Industrial park monitoring system based on big data

Abstract

The invention discloses an industrial park monitoring system based on big data, and the system comprises an information collection module which is used for collecting and inputting the correlation information of a loss event when an enterprise asset loss event occurs in an industrial park; the feature input module is used for inputting basic features, appearance features and other descriptive features of the lost object; the monitoring integration analysis module is used for carrying out integration analysis on the global monitoring of the industrial park according to the collected and recorded associated information and characteristics in the enterprise asset loss event; the abnormity identification module is used for carrying out abnormity identification judgment on the screened target monitoring; the monitoring and screening module is used for intelligently editing and screening monitoring pictures of the industrial park; and the global monitoring management module is used for carrying out global management on the monitoring picture and rapidly integrating and locking the target monitoring picture, and the system has the characteristics of high practicability and convenience in monitoring and query.

Description

Technical Field

[0001] This invention relates to the field of big data monitoring technology, specifically to a big data-based industrial park monitoring system. Background Technology

[0002] Surveillance systems are playing an increasingly important role in production and daily life, becoming an indispensable security barrier for people. The demand for surveillance in public areas such as banks, supermarkets, shopping malls, shops, factories, schools, residential communities, internet cafes, and public transportation is evident, and the crime-solving rate of public security organs across the country relying on video surveillance is rapidly increasing. With the construction of safe cities, surveillance systems will be integrated into our daily lives and fulfill their functions.

[0003] Industrial parks typically cover vast areas, house numerous companies, and experience complex asset flows, leading to frequent instances of asset loss and theft. Therefore, comprehensive and visualized management is essential. Consequently, designing a practical and user-friendly big data-based industrial park monitoring system is highly necessary. Summary of the Invention

[0004] The purpose of this invention is to provide an industrial park monitoring system based on big data to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for operating an industrial park monitoring system based on big data, comprising the following steps: Step S1: Build an industrial park monitoring and management platform and input related information and characteristics of enterprise asset loss events; Step S2: Retrieve the overall monitoring data of the industrial park and integrate and analyze the overall monitoring data of the industrial park based on the relevant information and characteristics of the collected and entered enterprise asset loss events; Step S3: Perform anomaly identification and judgment on the filtered target monitoring, and prioritize the viewing of monitoring screens; Step S4: Edit the screened surveillance footage, perform global management of the surveillance footage based on human screening and judgment, and quickly integrate and lock the target surveillance footage.

[0006] According to the above technical solution, step S1 specifically includes: entering the associated information of the enterprise asset loss event into the monitoring and management platform, wherein the associated information mainly includes the loss time, loss location, and specific lost items of this event; After entering the associated information of the completed event, start entering the characteristic information. The characteristic information mainly includes the basic characteristics, appearance characteristics and other descriptive characteristics of the lost object.

[0007] According to the above technical solution, in step S1, the feature input method mainly includes: using a 3D scanning unit to scan the contour features of the lost cylindrical object, scanning and identifying the color distribution of the object, and fitting the obtained object color area distribution into the 3D contour features; when the lost object is a small or easily concealed object, the party involved in the incident can provide diverse features for input into the monitoring and management platform.

[0008] According to the above technical solution, step S2 further includes: First, the time of loss is obtained, and the global monitoring is filtered to the time interval in which the loss occurred. Second, the location of the loss, the entrance and exit of the industrial park, and possible locations are obtained, and the escape trajectory after the theft is simulated. The comprehensive route recommendation is made in combination with the routes in the industrial park, and the corresponding monitoring images on the three routes with the highest comprehensive recommendation are selected as the images to be retained.

[0009] Next, the feature elements are analyzed. The entered features are matched with the initially screened monitoring screens. The monitoring screens are scanned frame by frame to find elements that match the features. When the matching degree of the element in the scanned screen is greater than 70%, the element is marked. After feature matching, the monitoring screens containing all marked elements are filtered and retained.

[0010] According to the above technical solution, step S3 further includes: acquiring the integrated and analyzed monitoring screen, dynamically fitting the walking speed of the marked persons in the monitoring screen one by one, then dynamically fitting the walking speed of the unmarked pedestrians in the monitoring screen and then normalizing it to obtain the average walking speed of vehicles and the walking speed of the marked persons under the same monitoring screen, then making anomaly judgments on the movement speed of all marked persons, and judging abnormal behavior for those whose movement speed is greater than 30% of the movement speed of the flow of people in the same screen, making the marking process more severe, and transmitting the electrical signal to the monitoring management platform.

