disclosure of Invention

The application mainly aims to provide a monitoring method, a monitoring system, a monitoring device and monitoring equipment based on information acquisition FSU, and aims to solve the technical problem that accuracy and stability of monitoring data cannot be guaranteed due to limitations of linkage capacity and calculation capacity of traditional gateway equipment.

In order to achieve the above purpose, the present application provides a monitoring method based on information collection FSU, the method comprising:

constructing a three-dimensional space model based on the imaging equipment layout points and the infrared detection equipment layout points;

when the camera equipment and/or the infrared detection equipment detect signal change, acquiring abnormal signal data through an information acquisition FSU;

and carrying out edge calculation analysis on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model to obtain a scene recognition result, and triggering a monitoring control strategy according to the scene recognition result.

In an embodiment, the scene recognition result includes entry of an operation and maintenance person, intrusion of an illegal person and false entry of other animals, and the step of performing edge calculation analysis on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model to obtain a scene recognition result, and triggering a monitoring control strategy according to the scene recognition result includes:

acquiring real-time data through a signal acquisition device based on the three-dimensional space model;

when the abnormal signal data is consistent with the real-time data, performing edge calculation analysis on the abnormal signal data through an image recognition algorithm to obtain a scene recognition result;

When the scene recognition result is that an operation and maintenance person enters or an illegal person enters, extracting face information of the abnormal signal data;

The face information is identified through an image identification algorithm, an identification result is obtained, and corresponding information is output according to the identification result;

And when the scene recognition result is that other animals are mistaken, obtaining the species category of the living body by analyzing the abnormal signal data, and generating corresponding alarm information.

In an embodiment, the step of identifying the face information by an image identification algorithm to obtain an identification result and outputting corresponding information according to the identification result includes:

the face information is identified through an image identification algorithm, and an identification result is obtained;

When the identification result is an illegal person, generating corresponding alarm information and outputting the face information;

and outputting the identification result when the identification result is an operation and maintenance person.

In an embodiment, the step of obtaining the species category of the living body by analyzing the abnormal signal data and generating the corresponding alarm information when the scene recognition result is that the other animals are mistaken includes:

When the scene recognition result is that other animals are mistaken, calculating an animal model based on edges, and carrying out feature extraction and classification on the abnormal signal data to obtain vital body key information;

And mapping the vital body key information to the corresponding species category through an edge calculation animal model classifier to obtain the vital body species category, and generating corresponding alarm information.

In an embodiment, the step of acquiring abnormal signal data through the information acquisition FSU when the image capturing apparatus and/or the infrared detection apparatus detects a signal change includes:

when the camera equipment and/or the infrared detection equipment detect signal change, triggering an adjacent monitoring area by a signal change single monitoring area;

based on the interactive identification of the cradle head camera equipment and the adjacent monitoring area, determining the spatial position of the signal change by acquiring the infrared radiation intensity and the change direction of the signal change;

and shooting the space position by using the image pickup equipment to obtain an abnormal picture, and transmitting the abnormal picture as abnormal signal data to an information acquisition FSU.

In an embodiment, the step of constructing a three-dimensional space model based on the imaging device layout points and the infrared detection device layout points includes:

Based on the imaging equipment layout points and the infrared detection equipment layout points, constructing an initial three-dimensional space model through a software built-in model;

Or alternatively, the first and second heat exchangers may be,

Based on the arrangement points of the camera equipment and the arrangement points of the infrared detection equipment, obtaining image data of different visual angles through the cradle head camera equipment, and constructing an initial three-dimensional space model according to the image data;

Mapping the image pickup device and the infrared detection device to corresponding point positions of the initial three-dimensional space model to obtain a three-dimensional space model.

In addition, in order to achieve the aim, the application also provides a monitoring system based on the information acquisition FSU, which comprises a camera device, an infrared detection device and the information acquisition FSU;

The camera shooting equipment and the infrared detection equipment are connected with the information acquisition FSU.

In addition, in order to achieve the above object, the present application further provides a monitoring device based on an information collection FSU, where the monitoring device based on the information collection FSU includes:

The model construction module is used for constructing a three-dimensional space model based on the arrangement points of the camera equipment and the arrangement points of the infrared detection equipment;

the data acquisition module is used for acquiring abnormal signal data through the information acquisition FSU when the camera equipment and/or the infrared detection equipment detect signal changes;

And the monitoring control module is used for carrying out edge calculation analysis on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model to obtain a scene recognition result, and triggering a monitoring control strategy according to the scene recognition result.

