CN117440018A - Network camera equipment fortune pipe platform based on thing networking - Google Patents
Network camera equipment fortune pipe platform based on thing networking Download PDFInfo
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
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Abstract
The invention belongs to the technical field of camera equipment management and control, in particular to a network camera equipment management platform based on the Internet of things, which comprises a server, an equipment management module, an intelligent analysis and identification module, an equipment quality precision evaluation module and a remote monitoring module; according to the invention, the remote management and monitoring of the equipment are realized through the technology of the Internet of things, the management efficiency and convenience of the network camera equipment are improved, the monitoring video quality and the running stability of the network camera equipment can be reasonably analyzed in real time, the running quality of the network camera equipment can be accurately estimated, the network camera equipment can be conveniently and efficiently operated and managed, the subsequent safe and stable running of the network camera equipment is ensured, the availability evaluation analysis of the network camera equipment is carried out when the running quality abnormal signal is generated, the targeted processing measures can be conveniently and rapidly made by a user, the running difficulty of the network camera equipment is reduced, and the intelligent degree is high.
Description
Technical Field
The invention relates to the technical field of control of camera equipment, in particular to a network camera equipment management platform based on the Internet of things.
Background
The network camera is a new generation camera generated by combining a traditional camera and a network technology, can transmit video and audio through the Internet or an internal local area network, and is widely applied to the fields of safety monitoring, intelligent home, traffic monitoring and the like along with the rapid development of the technology of the Internet of things;
however, at present, the quality and the running stability of the monitoring video of the network camera equipment cannot be reasonably analyzed in real time and the running quality of the network camera equipment cannot be accurately estimated, and the usability of the network camera equipment cannot be analyzed when the running quality of the network camera equipment is judged to be abnormal, so that the network camera equipment is difficult to operate and manage efficiently;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a network camera equipment management platform based on the Internet of things, which solves the problems that the prior art cannot reasonably analyze the monitoring video quality and the running stability of the network camera equipment in real time and accurately evaluate the running quality of the network camera equipment, the availability analysis cannot be performed on the network camera equipment when the running quality of the network camera equipment is judged to be abnormal, and the management difficulty of the network camera equipment is high.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a network camera equipment management platform based on the Internet of things comprises a server, an equipment management module, an intelligent analysis and identification module, an equipment quality precision evaluation module and a remote monitoring module; the equipment management module is communicated with the network camera equipment through the internet of things technology and is used for accessing, configuring, controlling and monitoring the state of the network camera equipment to realize the remote management of the network camera equipment; the remote monitoring module allows a user to access remotely through the Internet to check the state and video data of the network camera equipment;
the intelligent analysis and identification module is embedded into the network camera equipment, real-time analysis is carried out on video data of the network camera equipment by utilizing a deep learning technology, abnormal behaviors and events are automatically identified through training an optimized identification model, corresponding alarm information is generated, and the alarm information is sent to the remote monitoring module through the server; the equipment quality accurate evaluation module is used for analyzing the equipment quality of the network camera equipment, judging whether the operation quality of the network camera equipment is qualified or not through analysis, generating an operation quality abnormal signal or an operation quality normal signal according to the operation quality abnormal signal or the operation quality normal signal, and sending the operation quality abnormal signal or the operation quality normal signal to the remote monitoring module through the server.
Further, the specific operation process of the device quality precision evaluation module comprises the following steps:
the method comprises the steps of calling video quality abnormal data and shooting stability abnormal data from a server, respectively carrying out numerical comparison on the video quality abnormal data and the shooting stability abnormal data with a preset video quality abnormal data threshold value and a preset shooting stability abnormal data threshold value, and generating an operation quality abnormal signal of the network camera if the video quality abnormal data or the shooting stability abnormal data exceeds the corresponding preset threshold value;
if the video quality abnormal data or the camera shooting operation stability abnormal data do not exceed the corresponding preset threshold value, acquiring hardware fault frequency, software fault frequency and network connection fault frequency of the network camera shooting equipment in unit time, and summing the hardware fault frequency, the software fault frequency and the network connection fault frequency to obtain camera shooting fault rate data; marking hardware faults, software fault frequencies and network connection faults of the network camera equipment as camera faults, collecting duration of each camera fault, summing all duration to obtain a camera fault duration value, comparing the duration of the corresponding camera fault with a corresponding preset duration threshold value, and if the duration exceeds the preset duration threshold value, distributing a fault judgment symbol GP-1 to the corresponding camera fault; acquiring the number of the shooting faults corresponding to the network shooting equipment and the fault judgment symbol GP-1 in unit time, marking the number as the identification fault frequency, and carrying out ratio calculation on the identification fault frequency and the shooting fault rate data to obtain an identification fault coefficient;
performing numerical calculation on the identification fault coefficient, the image capturing fault duration value and the image capturing fault rate data of the network image capturing equipment to obtain an image capturing quality inspection value, performing numerical comparison on the image capturing quality inspection value and a preset image capturing quality inspection threshold value, and generating an operation quality abnormal signal of the network image capturing equipment if the image capturing quality inspection value exceeds the preset image capturing quality inspection threshold value; and if the image pickup quality inspection value does not exceed the preset image pickup quality inspection threshold value, generating an operation quality normal signal of the network image pickup equipment.
