A surveillance video transmission system based on video content analysis

Abstract

The present invention discloses a monitoring video transmission system based on video content analysis. A video acquisition unit acquires real-time monitoring video, and then a video analysis unit is used to perform initial phase analysis on the real-time monitoring video to determine a number of initial threshold videos, second threshold videos and super-threshold videos; then a video splitting unit is used to split the real-time monitoring video, the initial threshold video, the second threshold video and the super-threshold video to obtain a segmented video sequence and a number of hetero-directional videos; a transmission analysis unit is used to perform transmission operations on the segmented video sequence and the hetero-directional video in combination with a channel unit, and transmits them to an object analysis unit, and the transmission analysis unit also transmits the backup video to a storage unit for backup storage; this facilitates timely supplementation of missing videos when verifying the integrity of the video later; the present invention is simple, effective and easy to use.

Description

Technical Field

[0001] The invention belongs to the field of monitoring video transmission, relates to monitoring video content analysis technology, and specifically is a monitoring video transmission system based on video content analysis. Background Art

[0002] The patent with publication number CN105338325A discloses a monitoring video transmission method and device, the method comprising: a network camera detects the quality of 4G signal; when the 4G signal falls back to 3G and it is necessary to upload the live monitoring video, the auxiliary stream of the live monitoring video is sent to the streaming media server, and the mainstream video data of the monitoring video is stored locally until the 4G signal is restored from 3G to 4G; when the 4G signal is restored from 3G to 4G, the mainstream of the live monitoring video is sent to the streaming media server, and the locally stored mainstream video data of the monitoring video during the period when the 4G signal falls back to 3G and the 4G signal is restored from 3G to 4G is sent to the monitoring center, so that the storage server of the monitoring center stores the mainstream video data of the monitoring video. The application embodiment can ensure the clarity of the monitoring video stored in the storage server while ensuring the viewing demand of the live monitoring video, and improve the reliability of the video data during the subsequent investigation.

[0003] However, for surveillance videos, how to perform content analysis and back up key content based on the content analysis to avoid packet loss during transmission or other problems that cause video loss during transmission; based on this, a solution is provided. Summary of the invention

[0004] The purpose of the present invention is to provide a monitoring video transmission system based on video content analysis.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A surveillance video transmission system based on video content analysis, comprising:

[0007] The video acquisition unit is used to acquire the real-time monitoring video and transmit the real-time monitoring video to the video analysis unit. The video analysis unit receives the real-time monitoring video transmitted by the video acquisition unit and performs a preliminary phase analysis on the real-time monitoring video. The specific method of the preliminary phase analysis is as follows:

[0008] Step 1: Get the real-time surveillance video, then retrieve the past surveillance video and perform inertial analysis to get the regular upper limit;

[0009] Step 2: After obtaining the regular upper limit, the value obtained by multiplying the regular upper limit by 0.05 is marked as the initial threshold value, and the value obtained by multiplying the regular upper limit by 0.85 is marked as the second threshold value;

[0010] Step 3: After obtaining the real-time monitoring video, segment the real-time monitoring video and analyze it to determine a number of initial threshold videos, second threshold videos, and over-threshold videos;

[0011] The video parsing unit is also used to transmit the real-time monitoring video, the initial threshold video, the second threshold video and the super threshold video to the video splitting unit, and the video splitting unit is used to split the real-time monitoring video, the initial threshold video, the second threshold video and the super threshold video to obtain a segmented scattered video sequence and a plurality of segments of heterogeneous videos;

[0012] The video splitting unit transmits the segmented video sequence and the heterogeneous video to the transmission analysis unit via the processor. The transmission analysis unit is used to perform a transmission operation on the segmented video sequence and the heterogeneous video in combination with the channel unit and transmits them to the object analysis unit. The specific transmission operation is as follows:

[0013] The first thirty percent of the scattered video sequence is rounded to an integer and transmitted to the object analysis unit, and the corresponding scattered video is marked as a backup video; then the scattered videos in the remaining scattered video sequence are transmitted to the object analysis unit;

[0014] After the different-direction video is transmitted to the object analysis unit, the different-direction video is also backed up to obtain a backup video;