In addition, in order to achieve the above object, the application also proposes an information acquisition FSU-based monitoring device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the information acquisition FSU-based monitoring method as described above.

In addition, to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the information collection FSU based monitoring method as described above.

Furthermore, to achieve the above object, the present application provides a computer program product comprising a computer program which, when being executed by a processor, implements the steps of the information acquisition FSU based monitoring method as described above.

According to the technical scheme, a three-dimensional space model is constructed based on the arrangement points of the camera equipment and the arrangement points of the infrared detection equipment, abnormal signal data are acquired through the information acquisition FSU when the camera equipment and/or the infrared detection equipment detect signal changes, edge calculation analysis is carried out on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model, a scene recognition result is obtained, and a monitoring control strategy is triggered according to the scene recognition result. According to the application, the three-dimensional space model and the image recognition algorithm are used for carrying out edge calculation analysis on the abnormal signal data, so that the monitoring control strategy is triggered, the monitoring operation efficiency is improved, and the accuracy and stability of the monitoring data are ensured.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a monitoring method based on information collection FSU according to an embodiment of the present application;

fig. 2 is a schematic flow chart of a second embodiment of a monitoring method based on information collection FSU according to the present application;

FIG. 3 is a flow chart of the scene monitoring data fusion analysis of the present application;

fig. 4 is a schematic flow chart of a third embodiment of a monitoring method based on information collection FSU according to the present application;

Fig. 5 is a schematic block diagram of a monitoring device based on an information collection FSU according to an embodiment of the present application;

Fig. 6 is a schematic diagram of a device structure of a hardware operating environment related to a monitoring method based on an information collection FSU according to an embodiment of the present application.

The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.

For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.

The traditional movable ring machine room monitoring equipment monitors through intelligent equipment such as an independent processing access control device and an infrared device, generally only relevant alarm information can be reported to a platform for processing, and the machine room camera equipment is accessed to the platform through traditional movable ring gateway equipment, so that all information is converged and processed on the platform, the operation steps are complicated, and the working tasks of personnel are heavy. In a large-scale platform, the information quantity is too large, so that the accuracy and the real-time performance of the received monitoring data information are poor, and the problem of false alarm is also caused.

Therefore, in order to overcome the defects, the application provides a solution, and the edge calculation analysis is carried out on the abnormal signal data through the three-dimensional space model and the image recognition algorithm, so that the monitoring control strategy is triggered, the monitoring operation efficiency is improved, and the accuracy and the stability of the monitoring data are ensured.

It should be noted that, the execution body of each embodiment of the present application may be a computing service system with functions of data processing, network communication and program running, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic system capable of implementing the above functions, a monitoring system based on information collection FSU, etc. The following embodiments will be described with reference to a monitoring system based on information collection FSU.

Based on this, the embodiment of the application provides a monitoring method based on information collection FSU, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the monitoring method based on information collection FSU.

In this embodiment, the monitoring method based on the information collection FSU includes steps S10 to S30:

and S10, constructing a three-dimensional space model based on the image pickup equipment layout points and the infrared detection equipment layout points.

IT should be noted that, the machine room generally refers to a room or space where important IT devices such as a server, a network device, and a storage device are placed, and is a core part of a data center, a communication center, or an IT infrastructure of an enterprise, and the present application may be applied to a machine room environment, which is not limited in this embodiment.

In a machine room, the camera device is usually referred to as a high-definition camera or an intelligent monitoring camera, and is used for capturing image information in the machine room in real time. The infrared detection technology is based on the infrared radiation principle, all objects can emit infrared radiation at (-273.15 ℃) higher than absolute zero, when the moving condition of a living body occurs in the detection range, the infrared detection signal can change, and the intensity and the change direction of the infrared radiation emitted by the living body are analyzed. The three-dimensional space model is a digital model constructed based on three-dimensional geometric information, and can truly reflect information such as structures, equipment layout, space relations and the like in a machine room.

In step S20, when the imaging apparatus and/or the infrared detection apparatus detects a signal change, abnormal signal data is acquired through the information acquisition FSU.

It can be understood that the camera device captures dynamic scenes in the machine room in real time, and these are regarded as signal changes when abnormal changes occur in the image, such as the presence of living body movement, unusual movement of the device position, the camera being blocked, and the like.