Further, the server is in communication connection with the video quality detection module and the camera shooting stability detection module, the video quality detection module monitors the quality of the monitoring video of the network camera equipment in real time, obtains video quality abnormal data of the network camera equipment through analysis, and sends the video quality abnormal data of the network camera equipment to the server for storage; the image-taking stability detection data are used for detecting the running stability of the network image-taking equipment, acquiring image-taking stability abnormal data of the network image-taking equipment through analysis, and sending the image-taking stability abnormal data of the network image-taking equipment to a server for storage.
Further, the specific operation process of the video quality detection module includes:
the method comprises the steps of carrying out real-time detection analysis on a monitoring video picture of the network camera equipment to judge whether the corresponding moment is in a low-quality monitoring state, obtaining a time length occupation ratio of the network camera equipment in the low-quality monitoring state in unit time, marking the time length occupation ratio as a monitoring low-quality coefficient, carrying out summation calculation on all video detection values of the network camera equipment in the unit time and taking an average value to obtain a video detection value, obtaining duration time of the network camera equipment in the low-quality monitoring state each time and marking the duration time as a single low-quality duration value, carrying out numerical comparison on the single low-quality duration value and a preset single low-quality duration time threshold value, and marking the number of the single low-quality duration values exceeding the preset single low-quality duration time threshold value as low-quality high duration frequency; and carrying out numerical calculation on the low-quality high-duration frequency, the video abnormal value and the monitoring low-quality coefficient to obtain video quality abnormal data.
Further, the judging and analyzing process of the low-quality monitoring state is as follows:
acquiring definition detection data, contrast detection data, color rendition detection data and picture shaking detection data of a monitoring video picture in real time, carrying out average value calculation on the maximum value and the minimum value of a preset standard contrast range to obtain a contrast judgment value, carrying out difference value calculation on the contrast detection data and the contrast judgment value and taking an absolute value to obtain a contrast evaluation value, and similarly obtaining a color rendition evaluation value; performing numerical calculation on the definition detection data, the contrast evaluation value, the color rendition evaluation value and the picture jitter detection data to obtain a video detection value; and comparing the video detection value with a preset video detection threshold value, and judging that the corresponding moment of the network camera equipment is in a low-quality monitoring state if the video detection value exceeds the preset video detection threshold value.
Further, the specific operation process of the camera operation stability detection module comprises the following steps:
setting a plurality of detection time periods in unit time, acquiring image transmission fluency data and data storage rate data of the network camera equipment corresponding to the detection time periods, respectively carrying out numerical comparison on the image transmission fluency data and the data storage rate data and a preset image transmission fluency data threshold value and a preset data storage rate data threshold value, marking the corresponding detection time periods as stable operation time periods if the image transmission fluency data and the data storage rate data exceed the corresponding preset threshold values, and judging the corresponding detection time periods as unstable operation time periods if the other conditions exist;
calculating the ratio of the number of unstable operation time periods to the number of stable operation time periods in unit time to obtain an image capture unstable detection value, collecting monitoring identification efficiency data and monitoring identification erroneous judgment rate data of the network image capture equipment aiming at abnormal behaviors and events in unit time, and calculating the numerical values of the monitoring identification efficiency data, the monitoring identification erroneous judgment rate data and the image capture unstable detection value to obtain image capture operation stability abnormal data.
Further, the server is in communication connection with the equipment availability evaluation module, the server sends the operation quality abnormal signal of the network camera equipment to the equipment availability evaluation module, the equipment availability evaluation module carries out availability evaluation analysis when receiving the operation quality abnormal signal, a high availability signal or a low availability signal of the network camera equipment is generated through analysis, and the high availability signal or the low availability signal of the network camera equipment is sent to the remote monitoring module.