[0015] The transmission analysis unit is used to transmit the backup video to the storage unit for backup storage; the object analysis unit is used to perform frame loss analysis on all the videos after receiving them. The specific method of frame loss analysis is as follows:

[0016] Analyze the scattered videos and the abnormal videos of the corresponding backup videos. When unreasonable time progress occurs, that is, when the actual playback time of a one-second video is less than one second, mark the time period of the corresponding scattered videos and the abnormal videos as the retransmission time period;

[0017] The object analysis unit is used to transmit the retransmission time period to the processor, and the processor is used to retrieve the video corresponding to the retransmission time period from the storage unit and retransmit it to the object analysis unit via the transmission analysis unit and the channel unit.

[0018] Furthermore, the specific method of inertia analysis in step 1 is:

[0019] S1: Inertia extraction is performed on the controlled area. The controlled area is the corresponding monitored target area. The specific inertia extraction is to first obtain the flow of people in the controlled area within the T1 time, where T1 is the preset value; the flow of people in the controlled area every day. The specific method of obtaining the flow of people is as follows:

[0020] Based on face recognition, if any face appears in the controlled area and is not detected again within T2 time, the flow rate value will be automatically increased by 1, where T2 is the preset value;

[0021] S2: Get the personal flow value of T1 and mark it as a personal flow value group Ri, i=1, ..., T1;

[0022] S3: Then automatically find the mean of Ri and mark it as P. Then use the formula to calculate the square difference W of Ri. The specific calculation formula is:

[0023]

[0024] S4: When W exceeds X1, select the corresponding Ri in descending order of |Ri-P|. After that, delete each Ri value when it is selected, and then recalculate the W value. If the W value still exceeds X1, continue to select the next corresponding Ri and delete it until the W value does not exceed X1. The maximum value of the deleted Ri at this time is marked as the regular upper limit.

[0025] X1 is the preset value.

[0026] Furthermore, the specific method of segmentation analysis in step 3 is as follows:

[0027] S01: Starting from the set time, the number of faces in the real-time monitoring video will be automatically obtained. When the number of faces in the real-time monitoring video is not zero and is lower than the initial threshold, the video of the corresponding stage will be continuously obtained until the number of faces is greater than or equal to the initial threshold. Then, the video will be intercepted and all videos below the initial threshold will be marked as initial threshold videos.

[0028] S02: obtaining a number of initial threshold videos;

[0029] S03: Then, the real-time surveillance video with the number of faces exceeding the initial threshold value but not exceeding the second threshold value is intercepted and marked as the second threshold video;

[0030] S04: obtaining a number of second-threshold videos;

[0031] S05: then intercepting the real-time surveillance video with the number of faces exceeding the second threshold value, and marking it as a super video;

[0032] S06: Obtain several segments of super-threshold videos.

[0033] Furthermore, the specific splitting process is as follows:

[0034] SS1: Get the real-time surveillance video that is not classified into any initial threshold video, second threshold video, or over-threshold video, and mark it as the remaining video;

[0035] SS2: Get the remaining videos, perform dynamic analysis, and obtain a hetero-directional video group consisting of several hetero-directional videos based on the generated core difference signals;

[0036] SS3: Then, the initial threshold video, the second threshold video, and the super threshold video are processed in the same manner according to the principle of step SS2 to obtain the numbers of core difference signals generated by the initial threshold video, the second threshold video, and the super threshold video, respectively, which are marked as the corresponding initial threshold number, the second threshold number, and the super threshold number, respectively;

[0037] SS4: Then, several segments of initial threshold videos, second threshold videos, and super threshold videos are uniformly marked as scattered videos, and the doubling value is defined according to the initial threshold videos, second threshold videos, and super threshold videos.

[0038] The doubling values ​​of all first-threshold videos are marked as 1.75, the doubling values ​​of all second-threshold videos are marked as 1, and the doubling value of super-threshold videos is defined as 1.65;

[0039] SS5: Calculate the crowd value of all scattered videos. The crowd value is equal to the corresponding initial threshold, second threshold and super threshold multiplied by 1.75, 1 and 1.65 respectively.

[0040] SS6: Get the crowd values ​​of all the scattered videos, and sort the scattered videos in descending order of crowd values ​​to get a scattered video sequence.