At the same time, the infrared detection device is focused on living body detection and temperature monitoring in the machine room, and when the infrared detection device detects abnormal temperature changes, such as unauthorized living body activities, overheating of the device or potential fire hazards, these are also regarded as important signal changes. Once the camera device or infrared detection device detects a signal change, this information is immediately transmitted to the information acquisition FSU (Field Supervision Unit).

It should be noted that, the information collection FSU is an edge computing gateway device, which has a strong data processing capability, and can also perform real-time analysis and decision at the source of data generation. The method receives raw data from the camera equipment and the infrared detection equipment, and utilizes a built-in algorithm and model to rapidly process the data so as to identify potential abnormal signals.

In the application, the infrared detection equipment and the camera equipment can be ensured to be normally connected in a physical wiring mode when the information acquisition FSU is installed. The information acquisition FSU can normally configure access and data access of various devices on a software interface, video streams of the camera equipment can be accessed in a pull stream mode, and various image recognition algorithms downloaded by the edge computing gateway can be normally used. In addition, the visual field range shot by the arrangement point positions of the camera equipment needs to radiate mutually, so that the visual field blind area is reduced, and the range detected by the arrangement point positions of the infrared detection equipment needs to cover the whole machine room.

It can be appreciated that the edge computing gateway device has lower latency and higher real-time performance in data processing than conventional data center or cloud processing approaches. This means that when an abnormality occurs in the machine room, the information collection FSU can quickly capture these changes and immediately trigger a corresponding alarm or response mechanism, effectively shortening the time from abnormality detection to problem resolution.

And step S30, carrying out edge calculation analysis on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model to obtain a scene recognition result, and triggering a monitoring control strategy according to the scene recognition result.

It should be noted that an image recognition algorithm, such as a convolutional neural network or a deep learning model, may be deployed on the edge computing device, so as to recognize key information, such as personnel activities and device state changes, in the machine room, and detect abnormal signals, such as device overheating and personnel intrusion.

It should be understood that according to the edge calculation analysis result of the abnormal signal data, the scene recognition result is generated by combining the equipment layout and the position information in the three-dimensional space model, and according to the scene recognition result, the corresponding monitoring control strategy is automatically triggered.

For example, if a person intrusion is identified, an access control system or monitoring system may be activated to record and track the intruder, and if equipment overheating is identified, an alarm system may be triggered to notify the service personnel to process.

In addition, the monitoring control strategy may also include automatically adjusting equipment parameters (e.g., fan speed, air conditioning temperature, etc.), triggering an emergency response protocol, etc.

According to the embodiment, a three-dimensional space model is built based on the arrangement points of the camera equipment and the arrangement points of the infrared detection equipment, when the camera equipment and/or the infrared detection equipment detect signal changes, abnormal signal data are obtained through the information acquisition FSU, edge calculation analysis is carried out on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model, a scene recognition result is obtained, and a monitoring control strategy is triggered according to the scene recognition result. According to the application, the three-dimensional space model and the image recognition algorithm are used for carrying out edge calculation analysis on the abnormal signal data, so that the monitoring control strategy is triggered, the monitoring operation efficiency is improved, and the accuracy and stability of the monitoring data are ensured.

In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 2, the step S30 includes S301 to S305:

Step S301, acquiring real-time data through signal acquisition FSU based on the three-dimensional space model.

It will be appreciated that the camera or infrared device, while capable of detecting signal changes, may have some error or uncertainty in the detected data due to performance limitations of the device itself (e.g., resolution, detection range, sensitivity, etc.), as well as environmental factors (e.g., light changes, temperature fluctuations, noise interference, etc.). The information acquisition FSU is used for acquiring real-time data and comparing and verifying the real-time data with abnormal signal data, so that the accuracy of the data can be further improved.

Step S302, when the abnormal signal data are consistent with the real-time data, performing edge calculation analysis on the abnormal signal data through an image recognition algorithm to obtain a scene recognition result.

It should be understood that the anomaly signal data is consistent with the real-time data, meaning that the signal changes captured by the camera or infrared detection device (e.g., personnel intrusion, animal misentry, or device overheating, etc.) are synchronized in time with the real-time data (e.g., video stream, temperature readings, etc.) acquired simultaneously by the information acquisition FSU, and are mutually documented in content, i.e., both indicate that some anomaly event has occurred.