Further, the specific analysis procedure of the usability assessment analysis is as follows:
the method comprises the steps of calling camera damage data of the network camera equipment from a server, collecting total monitoring duration of the network camera equipment in a historical operation process, collecting the times of network virus attack of the network camera equipment in the historical operation process, and marking the times as a network attack damage value; the production interval time length of the network camera equipment is collected, the average interval time length of the production date and the scrapping date of the scrapped corresponding type of network camera equipment is called from the server and marked as scrapped reference time length, and the ratio of the production interval time length to the scrapped reference time length is calculated to obtain a scrapped ratio analysis value;
performing numerical calculation on the rejection ratio analysis value, the monitoring total duration, the image capturing damage data and the network attack damage value of the network image capturing equipment to obtain an availability evaluation value, performing numerical comparison on the availability evaluation value and a preset availability evaluation threshold value, and generating a low availability signal of the network image capturing equipment if the availability evaluation value exceeds the preset availability evaluation threshold value; if the usability evaluation value exceeds a preset usability evaluation threshold, a high usability signal of the network camera device is generated.
Further, the server is in communication connection with a different-decision evaluation module, the different-decision evaluation module collects temperature data at a plurality of positions in the network camera equipment in real time and carries out mean value calculation on the temperature data, and the mean value calculation result is marked as a camera temperature detection value; collecting the jitter frequency and the jitter amplitude of the network camera equipment, performing product calculation on the jitter frequency and the jitter amplitude to obtain a camera-shake detection value, and performing weighting summation calculation on the camera-shake detection value and the camera-shake detection value to obtain a camera-shake live value;
comparing the image capturing live value with a preset image capturing live threshold value in a numerical mode, and judging that the network image capturing equipment is in a running damaged state if the image capturing live value exceeds the preset image capturing live threshold value; if the image capturing live value does not exceed the preset image capturing live threshold value, acquiring an environment temperature and humidity value and an environment pollution value of the environment of the network image capturing equipment, and carrying out weighting summation calculation on the environment temperature and humidity value and the environment pollution value to obtain an image capturing ring condition value; comparing the image pickup ring condition value with a preset image pickup ring condition threshold value, and judging that the network image pickup state is in an operation damaged state if the image pickup ring condition value exceeds a preset image pickup ring Kuang Yuzhi; the method comprises the steps of obtaining the total duration of the network camera equipment in a running damaged state in a historical running process, marking the total duration as camera damaged data, and sending the camera damaged data of the network camera equipment to a server for storage.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the remote management and monitoring of the equipment are realized through the technology of the Internet of things, the management efficiency and convenience of the network camera equipment are improved, the quality of the monitoring video of the network camera equipment is monitored and analyzed in real time through the video quality detection module, the operation stability of the network camera equipment is detected and analyzed through the camera operation stability detection data, the data support is provided for the analysis process of the equipment quality accurate assessment module, the equipment quality of the network camera equipment is analyzed through the equipment quality accurate assessment module, so that whether the operation quality of the network camera equipment is qualified or not is judged, the accurate assessment and feedback early warning of the operation quality of the network camera equipment are realized, the operation and management of the network camera equipment are conveniently and efficiently carried out, and the subsequent safe and stable operation of the network camera equipment is ensured;
2. according to the invention, the abnormal operation decision evaluation module is used for analyzing to judge the damage condition of the network camera equipment, so that corresponding regulation and control or reason investigation can be conveniently and timely carried out, data support can be provided for the analysis process of the equipment availability evaluation module, the network camera equipment is subjected to availability evaluation analysis through the equipment availability evaluation module when an abnormal operation quality signal is generated, and a high availability signal or a low availability signal of the network camera equipment is generated through analysis, so that a user can conveniently and quickly carry out targeted treatment measures, the transportation difficulty of the network camera equipment is reduced, and the intelligent degree is high.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a first system block diagram of a first embodiment of the present invention;
FIG. 2 is a second system block diagram according to a first embodiment of the invention;
fig. 3 is a system block diagram of the second and third embodiments of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one: as shown in fig. 1-2, the network camera equipment transportation platform based on the internet of things provided by the invention comprises a server, an equipment management module, an intelligent analysis and identification module, an equipment quality precision evaluation module and a remote monitoring module; the device management module communicates with the network camera device through the technology of the Internet of things (such as MQTT protocol, coAP protocol and the like), and is mainly responsible for accessing, configuring, controlling, monitoring the state and the like of the network camera device, so as to realize remote management of the network camera device; the remote monitoring module allows a user to access remotely through the Internet to check the state and video data of the network camera equipment;
the remote management and monitoring of the equipment are realized through the technology of the Internet of things, and the management efficiency and convenience of the network camera equipment are improved; the intelligent analysis and identification module is embedded into the network camera equipment, the video data of the network camera equipment is analyzed in real time by utilizing the deep learning technology, abnormal behaviors and events are automatically identified through training an optimized identification model, corresponding alarm information is generated and sent to the remote monitoring module through the server, an alarm can be sent out in time, a user is informed of the alarm, and the user can deal with abnormal conditions more quickly.