[0011] According to the above technical solution, step S3 specifically includes: acquiring the global monitoring screen of the industrial park on the monitoring management platform, splicing the global monitoring screen, pedestrian walking speed, and industrial park route together in time sequence, and predicting the monitoring screen where the pedestrian will appear next; then dynamically tracking the walking trajectory of the marked pedestrian through the monitoring screen.

[0012] According to the above technical solution, step S4 mainly includes: for pedestrians who are marked with heavy emphasis, the heavy emphasis can be superimposed. When the notification meets multiple conditions in step S3, the heavy emphasis corresponds to the three colors "yellow", "orange" and "red" respectively, and the heavy emphasis is applied "one", "two" and "three" times.

[0013] A big data-based industrial park monitoring system includes: The information collection module is used to collect and record relevant information about the loss of corporate assets when such an event occurs in the industrial park. The feature input module is used to input the basic features, appearance features and other descriptive features of lost objects; The monitoring integration and analysis module is used to integrate and analyze the overall monitoring of the industrial park based on the related information and characteristics of the collected and entered enterprise asset loss events; The anomaly detection module is used to identify and judge anomalies in the filtered target monitoring. The monitoring and screening module is used to intelligently edit and screen surveillance footage from the industrial park. The global monitoring and management module is used to manage the monitoring screen globally and quickly integrate and lock the target monitoring screen.

[0014] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By integrating and analyzing the overall monitoring images of the industrial park, and using multi-level matching and filtering to select monitoring images, this invention can greatly narrow the scope of monitoring retrieval, avoid blindly searching and thus increase the time spent on searching, leading to higher retrieval costs. At the same time, it further prioritizes the viewing of the integrated and filtered monitoring images, and strengthens the marking of marked pedestrians with abnormal movement trajectories, abnormal movement speeds, and abnormal movement scenes, thereby further improving the accuracy of the search targets and reducing the search time. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart illustrating the operation method of an industrial park monitoring system based on big data, provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of an industrial park monitoring system based on big data, provided in Embodiment 2 of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1, Figure 1This is a flowchart illustrating an operation method for an industrial park monitoring system based on big data, provided in Embodiment 1 of the present invention. This embodiment is applicable to industrial park monitoring and retrieval scenarios. The method can be executed by the industrial park monitoring system based on big data provided in this embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps: S1. Build an industrial park monitoring and management platform to record relevant information and characteristics of corporate asset loss events.

[0018] For example, in this embodiment of the invention, the associated information of the enterprise asset loss event is entered into the monitoring and management platform. The associated information mainly includes the time of loss, the location of loss, and the specific items lost in this event. The aforementioned associated information such as time, location, and items can be entered as unique values ​​or as range values ​​according to the actual situation of this event.

[0019] After entering the associated information of the completed event, the characteristic information is entered. The characteristic information mainly includes the basic features, appearance features, and other descriptive features of the lost object. The characteristic entry methods mainly include: using a 3D scanning unit to scan the contour features of cylindrical objects, scanning and identifying the color distribution of the object, and fitting the obtained color area distribution of the object into the 3D contour features. When the lost object is small or easily concealed, the parties involved can provide diverse characteristics to the monitoring and management platform, including the movement trajectory of the lost object after partial loss, or descriptive features such as the appearance of suspicious persons. For example, after a company's mobile phone is lost, the "Find" function on the phone can be used to obtain part of its movement trajectory within the industrial park. Suspicious individuals can then enter the trajectory features into the monitoring and management platform to achieve rapid location of the target in the monitoring footage.

[0020] S2. Retrieve the overall monitoring data of the industrial park and integrate and analyze the overall monitoring data of the industrial park based on the related information and characteristics of the collected and entered enterprise asset loss events; In this embodiment of the invention, the time of loss is first obtained, and the global monitoring is filtered to the time interval in which the loss occurred based on the time of loss. Then, the location of loss, the entrance and exit of the industrial park, and possible locations are obtained, and the escape trajectory after the theft is fitted. The comprehensive route recommendation is combined with the route in the industrial park, and the monitoring images corresponding to the three routes with the highest comprehensive recommendation degree are selected as the filtered and retained images.

[0021] Next, the feature elements are analyzed. The entered features are matched with the initially screened monitoring screens. The monitoring screens are scanned frame by frame to find elements that match the features. When the matching degree of the element in the scanned screen is greater than 70%, the element is marked. After feature matching, the monitoring screens containing all marked elements are filtered and retained.