It can be appreciated that the edge calculation transfers the tasks of calculation and data storage from the centralized data center to the edge of the network, namely, to a position close to the data generation, and the abnormal signal data is processed and analyzed in real time near the camera or infrared detection equipment (namely, the edge), so that specific scenes occurring in the machine room, such as the entry of operation and maintenance personnel, the intrusion of illegal personnel, the false entry of other animals and the like, are identified by combining with an image identification algorithm.

Step S303, when the scene recognition result is that the operation and maintenance personnel enter or illegal personnel break, face information of the abnormal signal data is extracted.

It can be understood that if the scene recognition result is that an operation and maintenance person enters or intrudes into the scene, face information is extracted from the video stream or the image frame, that is, a face detection algorithm is used to locate a face region in the image, and a face feature extraction algorithm is used to extract key features of the face, such as positions and shapes of eyes, nose, mouth and the like.

Step S304, the face information is identified through an image identification algorithm, an identification result is obtained, and corresponding information is output according to the identification result.

It should be understood that the extracted face information is input into an image recognition algorithm, and the algorithm extracts features of the input face image and compares the features with features in a preset database.

It should be noted that the preset database includes facial features of known operation and maintenance personnel, and may also include facial features of illegal personnel in the blacklist.

According to the result of the feature comparison, the algorithm determines whether the input face image matches a feature in the database. If the matching is successful, the system can obtain a recognition result, namely the face is a known operation and maintenance person or an illegal person in a blacklist, and corresponding information is output according to the recognition result.

The step S304 includes:

It can be understood that whether the identified face is an illegal person or an operation and maintenance person is judged according to the identification result. When the identification result is an illegal person, the system immediately generates alarm information, and the information may include detailed information such as face images, entry time, place and the like of the illegal person. When the identification result is an operation and maintenance person, the system records the entry information of the operation and maintenance person, such as time, place and the like, and possibly outputs the identification result to a related management system.

Step S305, when the scene recognition result is that other animals are mistaken, obtaining the species category of the living body by analyzing the abnormal signal data, and generating corresponding alarm information.

It should be appreciated that when other animal errors are identified, the system will further analyze the anomaly signal data, which typically includes video streams, infrared signals, and the like. By analyzing the abnormal signal data, the system can identify species of living bodies, such as cats, dogs, birds and the like, which are mistakenly entered into the machine room.

Once the species category of the living being is determined, the system generates corresponding alarm information according to preset rules. The alarm information may include detailed information of the kind, number, location, time, etc. of the living body, and suggested countermeasures.

The step S305 includes:

It should be noted that the system deploys an animal model on the edge device, and the model can identify the characteristics of different animals through training of a large number of animal images and videos based on a deep learning technology.

It can be understood that the edge computing animal model performs feature extraction on the abnormal signal data, which includes key information such as the shape, color, texture, motion trail and the like of the animal. The extracted characteristic information is input into a classifier of the edge calculation animal model, and the classifier classifies the living body according to the characteristic information and maps the living body to corresponding species categories such as cat, dog, bird, rodent and the like.

For ease of understanding, reference is made to fig. 3, but the application is not limited thereto. Fig. 3 is a flow chart of the scene monitoring data fusion analysis process of the present application, including three scene fusion analysis process flows of operation and maintenance personnel entry, illegal personnel intrusion and other animal misentry, the following examples illustrate trigger scenes:

1. when the infrared detection equipment detects signal change and the sign of the intrusion of the living body exists, the rest infrared detection equipment is linked, and the approximate spatial position coordinates of the living body are calculated by analyzing the intensity and the change direction of infrared radiation emitted by the object.

2. After the information acquisition FSU acquires the space coordinate information of the living body, the linkage camera equipment monitors and captures the target monitoring area, and the captured picture calculates and analyzes whether the living body is a human or an animal through the edge.

(1) If the living body is human, the face is extracted to carry out face recognition, and whether the living body is a in-store maintainer is judged.

A) If the personnel are not in the library, alarm information is generated, and meanwhile, the image information of the illegal intruder is reported to the platform.

B) And if the operation personnel are in the warehouse, recording maintenance information.

(2) And if the living body is not human, invoking an algorithm model to analyze the species category of the living body and generating corresponding alarm information.