The equipment quality accurate assessment module analyzes the equipment quality of the network camera equipment, judges whether the operation quality of the network camera equipment is qualified or not through analysis, generates an operation quality abnormal signal or an operation quality normal signal according to the operation quality abnormal signal or the operation quality normal signal, and sends the operation quality abnormal signal or the operation quality normal signal to the remote monitoring module through the server, so that accurate assessment and feedback early warning of the operation quality of the network camera equipment are realized, a user can grasp the operation quality abnormal condition of the network camera equipment in detail and make corresponding optimization management measures in time, and the subsequent safe, efficient and stable operation of the network camera equipment is ensured; the specific operation process of the equipment quality accurate assessment module is as follows:
the method comprises the steps of calling video quality abnormal data and shooting stability abnormal data from a server, respectively carrying out numerical comparison on the video quality abnormal data and the shooting stability abnormal data with a preset video quality abnormal data threshold value and a preset shooting stability abnormal data threshold value, and if the video quality abnormal data or the shooting stability abnormal data exceeds a corresponding preset threshold value, indicating that the running quality of the network camera equipment is poor, and timely taking measures such as corresponding inspection and the like, generating an running quality abnormal signal of the network camera equipment;
if the video quality abnormal data or the camera shooting operation stability abnormal data do not exceed the corresponding preset threshold value, acquiring hardware fault frequency, software fault frequency and network connection fault frequency of the network camera shooting equipment in unit time, and summing the hardware fault frequency, the software fault frequency and the network connection fault frequency to obtain camera shooting fault rate data; marking hardware faults, software fault frequencies and network connection faults of the network camera equipment as camera faults, collecting duration time of each camera fault, and summing all duration time to obtain a camera fault duration value;
the duration of the corresponding camera shooting fault is compared with a corresponding preset duration threshold value in value, and if the duration exceeds the preset duration threshold value, a fault judgment symbol GP-1 is distributed to the corresponding camera shooting fault; acquiring the number of the shooting faults corresponding to the network shooting equipment and the fault judgment symbol GP-1 in unit time, marking the number as the identification fault frequency, and carrying out ratio calculation on the identification fault frequency and the shooting fault rate data to obtain an identification fault coefficient;
carrying out numerical calculation on an identification fault coefficient RG, an imaging fault duration value RQ and imaging fault rate data RK of the network imaging equipment through a formula RZ=kp1RG+kp2RQ+kp3 RK to obtain an imaging quality inspection value RZ, wherein kp1, kp2 and kp3 are preset proportionality coefficients, and kp1 > kp3 > kp2 > 0; and, the larger the value of the imaging quality inspection value RZ is, the worse the operation quality of the network imaging device is; comparing the image quality inspection value RZ with a preset image quality inspection threshold value, and generating an operation quality abnormal signal of the network image pickup equipment if the image quality inspection value RZ exceeds the preset image quality inspection threshold value, which indicates that the operation quality of the network image pickup equipment is poor; if the image quality inspection value RZ does not exceed the preset image quality inspection threshold value, indicating that the operation quality of the network image pickup device is good, generating an operation quality normal signal of the network image pickup device.
Further, the server is in communication connection with the video quality detection module and the camera stability detection module, the video quality detection module monitors the quality of the monitoring video of the network camera equipment in real time, obtains video quality abnormal data of the network camera equipment through analysis, and sends the video quality abnormal data of the network camera equipment to the server for storage, so that the monitoring video quality condition of the network camera equipment can be fed back in real time, the network camera equipment can be checked and maintained in time, and data support can be provided for the analysis process of the equipment quality accurate assessment module; the specific operation process of the video quality detection module is as follows:
the monitoring video picture of the network camera equipment is detected and analyzed in real time to judge whether the corresponding moment is in a low-quality monitoring state or not, specifically: the method comprises the steps of collecting definition detection data, contrast detection data, color reproducibility detection data and picture jitter detection data of a monitoring video picture in real time, wherein the picture jitter detection data are data values representing the picture jitter degree, and the larger the value of the picture jitter detection data is, the more unstable the video picture is and the worse the picture quality is; performing average value calculation on the maximum value and the minimum value of a preset standard contrast range to obtain a contrast judgment value, obtaining a color rendition judgment value in a similar way, performing difference value calculation on contrast detection data and the contrast judgment value, and taking an absolute value to obtain a contrast evaluation value, and obtaining a color rendition evaluation value in a similar way;