[0041] Furthermore, the specific method of dynamic analysis in step SS2 is:

[0042] SS21: Get all the remaining videos. The remaining videos specifically exist in several groups where the real-time monitoring videos are divided by the initial threshold video, the second threshold video and the super threshold video. Compare the picture of the previous frame with the picture of the next frame. When the proportion of the difference between the two accounts for B1 proportion or more of the total area of ​​the next frame, generate an initial difference signal; B1 is a preset value;

[0043] SS22: Then mark the previous frame as the starting frame, and then obtain the X2 frames after the picture, X2 is a preset value, and mark the selected X2 frames after the picture as the next frame. If the proportion of the difference between the next frame and the starting frame accounts for B1 proportion of the total area of ​​the next frame or more, a core difference signal is generated;

[0044] SS23: Mark the corresponding difference part as an abnormal mark, obtain all videos from the appearance to the disappearance of the abnormal mark, mark them as hetero-directional videos, and all hetero-directional videos constitute a hetero-directional video group.

[0045] Furthermore, it also includes a management unit,

[0046] The management unit is connected to the processor for inputting all preset values.

[0047] Beneficial effects of the present invention:

[0048] The present invention obtains real-time monitoring video through a video acquisition unit, and then uses a video analysis unit to perform initial phase analysis on the real-time monitoring video to determine a number of initial threshold videos, second threshold videos and super threshold videos; then uses a video splitting unit to split the real-time monitoring video, the initial threshold video, the second threshold video and the super threshold video to obtain a segmented scattered video sequence and a number of heterogeneous videos;

[0049] The transmission analysis unit is then used to combine with the channel unit to perform transmission operations on the segmented video sequence and the heterogeneous video, and transmit them to the object analysis unit. At the same time, the transmission analysis unit also transmits the backup video to the storage unit for backup storage; this is convenient for subsequent verification of video integrity, and if there is any missing, it can be supplemented in time; the present invention is simple, effective, and easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0051] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0052] like Figure 1 As shown, a surveillance video transmission system based on video content analysis includes

[0053] Video acquisition unit, video parsing unit, video splitting unit, processor, transmission analysis unit, channel unit, object analysis unit, display unit, storage unit and management unit;

[0054] The video acquisition unit is used to acquire the real-time monitoring video and transmit the real-time monitoring video to the video analysis unit. The video analysis unit receives the real-time monitoring video transmitted by the video acquisition unit and performs an initial phase analysis on the real-time monitoring video. The specific method of the initial phase analysis is:

[0055] Step 1: Get the real-time surveillance video, then retrieve the past surveillance video and perform inertial analysis, specifically:

[0056] S1: Inertia extraction is performed on the controlled area. The controlled area is the corresponding monitored target area. The specific inertia extraction is to first obtain the value of the flow of people in the controlled area every day within the T1 period. Here, T1 is a preset value, which is usually three months. The specific management personnel can also set it to other values; the flow of people in the controlled area every day. The specific way to obtain the flow of people is as follows:

[0057] Based on face recognition, the number of faces that appear every day is obtained. Several cameras are set up in the controlled area to obtain the corresponding face situation. If any face appears in the controlled area and is not detected again within T2 time until it disappears, the flow value will be automatically increased by one. Here, T2 is a preset value, and the specific value can be set to two minutes or other values ​​desired by the management personnel; the face recognition here does not involve the collection of user privacy, but is only used for mutual comparison, and the face information is only temporarily stored, and the temporarily stored face information is cleared once at midnight every day;

[0058] S2: Get the personal flow value of T1 and mark it as a personal flow value group Ri, i=1, ..., T1;

[0059] S3: Then automatically find the mean of Ri and mark it as P. Then use the formula to calculate the square difference W of Ri. The specific calculation formula is:

[0060]

[0061] S4: When W exceeds X1, select the corresponding Ri in descending order of |Ri-P|. After that, delete each Ri value when it is selected, and then recalculate the W value. If the W value still exceeds X1, continue to select the next corresponding Ri and delete it until the W value does not exceed X1. The maximum value of the deleted Ri at this time is marked as the regular upper limit.

[0062] X1 is a preset value, which is set by the management personnel. The result can be inferred from any reasonable data set of the management personnel. The specific details are not publicly demonstrated here.