3. Reporting information to the platform to collect the analysis result of the FSU and the attached alarm information.

According to the embodiment, real-time data are acquired through the signal acquisition device based on the three-dimensional space model, when abnormal signal data are consistent with the real-time data, edge calculation analysis is carried out on the abnormal signal data through the image recognition algorithm, a scene recognition result is obtained, when the scene recognition result is that an operation and maintenance person enters or a illegal person enters, face information of the abnormal signal data is extracted, the face information is recognized through the image recognition algorithm, a recognition result is obtained, corresponding information is output according to the recognition result, when the scene recognition result is that other animals are mistaken, the types of life species are obtained through analysis of the abnormal signal data, and corresponding alarm information is generated, so that intelligent response to different scenes can be achieved, regional safety and order are effectively guaranteed, and false alarm and missing report are reduced.

In the third embodiment of the present application, the same or similar content as the first embodiment of the present application can be referred to the above description, and the description thereof will not be repeated. On this basis, referring to fig. 4, the step S20 may include steps S201 to S203:

In step S201, when the imaging apparatus and/or the infrared detection apparatus detects a signal change, the adjacent monitoring area is triggered by the signal change single monitoring area.

It will be appreciated that upon detection of a signal change by the imaging device and/or the infrared detection device, the abnormal signal data is first sent to a single monitoring area processing unit associated therewith which will analyze the trigger signal to determine the type, location and severity of the signal change. If the signal change is confirmed as abnormal (e.g., a person or animal misenters the machine room), the processing unit generates a linkage request to trigger a response in the vicinity of the monitored area.

The linkage request is sent to the processing units in the adjacent monitoring area, and the processing units can adjust the state of the monitoring equipment according to preset rules and strategies, such as adjusting the focal length and the direction of the camera or starting the infrared detection equipment. At the same time, they may trigger other security devices, such as access control systems, alarms, etc., to provide additional security.

Step S202, based on the interaction identification of the cradle head camera device and the adjacent monitoring area, the spatial position of the signal change is determined by acquiring the infrared radiation intensity and the change direction of the signal change.

It should be understood that the whole monitoring range can be determined through the camera equipment, the monitoring areas of the camera equipment radiate mutually, the adjacent interactive recognition is carried out, the infrared detection and the image algorithm recognition are overlapped, the point area and the surface are used, the characteristic that the monitoring area of the camera equipment of the holder is variable is relied on, and the single monitoring area triggers an alarm to drive the multi-monitoring area to carry out joint monitoring, so that the omnibearing monitoring of a machine room is realized.

It should be understood that the pan-tilt camera device can automatically adjust its focal length, direction or viewing angle according to a preset rule or algorithm, and adjust in real time according to the position and dynamic characteristics of signal change, so as to ensure image definition and coverage. When the focal length and the direction of the cradle head camera shooting equipment are adjusted, the movement track or direction of the signal change can be captured, and by analyzing the movement track or direction, the system can determine the movement path of the signal change in space, so that the position of the signal change can be positioned more accurately.

It should be noted that the infrared detection device measures the intensity of infrared radiation in the signal change region. The intensity of infrared radiation is a function of the temperature of the object and thus can provide additional information about signal changes, such as the body temperature of an intruder.

The position of the signal change in three-dimensional space can be calculated by combining the information of the infrared radiation intensity and the change direction.

Step S203, shooting the spatial position by using the image capturing apparatus, obtaining an abnormal picture, and transmitting the abnormal picture as abnormal signal data to an information collection FSU.

It will be appreciated that, depending on the determined spatial position, the imaging device will automatically adjust its focal length, direction, viewing angle etc. parameters to ensure that the position can be clearly captured. After the camera device is adjusted in place, a shooting command is triggered to capture images of the spatial location, which may contain specific features of the anomaly signal, such as the appearance of an intruder, the activity of an animal, etc. The photographed abnormal picture is regarded as abnormal signal data including visual information about the abnormal signal, and the abnormal signal data (i.e., the abnormal picture and additional information thereof) is then transmitted to the information collection FSU.

According to the embodiment, when the imaging device and/or the infrared detection device detect signal change, the signal change single monitoring area triggers the adjacent monitoring area, based on interactive identification of the cradle head imaging device and the adjacent monitoring area, the spatial position of the signal change is determined by acquiring the infrared radiation intensity and the change direction of the signal change, the imaging device is used for shooting the spatial position to obtain an abnormal picture, and the abnormal picture is used as abnormal signal data to be transmitted to the information acquisition FSU, so that linkage effect can be achieved, the sensitivity and accuracy of monitoring are enhanced, and the speed and efficiency of emergency response are improved.