performing numerical calculation on the sharpness detection data FR, the contrast evaluation value FQ, the color reduction evaluation value FW and the picture shake detection data FK by a formula fy= (tg 2×fq+tg3×fw+tg4×fk)/(tg 1×fr+0.283) to obtain a video detection value FY; wherein, tg1, tg2, tg3 and tg4 are preset proportionality coefficients, and values of tg1, tg2, tg3 and tg4 are all larger than zero; and the larger the value of the video detection value FY is, the worse the monitoring video quality condition of the network camera equipment at the corresponding moment is indicated; comparing the video detection value FY with a preset video detection threshold value, and judging that the corresponding moment of the network camera equipment is in a low-quality monitoring state if the video detection value FY exceeds the preset video detection threshold value;
acquiring the ratio of the duration of the network camera equipment in the low-quality monitoring state in unit time, marking the ratio as a monitoring low-quality coefficient, carrying out summation calculation on all video detection values of the network camera equipment in the unit time, taking an average value to obtain a video detection value, acquiring the duration of the network camera equipment in the low-quality monitoring state each time, marking the duration as a single low-quality duration value, carrying out numerical comparison on the single low-quality duration value and a preset single low-quality duration threshold value, and marking the number of the single low-quality duration values exceeding the preset single low-quality duration threshold value as low-quality high-duration frequency;
the low-quality high-duration frequency YK is calculated by the formula YP=c1+c2+YF+c3 performing numerical calculation on the video abnormal value YF and the monitoring low-quality coefficient YG to obtain video quality abnormal data YP; wherein, c1, c2 and c3 are preset proportionality coefficients, and c3 is more than c1 and more than c2 is more than 0; and, the larger the value of the video quality anomaly data YP, the worse the video quality of the network camera device in unit time is, the larger the probability of the network camera device having anomaly is, and the more the corresponding inspection and maintenance operation is required to be performed on the network camera device in time.
The camera operation stability detection data are used for detecting the operation stability of the network camera equipment, the camera operation stability abnormal data of the network camera equipment are obtained through analysis, and the camera operation stability abnormal data of the network camera equipment are sent to a server for storage, so that the transmission and the stability identification conditions of the network camera equipment can be fed back in real time, the network camera equipment can be checked and maintained in time, and data support can be provided for the analysis process of the equipment quality precision evaluation module; the specific operation process of the camera shooting operation stability detection module is as follows:
setting a plurality of detection time periods in unit time, and collecting image transmission fluency data and data storage rate data of the network camera equipment corresponding to the detection time periods, wherein the larger the numerical values of the image transmission fluency data and the data storage rate data are, the better the running condition of the network camera equipment is indicated; respectively carrying out numerical comparison on the image transmission fluency data and the data storage rate data with a preset image transmission fluency data threshold and a preset data storage rate data threshold, if the image transmission fluency data and the data storage rate data both exceed the corresponding preset thresholds, marking the corresponding detection time period as a stable operation time period, and otherwise judging that the corresponding detection time period is an unstable operation time period;
calculating the ratio of the number of unstable operation time periods to the number of stable operation time periods in unit time to obtain an image capture unstable detection value, and collecting monitoring identification efficiency data and monitoring identification erroneous judgment rate data of the network image capture equipment aiming at abnormal behaviors and events in unit time, wherein the monitoring identification efficiency data is a data magnitude representing the identification rate, and the larger the numerical value of the monitoring identification efficiency data is, the faster the identification rate is and the better the identification performance is; the data of the monitoring and identifying misjudgment rate is a data value representing the percentage of the number of the identifying errors to the total number of the identifying errors, and the smaller the value of the monitoring and identifying misjudgment rate is, the better the identifying performance is;
by the formulaPerforming numerical calculation on the monitoring identification efficiency data GM, the monitoring identification erroneous judgment rate data GB and the camera unsteady detection value GW to obtain camera operational stability abnormal data YW; wherein, eq1, eq2, eq3 are preset proportionality coefficients, eq1, eq2, eq3 are positive numbers; and, the larger the value of the image-capturing operation stability abnormal data YW is, the worse the operation condition of the network image-capturing equipment in unit time is, the larger the probability of the abnormality of the network image-capturing equipment is, and the corresponding inspection and maintenance operation of the network image-capturing equipment is required in time.