[0063] Step 2: After obtaining the regular upper limit, the value obtained by multiplying the regular upper limit by 0.05 is marked as the initial threshold value, and the value obtained by multiplying the regular upper limit by 0.85 is marked as the second threshold value;

[0064] Step 3: After obtaining the real-time surveillance video, perform segment analysis on the real-time surveillance video. The specific method of segment analysis is as follows:

[0065] S01: Starting from the set time, the number of faces in the real-time monitoring video will be automatically obtained. When the number of faces in the real-time monitoring video is not zero and is lower than the initial threshold, the video of the corresponding stage will be continuously obtained until the number of faces is greater than or equal to the initial threshold. Then, the video will be intercepted and all videos below the initial threshold will be marked as initial threshold videos.

[0066] S02: obtaining a number of initial threshold videos;

[0067] S03: Then, the real-time surveillance video with the number of faces exceeding the initial threshold value but not exceeding the second threshold value is intercepted and marked as the second threshold video;

[0068] S04: obtaining a number of second-threshold videos;

[0069] S05: then intercepting the real-time surveillance video with the number of faces exceeding the second threshold value, and marking it as a super video;

[0070] S06: obtaining a number of over-threshold videos;

[0071] Step 4: Obtain a number of initial threshold videos, second threshold videos and super threshold videos;

[0072] The video parsing unit is also used to transmit the real-time monitoring video, the initial threshold video, the second threshold video and the over-threshold video to the video splitting unit, and the video splitting unit is used to split the real-time monitoring video, the initial threshold video, the second threshold video and the over-threshold video. The specific splitting method is:

[0073] SS1: Get the real-time surveillance video that is not classified into any initial threshold video, second threshold video, or over-threshold video, and mark it as the remaining video;

[0074] SS2: Get the remaining videos and perform dynamic analysis, specifically:

[0075] SS21: Get all the remaining videos. The remaining videos specifically exist in several groups where the real-time monitoring videos are divided by the initial threshold video, the second threshold video and the super threshold video. Compare the picture of the previous frame with the picture of the next frame. When the proportion of the difference between the two accounts for B1 proportion or more of the total area of ​​the next frame, generate an initial difference signal; B1 is a preset value;

[0076] SS22: Then mark the previous frame as the starting frame, and then obtain the X2 frames again. X2 is a preset value, usually the corresponding frame after 1 minute. You can also set other values ​​according to actual needs. Mark the selected X2 frames as the next frame. If the difference between the next frame and the starting frame accounts for B1 ratio or more of the total area of ​​the next frame, a core difference signal is generated;

[0077] SS23: Mark the corresponding difference part as an abnormal mark, obtain all videos from the appearance to the disappearance of the abnormal mark, mark them as different-direction videos, and all different-direction videos constitute a different-direction video group;

[0078] SS3: Then, the initial threshold video, the second threshold video, and the super threshold video are processed in the same manner according to the principle of step SS2 to obtain the numbers of core difference signals generated by the initial threshold video, the second threshold video, and the super threshold video, respectively, which are marked as the corresponding initial threshold number, the second threshold number, and the super threshold number, respectively;

[0079] SS4: Then, several segments of initial threshold videos, second threshold videos, and super threshold videos are uniformly marked as scattered videos, and the doubling value is defined according to the initial threshold videos, second threshold videos, and super threshold videos.

[0080] The doubling values ​​of all first-threshold videos are marked as 1.75, the doubling values ​​of all second-threshold videos are marked as 1, and the doubling value of super-threshold videos is defined as 1.65;

[0081] SS5: Calculate the crowd value of all scattered videos. The crowd value is equal to the corresponding initial threshold, second threshold and super threshold multiplied by 1.75, 1 and 1.65 respectively.