[0022] By integrating and analyzing the overall monitoring footage of the industrial park through the above steps, and then using multi-level matching and filtering to select the monitoring footage, the scope of monitoring retrieval can be greatly narrowed, avoiding blind searches that would increase search time and lead to higher retrieval costs.

[0023] S3. Perform anomaly identification and judgment on the screened target monitoring, and prioritize the viewing of monitoring screens. In this embodiment of the invention, the monitoring screen after integration and analysis is acquired, and the walking speed of the marked persons in the monitoring screen is dynamically fitted one by one. Then, the walking speed of the unmarked pedestrians in the monitoring screen is dynamically fitted and normalized to obtain the average walking speed of vehicles and the walking speed of the marked persons under the same monitoring screen. Then, the movement speed of all marked persons is judged to be abnormal. For those whose movement speed is greater than 30% of the movement speed of the flow of people in the same screen, abnormal behavior is judged, the marking is strengthened, and the electrical signal is transmitted to the monitoring management platform.

[0024] The monitoring management platform acquires global monitoring footage of the industrial park. This footage, along with pedestrian walking speed and routes within the park, is stitched together in chronological order to predict the next location a pedestrian might appear in. This adaptively integrates the monitoring footage of the industrial park. The platform then dynamically tracks the marked pedestrian's trajectory using the monitoring footage. The primary tracking method involves deep scanning to record the marked pedestrian's body contour and facial features within the current monitoring frame. This captures the pedestrian's walking speed and the time point corresponding to the next possible location in the route. Within the time interval t before and after this time point, the monitoring management platform performs high-definition identification of the possible location's monitoring footage. If the marked pedestrian's body contour and facial features are not identified within this time interval, the marked pedestrian's trajectory is deemed abnormal. If the marked pedestrian appears in other unreasonable monitoring footage or at unusual times, the marked pedestrian is further highlighted, and the signal is transmitted to the monitoring management platform.

[0025] In other embodiments of the present invention, the number of people in the monitored image is also assessed. When a marked pedestrian appears alone in a monitored image, further abnormal judgment is made, and the marking is intensified. By identifying and judging the abnormal behavior of marked individuals, the suspiciousness of marked individuals can be compared through behavioral actions. People who commit theft are often more likely to exhibit abnormal behavior than ordinary people.

[0026] Furthermore, through the above steps, after integrating and filtering the monitoring footage of the industrial park, the viewing priority of the integrated and filtered monitoring footage can be further sorted out. Pedestrians with abnormal movement trajectories, abnormal movement speeds, or abnormal movement scenes will be marked more heavily, thereby further improving the accuracy of the search targets and reducing the search time.

[0027] S4. After editing and screening the surveillance footage, based on human screening and judgment, perform global management of the surveillance footage and quickly integrate and lock the target surveillance footage.

[0028] For example, in this embodiment of the invention, for pedestrians whose markings are emphasized, the emphasis can be superimposed. When multiple conditions in step S3 are met, the marking is emphasized according to the three colors "yellow," "orange," and "red," respectively, with emphasis applied "one," "two," and "three" times. Furthermore, through the industrial park monitoring and management platform, the system can prioritize screening those marked with red markings, gradually expanding the screening scope to include those marked with orange, yellow, and ordinary markings, thereby greatly improving the practicality of the industrial park monitoring system.

[0029] In other embodiments of the present invention, the personnel parameter feature settings can be changed at any time based on the intermediate value of the monitoring query output, so as to realize a multi-level non-directional query mode and avoid excessive or invalid filtering caused by a certain feature element.