Embodiment two: as shown in fig. 3, this embodiment differs from embodiment 1 in that a server is communicatively connected to an apparatus availability evaluation module, the server transmits an operation quality abnormality signal of the network image capturing apparatus to the apparatus availability evaluation module, the apparatus availability evaluation module performs an availability evaluation analysis on the operation quality abnormality signal when it is received, generates a high availability signal or a low availability signal of the network image capturing apparatus by the analysis, and transmits the high availability signal or the low availability signal of the network image capturing apparatus to a remote monitoring module, so that a user grasps the availability condition of the network image capturing apparatus in detail, and makes a targeted processing measure based thereon, such as disabling the network image capturing apparatus when the low availability signal is received, reducing management difficulty and ensuring a subsequent monitoring effect; the specific analytical procedure for the usability assessment analysis is as follows:
the method comprises the steps of calling camera damage data of the network camera equipment from a server, and collecting monitoring total duration of the network camera equipment in a historical operation process, wherein the monitoring total duration is a data value representing the total duration of the network camera equipment in a monitoring operation state; collecting the times of network virus attack of the network camera equipment in the history operation process and marking the times as a network attack damage value; the production interval time length of the network camera equipment is collected, the average interval time length of the production date and the scrapping date of the scrapped corresponding type of network camera equipment is called from the server and marked as scrapped reference time length, and the ratio of the production interval time length to the scrapped reference time length is calculated to obtain a scrapped ratio analysis value; wherein, the larger the value of the rejection ratio analysis value is, the more the network camera equipment tends to be rejected;
by the formulaCarrying out numerical calculation on a rejection ratio analysis value KR, a monitoring total duration KS, camera damage data KD and a network attack damage value KF of the network camera equipment to obtain an availability evaluation value KY, wherein wt1, wt2, wt3 and wt4 are preset proportionality coefficients, and wt1 is more than wt4 is more than wt2 is more than wt1 is more than 0; and, the larger the value of the usability evaluation value KY, the worse the usability of the network image pickup apparatus is, the more likely to be scrapped; comparing the usability evaluation value KY with a preset usability evaluation threshold value, and generating a low usability signal of the network camera device if the usability evaluation value KY exceeds the preset usability evaluation threshold value, which indicates that the usability of the network camera device is poor; if the usability evaluation value KY exceeds a preset usability evaluation threshold, indicating that the usability of the network camera device is good, a high usability signal of the network camera device is generated.
Embodiment III: as shown in fig. 3, the difference between the present embodiment and embodiments 1 and 2 is that the server is in communication connection with the abnormal decision evaluation module, the abnormal decision evaluation module collects temperature data at a plurality of positions in the network camera device in real time and performs mean value calculation, and marks the mean value calculation result as a camera temperature detection value; collecting the jitter frequency and the jitter amplitude of the network camera equipment, performing product calculation on the jitter frequency and the jitter amplitude to obtain a camera shake detection value, and performing weighting summation calculation on the camera shake detection value SW and the camera shake detection value SD through a formula SY=a1 x SW+a2 x SD to obtain a camera live value SY; wherein a1 and a2 are preset weight coefficients, and a1 is more than a2 and more than 0.3; and, the larger the value of the image capturing live value SY, the larger the damage to the network image capturing apparatus is shown;
comparing the image capturing live value SY with a preset image capturing live threshold value in a numerical mode, and judging that the network image capturing equipment is in a running damaged state if the image capturing live value SY exceeds the preset image capturing live threshold value; if the image capturing live value SY does not exceed the preset image capturing live threshold value, acquiring an environment temperature and humidity value and an environment pollution value of the environment of the network image capturing device, wherein the environment temperature and humidity value is a data magnitude representing both a deviation value of the environment temperature compared with a preset temperature standard value and a deviation value of the environment humidity compared with a preset humidity standard value and the value; the environmental pollution value is a data value representing the dust concentration in the environment where the network camera equipment is located;
weighting and summing the environmental temperature and humidity value SP and the environmental pollution value SR through a formula SH=a3×SP+a4×SR to obtain a camera ring condition value SH; wherein a3 and a4 are preset weight coefficients, and a4 is more than a3 and more than 0.5; and, the larger the value of the image capturing ring condition value SH, the larger the damage to the network image capturing apparatus is indicated; the camera ring condition value SH is compared with a preset camera ring condition threshold value in value, if the camera ring condition value SH exceeds the preset camera ring Kuang Yuzhi, the network camera state is judged to be in an operation damaged state, the damaged state of the network camera equipment can be accurately and real-timely fed back, so that a manager can timely perform corresponding regulation and control or conduct reason investigation, and the service life of the network camera equipment is prolonged; the method comprises the steps of obtaining the total time length of the network camera equipment in a running damaged state in a historical running process, marking the total time length as camera damage data, sending the camera damage data of the network camera equipment to a server for storage, and providing data support for an analysis process of the equipment availability evaluation module.