[0082] SS6: Get the crowd values ​​of all the scattered videos, and sort the scattered videos in descending order of the crowd values ​​to get a scattered video sequence;

[0083] The video splitting unit transmits the segmented video sequence and the heterogeneous video to the transmission analysis unit via the processor. The transmission analysis unit is used to perform a transmission operation on the segmented video sequence and the heterogeneous video in combination with the channel unit and transmits them to the object analysis unit. The specific transmission operation is as follows:

[0084] The first thirty percent of the scattered video sequence is rounded to an integer and transmitted to the object analysis unit, and the corresponding scattered video is marked as a backup video; then the scattered videos in the remaining scattered video sequence are transmitted to the object analysis unit;

[0085] After the different-direction video is transmitted to the object analysis unit, the different-direction video is also backed up to obtain a backup video;

[0086] The transmission analysis unit is used to transmit the backup video to the storage unit for backup storage; the object analysis unit is used to perform frame loss analysis on all the videos after receiving them. The specific method of frame loss analysis is as follows:

[0087] Analyze the scattered videos and the abnormal videos of the corresponding backup videos. When the video freezes or the time progress is unreasonable, that is, the actual playback time of one second of video is less than one second, mark the time period of the corresponding scattered videos and abnormal videos as the retransmission time period;

[0088] The object analysis unit is used to transmit the retransmission time period to the processor, and the processor is used to retrieve the video corresponding to the retransmission time period from the storage unit and retransmit it to the object analysis unit via the transmission analysis unit and the channel unit;

[0089] The management unit is connected to the processor for inputting all preset values.

[0090] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A surveillance video transmission system based on video content analysis, characterized in that: include: The video analysis unit is used to perform an initial phase analysis on the real-time monitoring video to determine a number of initial threshold videos, second threshold videos and over-threshold videos; the specific method of the initial phase analysis is: Step 1: Get the real-time surveillance video, then retrieve the past surveillance video and perform inertial analysis to get the regular upper limit; Step 2: After obtaining the regular upper limit, the value obtained by multiplying the regular upper limit by 0.05 is marked as the initial threshold value, and the value obtained by multiplying the regular upper limit by 0.85 is marked as the second threshold value; Step 3: After obtaining the real-time monitoring video, segment the real-time monitoring video and analyze it to determine a number of initial threshold videos, second threshold videos, and over-threshold videos; The video splitting unit is used to split the real-time monitoring video, the initial threshold video, the second threshold video and the super threshold video to obtain a segmented video sequence and a plurality of segments of heterogeneous videos; the specific splitting processing method is as follows: SS1: Get the real-time surveillance video that is not classified into any initial threshold video, second threshold video, or over-threshold video, and mark it as the remaining video; SS2: Get the remaining videos, perform dynamic analysis, and obtain a hetero-directional video group consisting of several hetero-directional videos based on the generated core difference signals; SS3: Then, the initial threshold video, the second threshold video, and the super threshold video are processed in the same manner according to the principle of step SS2 to obtain the numbers of core difference signals generated by the initial threshold video, the second threshold video, and the super threshold video, respectively, which are marked as the corresponding initial threshold number, the second threshold number, and the super threshold number, respectively; SS4: Then, several segments of initial threshold videos, second threshold videos and over-threshold videos are uniformly marked as scattered videos; The specific method of dynamic analysis in step SS2 is: SS21: Get all the remaining videos. The remaining videos specifically exist in several groups where the real-time monitoring videos are divided by the initial threshold video, the second threshold video and the super threshold video. Compare the picture of the previous frame with the picture of the next frame. When the proportion of the difference between the two accounts for B1 proportion or more of the total area of ​​the next frame, generate an initial difference signal; B1 is a preset value; SS22: Then mark the previous frame as the starting frame, and then obtain the X2 frames after the picture, X2 is a preset value, and mark the selected X2 frames after the picture as the next frame. If the proportion of the difference between the next frame and the starting frame accounts for B1 proportion of the total area of ​​the next frame or more, a core difference signal is generated; SS23: Mark the corresponding difference part as an abnormal mark, obtain all videos from the appearance to the disappearance of the abnormal mark, mark them as different-direction videos, and all different-direction videos constitute a different-direction video group; The video splitting unit transmits the segmented video sequence and the heterogeneous video to the transmission analysis unit via the processor. The transmission analysis unit is used to perform a transmission operation on the segmented video sequence and the heterogeneous video in combination with the channel unit and transmits them to the object analysis unit. The specific transmission operation is as follows: Obtain the first thirty percent of the segmented video sequence, round the first thirty percent of the segmented video sequence and transmit it to the object analysis unit, and mark the corresponding segmented video as a backup video; then transmit the remaining segmented videos in the segmented video sequence to the object analysis unit; After the different-direction video is transmitted to the object analysis unit, the different-direction video is also backed up to obtain a backup video; The transmission analysis unit is used to transmit the backup video to the storage unit for backup storage; the object analysis unit is used to perform frame loss analysis on all the videos after receiving them, obtain the retransmission time period and transmit it to the processor, and the processor is used to retrieve the video corresponding to the retransmission time period from the storage unit and retransmit it to the object analysis unit via the transmission analysis unit and the channel unit.