In a third embodiment, the step S10 may include:

Or alternatively, the first and second heat exchangers may be,

It will be appreciated that, depending on the characteristics and requirements of the monitored area, suitable software built-in three-dimensional space models are selected, which may include preset elements such as buildings, terrains, indoor layouts, etc. Parameters (size, shape, position and the like) of the built-in software model are adjusted according to the actual layout point position information so as to ensure that the model accords with the actual situation. In the software, the adjusted built-in model is rendered into an initial three-dimensional space model. In addition, the support parameters of the built-in common three-dimensional space model can be modified, a built-in default model can also be used, and the device can be placed on the correct position of the model in a manual dragging mode.

Or the camera equipment of the cradle head is controlled to shoot image data of the monitoring area from a plurality of different visual angles, the whole monitoring area is covered, and the image data contain enough characteristic points to carry out three-dimensional reconstruction. Then, the shot image data is processed, including steps of feature extraction, matching, optimization and the like. And constructing an initial three-dimensional space model according to the processed image data by using image processing software or a three-dimensional modeling tool.

And determining the specific positions of the imaging equipment and the infrared detection equipment in the three-dimensional space model according to the layout point position information of the imaging equipment and the infrared detection equipment. In the three-dimensional space model, the imaging equipment and the infrared detection equipment are mapped to corresponding point positions according to the determined mapping rule, and the model of each equipment is marked. And then, verifying the accuracy of the mapping result, and ensuring that the positions of the image pickup device and the infrared detection device in the three-dimensional space model are consistent with the actual layout points. After the mapping and adjustment are completed, a final three-dimensional space model is generated.

According to the embodiment, the initial three-dimensional space model is built through the built-in software model based on the image capturing equipment layout point positions and the infrared detection equipment layout point positions, or the image data of different visual angles are obtained through the cradle head image capturing equipment based on the image capturing equipment layout point positions and the infrared detection equipment layout point positions, the initial three-dimensional space model is built according to the image data, the image capturing equipment and the infrared detection equipment are mapped to the corresponding point positions of the initial three-dimensional space model, and the three-dimensional space model is obtained, so that the space perception precision and the three-dimensional of the monitoring system can be improved.

It should be noted that the foregoing examples are only for understanding the present application, and do not constitute a limitation of the monitoring method based on the information collection FSU of the present application, and it is within the scope of the present application to make more simple transformations based on the technical concept.

The application also provides a monitoring device based on the information collection FSU, referring to FIG. 5, the monitoring device based on the information collection FSU comprises:

The model construction module 10 is used for constructing a three-dimensional space model based on the imaging equipment layout points and the infrared detection equipment layout points;

The data acquisition module 20 is configured to acquire abnormal signal data through the information acquisition FSU when the image capturing apparatus and/or the infrared detection apparatus detects a signal change;

The monitoring control module 30 is configured to perform edge calculation analysis on the abnormal signal data through an image recognition algorithm based on the three-dimensional space model, obtain a scene recognition result, and trigger a monitoring control policy according to the scene recognition result.

The monitoring device based on the information acquisition FSU provided by the application can solve the technical problem that the accuracy and stability of monitoring data cannot be ensured due to the limitations of the linkage capacity and the calculation capacity of the traditional gateway equipment by adopting the monitoring method based on the information acquisition FSU in the embodiment. Compared with the prior art, the monitoring device based on the information collection FSU has the same beneficial effects as the monitoring method based on the information collection FSU provided by the embodiment, and other technical features in the monitoring device based on the information collection FSU are the same as the features disclosed by the method of the embodiment, and are not repeated herein.

The application provides monitoring equipment based on an information acquisition FSU, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the monitoring method based on the information acquisition FSU in the first embodiment.

Referring now to fig. 6, a schematic diagram of an information acquisition FSU-based monitoring device suitable for use in implementing embodiments of the present application is shown. The monitoring device based on the information collection FSU in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Desction: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The information collection FSU-based monitoring device shown in fig. 6 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.

As shown in fig. 6, the information collection FSU-based monitoring apparatus may include a processing device 1001 (e.g., a central processor, a graphics processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. Also stored in RAM1004 are various programs and data required for the operation of the monitoring system based on the information acquisition FSU. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communicator 1009 may allow wireless or wired communication of the information collecting FSU-based monitoring device with other systems to exchange data. While information-gathering FSU-based monitoring devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.