[0030] Example 2: This invention provides a big data-based industrial park monitoring system. Figure 2 This is a schematic diagram of the module composition of an industrial park monitoring system based on big data, provided in Embodiment 2 of the present invention. Figure 2 As shown, the system includes: The information collection module is used to collect and record relevant information about the loss of corporate assets when such an event occurs in the industrial park. The feature input module is used to input the basic features, appearance features and other descriptive features of lost objects; The monitoring integration and analysis module is used to integrate and analyze the overall monitoring of the industrial park based on the related information and characteristics of the collected and entered enterprise asset loss events; The anomaly detection module is used to identify and judge anomalies in the filtered target monitoring. The monitoring and screening module is used to intelligently edit and screen surveillance footage from the industrial park. The global monitoring and management module is used to manage the monitoring screen globally and quickly integrate and lock the target monitoring screen.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0032] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for operating an industrial park monitoring system based on big data, characterized in that: The method specifically includes the following steps: Step S1: Build an industrial park monitoring and management platform and input related information and characteristics of enterprise asset loss events; Step S2: Retrieve the overall monitoring data of the industrial park and integrate and analyze the overall monitoring data of the industrial park based on the related information and characteristics of the collected and entered enterprise asset loss events; Step S3: Perform anomaly identification and judgment on the filtered target monitoring, and prioritize the viewing of monitoring screens; Step S4: Edit the screened surveillance footage, perform global management of the surveillance footage based on human screening and judgment, and quickly integrate and lock the target surveillance footage. 2.The big data-based industrial park monitoring system operation method of claim 1, wherein: Step S1 specifically includes: entering the associated information of the enterprise asset loss event into the monitoring and management platform, wherein the associated information mainly includes the time of loss, the location of loss, and the specific items lost in this event; After entering the associated information of the completed event, start entering the characteristic information. The characteristic information mainly includes the basic characteristics, appearance characteristics and other descriptive characteristics of the lost object.

3. The method for operating a big data-based industrial park monitoring system according to claim 2, characterized in that: In step S1, the feature input method mainly includes: using a 3D scanning unit to scan the contour features of the lost cylindrical object, scanning and identifying the color distribution of the object, and fitting the obtained object color area distribution into the 3D contour features; when the lost object is a small or easily concealed object, the parties involved in the incident can provide diverse features for input into the monitoring and management platform.

4. The method for operating a big data-based industrial park monitoring system according to claim 1, characterized in that: Step S2 further includes: First, the time of loss is obtained, and the global monitoring is filtered to the time interval in which the loss occurred. Second, the location of the loss, the entrance and exit of the industrial park, and possible locations are obtained, and the escape trajectory after the theft is simulated. The comprehensive route recommendation is made in combination with the routes in the industrial park, and the monitoring images corresponding to the three routes with the highest comprehensive recommendation are selected as the images to be retained. Next, the feature elements are analyzed. The entered features are matched with the initially screened monitoring screens. The monitoring screens are scanned frame by frame to find elements that match the features. When the matching degree of the element in the scanned screen is greater than 70%, the element is marked. After feature matching, the monitoring screens containing all marked elements are filtered and retained.

5. The method for operating a big data-based industrial park monitoring system according to claim 1, characterized in that: Step S3 further includes: acquiring the integrated and analyzed monitoring screen, dynamically fitting the walking speed of the marked persons in the monitoring screen one by one, then dynamically fitting the walking speed of the unmarked pedestrians in the monitoring screen and then normalizing it to obtain the average walking speed of vehicles and the walking speed of the marked persons under the same monitoring screen, then making anomaly judgments on the movement speed of all marked persons, and judging abnormal behavior for those whose movement speed is greater than 30% of the movement speed of the flow of people in the same screen, making the marking process more severe, and transmitting the electrical signal to the monitoring management platform.

6. The method for operating a big data-based industrial park monitoring system according to claim 5, characterized in that: Step S3 specifically includes: acquiring the global monitoring screen of the industrial park from the monitoring management platform, stitching together the global monitoring screen, pedestrian walking speed, and industrial park route in chronological order, and predicting the next monitoring screen where the pedestrian will appear; then dynamically tracking the walking trajectory of the marked pedestrian through the monitoring screen.

7. The method for operating a big data-based industrial park monitoring system according to claim 1, characterized in that: Step S4 mainly includes: for pedestrians whose marking is emphasized, the emphasis processing can be superimposed. When the notification meets multiple conditions in step S3, the marking is emphasized according to the three colors "yellow", "orange" and "red", and the emphasis is increased "one", "two" and "three" times respectively.

8. A big data-based industrial park monitoring system, characterized in that: The system includes: The information collection module is used to collect and record relevant information about the loss of corporate assets when such an event occurs in the industrial park. The feature input module is used to input the basic features, appearance features and other descriptive features of lost objects; The monitoring integration and analysis module is used to integrate and analyze the overall monitoring of the industrial park based on the related information and characteristics of the collected and entered enterprise asset loss events; The anomaly detection module is used to identify and judge anomalies in the filtered target monitoring. The monitoring and screening module is used to intelligently edit and screen surveillance footage from the industrial park. The global monitoring and management module is used to manage the monitoring screen globally and quickly integrate and lock the target monitoring screen.

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