The working principle of the invention is as follows: when the intelligent analysis and identification module is used, the remote management and monitoring of the equipment are realized through the technology of the Internet of things, the management efficiency and convenience of the network camera equipment are improved, the intelligent analysis and identification module utilizes the deep learning technology to analyze the video data of the network camera equipment in real time, abnormal behaviors and events are automatically identified, and an alarm can be sent out and a user can be notified in time; the quality of the monitoring video of the network camera equipment is monitored and analyzed in real time through the video quality detection module, so that the quality condition of the monitoring video of the network camera equipment is fed back in real time, the operation stability of the network camera equipment is detected and analyzed by the camera operation stability detection data, the transmission and identification stability condition of the network camera equipment is fed back in real time, and a data support is provided for the analysis process of the equipment quality precision evaluation module; and analyzing the equipment quality of the network camera equipment through the equipment quality accurate assessment module, judging whether the operation quality of the network camera equipment is qualified through analysis, realizing accurate assessment and feedback early warning of the operation quality of the network camera equipment, conveniently and efficiently operating and managing the network camera equipment, and ensuring the subsequent safe and stable operation of the network camera equipment.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (9)
1. The network camera equipment management platform based on the Internet of things is characterized by comprising a server, an equipment management module, an intelligent analysis and identification module, an equipment quality precision evaluation module and a remote monitoring module; the equipment management module is communicated with the network camera equipment through the internet of things technology and is used for accessing, configuring, controlling and monitoring the state of the network camera equipment to realize the remote management of the network camera equipment; the remote monitoring module allows a user to access remotely through the Internet to check the state and video data of the network camera equipment;
the intelligent analysis and identification module is embedded into the network camera equipment, real-time analysis is carried out on video data of the network camera equipment by utilizing a deep learning technology, abnormal behaviors and events are automatically identified through training an optimized identification model, corresponding alarm information is generated, and the alarm information is sent to the remote monitoring module through the server; the equipment quality accurate evaluation module is used for analyzing the equipment quality of the network camera equipment, judging whether the operation quality of the network camera equipment is qualified or not through analysis, generating an operation quality abnormal signal or an operation quality normal signal according to the operation quality abnormal signal or the operation quality normal signal, and sending the operation quality abnormal signal or the operation quality normal signal to the remote monitoring module through the server.
2. The network camera equipment transportation platform based on the internet of things according to claim 1, wherein the specific operation process of the equipment quality accuracy assessment module comprises the following steps:
the video quality abnormal data and the shooting operation stability abnormal data are called from the server, and if the video quality abnormal data or the shooting operation stability abnormal data exceed the corresponding preset threshold, an operation quality abnormal signal of the network shooting equipment is generated; if the video quality abnormal data or the camera shooting operation stability abnormal data do not exceed the corresponding preset threshold value, acquiring hardware fault frequency, software fault frequency and network connection fault frequency of the network camera shooting equipment in unit time, and summing the hardware fault frequency, the software fault frequency and the network connection fault frequency to obtain camera shooting fault rate data;
marking hardware faults, software fault frequencies and network connection faults of the network camera equipment as camera faults, collecting duration of each camera fault, summing all duration to obtain a camera fault duration value, comparing the duration of the corresponding camera fault with a corresponding preset duration threshold value, and if the duration exceeds the preset duration threshold value, distributing a fault judgment symbol GP-1 to the corresponding camera fault; acquiring the number of the shooting faults corresponding to the network shooting equipment and the fault judgment symbol GP-1 in unit time, marking the number as the identification fault frequency, and carrying out ratio calculation on the identification fault frequency and the shooting fault rate data to obtain an identification fault coefficient;
performing numerical calculation on the identification fault coefficient, the imaging fault duration value and the imaging fault rate data of the network imaging equipment to obtain an imaging quality inspection value, and generating an operation quality abnormal signal of the network imaging equipment if the imaging quality inspection value exceeds a preset imaging quality inspection threshold; and if the image pickup quality inspection value does not exceed the preset image pickup quality inspection threshold value, generating an operation quality normal signal of the network image pickup equipment.
3. The network camera equipment management platform based on the internet of things according to claim 2, wherein the server is in communication connection with the video quality detection module and the camera operation stability detection module, the video quality detection module monitors the quality of the monitoring video of the network camera equipment in real time, obtains video quality abnormal data of the network camera equipment through analysis, and sends the video quality abnormal data of the network camera equipment to the server for storage; the image-taking stability detection data are used for detecting the running stability of the network image-taking equipment, acquiring image-taking stability abnormal data of the network image-taking equipment through analysis, and sending the image-taking stability abnormal data of the network image-taking equipment to a server for storage.
4. The network camera equipment management platform based on the internet of things according to claim 3, wherein the specific operation process of the video quality detection module comprises the following steps:
the method comprises the steps of carrying out real-time detection analysis on a monitoring video picture of the network camera equipment to judge whether the corresponding moment is in a low-quality monitoring state, obtaining a time length occupation ratio of the network camera equipment in the low-quality monitoring state in unit time and marking the time length occupation ratio as a monitoring low-quality coefficient, carrying out summation calculation on all video detection values of the network camera equipment in the unit time and taking an average value to obtain a video abnormal value, obtaining duration time of the network camera equipment in the low-quality monitoring state each time and marking the duration time as a single low-quality duration value, and marking the number of the single low-quality duration values exceeding a preset single low-quality duration time threshold as low-quality high duration frequency; and carrying out numerical calculation on the low-quality high-duration frequency, the video abnormal value and the monitoring low-quality coefficient to obtain video quality abnormal data.