The monitoring equipment based on the information acquisition FSU provided by the application adopts the monitoring method based on the information acquisition FSU in the embodiment, and can solve the technical problem that the accuracy and stability of monitoring data cannot be ensured due to the limitations of the linkage capacity and the calculation capacity of the traditional gateway equipment. Compared with the prior art, the monitoring equipment based on the information collection FSU has the same beneficial effects as the monitoring method based on the information collection FSU provided by the embodiment, and other technical features in the monitoring equipment based on the information collection FSU are the same as the features disclosed by the method of the embodiment, and are not repeated herein.

It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present application provides a computer readable storage medium having computer readable program instructions (i.e., a computer program) stored thereon for performing the information acquisition FSU-based monitoring method of the above-described embodiments.

The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.

The computer readable storage medium may be included in the information collecting FSU-based monitoring device or may exist alone without being incorporated into the information collecting FSU-based monitoring device.

The computer readable storage medium carries one or more programs, when the one or more programs are executed by monitoring equipment based on the information acquisition FSU, the monitoring equipment based on the information acquisition FSU is enabled to construct a three-dimensional space model based on the image capturing equipment layout point positions and the infrared detection equipment layout point positions, abnormal signal data are obtained through the information acquisition FSU when the image capturing equipment and/or the infrared detection equipment detect signal changes, the abnormal signal data are subjected to edge calculation analysis through an image recognition algorithm based on the three-dimensional space model, scene recognition results are obtained, and a monitoring control strategy is triggered according to the scene recognition results.

Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer program) for executing the monitoring method based on the information acquisition FSU, so that the technical problem that the accuracy and stability of monitoring data cannot be ensured due to the limitations of the linkage capacity and the calculation capacity of the traditional gateway equipment can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the monitoring method based on the information acquisition FSU provided by the embodiment, and are not repeated here.

The application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the information acquisition FSU based monitoring method as described above.

The computer program product provided by the application can solve the technical problem that the accuracy and stability of monitoring data cannot be ensured due to the limitations of the linkage capacity and the calculation capacity of the traditional gateway equipment. Compared with the prior art, the beneficial effects of the computer program product provided by the application are the same as the beneficial effects of the monitoring method based on the information acquisition FSU provided by the embodiment, and are not repeated here.

The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.

Claims

1. The monitoring method based on the information acquisition FSU is characterized by comprising the following steps of:

2. The method for monitoring FSU based on information acquisition according to claim 1, wherein said scene recognition result includes entry of operation and maintenance personnel, intrusion of illegal personnel and misentry of other animals, said performing edge calculation analysis on said abnormal signal data by image recognition algorithm based on said three-dimensional space model to obtain a scene recognition result, and triggering a monitoring control strategy according to said scene recognition result, comprising:

acquiring real-time data through signal acquisition FSU based on the three-dimensional space model;

3. The method for monitoring FSU based on information acquisition according to claim 2, wherein said step of recognizing said face information by an image recognition algorithm to obtain a recognition result, and outputting corresponding information according to said recognition result comprises:

4. The method for monitoring FSU based on information collection according to claim 2, wherein said step of obtaining a category of a living body species by analyzing said abnormal signal data and generating corresponding alarm information when said scene recognition result is other animal errors comprises:

5. An information acquisition FSU based monitoring method according to any one of claims 1 to 4, wherein the step of acquiring abnormal signal data by the information acquisition FSU when the imaging apparatus and/or the infrared detection apparatus detects a signal change comprises:

6. The information acquisition FSU-based monitoring method according to any one of claims 1 to 4, wherein the step of constructing a three-dimensional spatial model based on imaging device placement points and infrared detection device placement points includes:

Or alternatively, the first and second heat exchangers may be,

7. The monitoring system based on the information acquisition FSU is characterized by comprising a camera device, an infrared detection device and the information acquisition FSU;

8. A monitoring device based on information collection FSU is characterized in that, the monitoring device based on the information acquisition FSU comprises:

9. An information-collecting FSU-based monitoring device, comprising a memory, a processor and an information-collecting FSU-based monitoring program stored on the memory and executable on the processor, the information-collecting FSU-based monitoring program implementing the information-collecting FSU-based monitoring method according to any one of claims 1 to 6 when executed by the processor.

10. A storage medium, wherein an information-collecting FSU-based monitoring program is stored on the storage medium, and wherein the information-collecting FSU-based monitoring program, when executed by a processor, implements the information-collecting FSU-based monitoring method according to any one of claims 1 to 6.