2. A surveillance video transmission system based on video content analysis according to claim 1, characterized in that: It also includes a video acquisition unit, which is used to acquire real-time monitoring video and transmit the real-time monitoring video to the video analysis unit. The video analysis unit receives the real-time monitoring video transmitted by the video acquisition unit and performs preliminary phase analysis on the real-time monitoring video.

3. A surveillance video transmission system based on video content analysis according to claim 1, characterized in that: The specific method of inertia analysis in step 1 is: S1: Inertia extraction is performed on the controlled area. The controlled area is the corresponding monitored target area. The specific inertia extraction is to first obtain the flow of people in the controlled area within the T1 time, where T1 is the preset value; the flow of people in the controlled area every day. The specific method of obtaining the flow of people is as follows: Based on face recognition, if any face appears in the controlled area and is not detected again within T2 time, the flow rate value will be automatically increased by 1, where T2 is the preset value; S2: Get the personal flow value of T1 and mark it as a personal flow value group Ri, i=1, ..., T1; S3: Then automatically find the mean of Ri and mark it as P. Then use the formula to calculate the square difference W of Ri. The specific calculation formula is: S4: When W exceeds X1, select the corresponding Ri in descending order of |Ri-P|. After that, delete each Ri value when it is selected, and then recalculate the W value. If the W value still exceeds X1, continue to select the next corresponding Ri and delete it until the W value does not exceed X1. The maximum value of the deleted Ri at this time is marked as the regular upper limit. X1 is the preset value.

4. A surveillance video transmission system based on video content analysis according to claim 1, characterized in that: The specific method of frame loss analysis is as follows: The scattered videos and anomaly videos of the corresponding backup video are analyzed. When unreasonable time progress occurs, that is, the actual playback time of a one-second video is less than one second, the time period of the corresponding scattered videos and abnormal videos is marked as the retransmission time period.

5. The monitoring video transmission system based on video content analysis according to claim 1, characterized in that: The specific method of segmentation analysis in step 3 is as follows: S01: Starting from the set time, the number of faces in the real-time monitoring video will be automatically obtained. When the number of faces in the real-time monitoring video is not zero and is lower than the initial threshold, the video of the corresponding stage will be continuously obtained until the number of faces is greater than or equal to the initial threshold. Then, the video will be intercepted and all videos below the initial threshold will be marked as initial threshold videos. S02: obtaining a number of initial threshold videos; S03: Then, the real-time surveillance video with the number of faces exceeding the initial threshold value but not exceeding the second threshold value is intercepted and marked as the second threshold video; S04: obtaining a number of second-threshold videos; S05: then intercepting the real-time surveillance video with the number of faces exceeding the second threshold value, and marking it as a super video; S06: Obtain several segments of super-threshold videos.

6. A surveillance video transmission system based on video content analysis according to claim 1, characterized in that: The split process also includes the following steps: Define the doubling value based on the initial threshold video, the second threshold video, and the super threshold video. The doubling values ​​of all first-threshold videos are marked as 1.75, the doubling values ​​of all second-threshold videos are marked as 1, and the doubling value of super-threshold videos is defined as 1.65; Calculate the crowd value of all scattered videos, the crowd value is equal to the corresponding initial threshold, second threshold and super threshold multiplied by 1.75, 1 and 1.65 respectively; The crowd values ​​of all the scattered videos are obtained, and the scattered videos are sorted in descending order according to the crowd values ​​to obtain a scattered video sequence.

7. The monitoring video transmission system based on video content analysis according to claim 1, characterized in that: Also includes snap-ins: The management unit is connected to the processor for inputting all preset values.

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