5. The internet of things-based network camera equipment transportation platform according to claim 4, wherein the low-quality monitoring state judging and analyzing process comprises the following steps:
acquiring definition detection data, contrast detection data, color rendition detection data and picture shaking detection data of a monitoring video picture in real time, carrying out average value calculation on the maximum value and the minimum value of a preset standard contrast range to obtain a contrast judgment value, carrying out difference value calculation on the contrast detection data and the contrast judgment value and taking an absolute value to obtain a contrast evaluation value, and similarly obtaining a color rendition evaluation value; performing numerical calculation on the definition detection data, the contrast evaluation value, the color rendition evaluation value and the picture jitter detection data to obtain a video detection value; if the video detection value exceeds the preset video detection threshold value, judging that the corresponding moment of the network camera equipment is in a low-quality monitoring state.
6. The network camera equipment transportation platform based on the internet of things according to claim 3, wherein the specific operation process of the camera operation stability detection module comprises the following steps:
setting a plurality of detection time periods in unit time, acquiring image transmission fluency data and data storage rate data of the network camera equipment in the corresponding detection time periods, marking the corresponding detection time periods as stable operation time periods if the image transmission fluency data and the data storage rate data exceed corresponding preset thresholds, and judging the corresponding detection time periods as unstable operation time periods in other cases;
calculating the ratio of the number of unstable operation time periods to the number of stable operation time periods in unit time to obtain an image capture unstable detection value, collecting monitoring identification efficiency data and monitoring identification erroneous judgment rate data of the network image capture equipment aiming at abnormal behaviors and events in unit time, and calculating the numerical values of the monitoring identification efficiency data, the monitoring identification erroneous judgment rate data and the image capture unstable detection value to obtain image capture operation stability abnormal data.
7. The internet of things-based network camera equipment management platform according to claim 1, wherein the server is in communication connection with the equipment availability evaluation module, the server sends an operation quality abnormality signal of the network camera equipment to the equipment availability evaluation module, the equipment availability evaluation module performs availability evaluation analysis when receiving the operation quality abnormality signal, generates a high availability signal or a low availability signal of the network camera equipment through analysis, and sends the high availability signal or the low availability signal of the network camera equipment to the remote monitoring module.
8. The internet of things-based network camera equipment transportation platform according to claim 7, wherein the specific analysis process of the usability evaluation analysis is as follows:
the method comprises the steps of calling camera damage data of the network camera equipment from a server, collecting total monitoring duration of the network camera equipment in a historical operation process, collecting the times of network virus attack of the network camera equipment in the historical operation process, and marking the times as a network attack damage value; the production interval time length of the network camera equipment is collected, the average interval time length of the production date and the scrapping date of the scrapped corresponding type of network camera equipment is called from the server and marked as scrapped reference time length, and the ratio of the production interval time length to the scrapped reference time length is calculated to obtain a scrapped ratio analysis value;
performing numerical calculation on the rejection ratio analysis value, the monitoring total duration, the image capturing damage data and the network attack damage value of the network image capturing equipment to obtain an availability evaluation value, and if the availability evaluation value exceeds a preset availability evaluation threshold value, generating a low availability signal of the network image capturing equipment; if the usability evaluation value exceeds a preset usability evaluation threshold, a high usability signal of the network camera device is generated.
9. The internet of things-based network camera equipment management platform according to claim 8, wherein the server is in communication connection with a different-decision evaluation module, the different-decision evaluation module collects temperature data at a plurality of positions in the network camera equipment in real time and carries out mean value calculation on the temperature data, and the mean value calculation result is marked as a camera temperature detection value; collecting the jitter frequency and the jitter amplitude of the network camera equipment, performing product calculation on the jitter frequency and the jitter amplitude to obtain a camera-shake detection value, and performing weighting summation calculation on the camera-shake detection value and the camera-shake detection value to obtain a camera-shake live value; if the image capturing live value exceeds a preset image capturing live threshold value, judging that the network image capturing equipment is in a running damaged state;
if the image capturing live value does not exceed the preset image capturing live threshold value, acquiring an environment temperature and humidity value and an environment pollution value of the environment of the network image capturing equipment, and carrying out weighting summation calculation on the environment temperature and humidity value and the environment pollution value to obtain an image capturing ring condition value; if the condition value of the camera ring exceeds the preset camera ring Kuang Yuzhi, judging that the network camera state is in a running damaged state; the method comprises the steps of obtaining the total duration of the network camera equipment in a running damaged state in a historical running process, marking the total duration as camera damaged data, and sending the camera damaged data of the network camera equipment to a server for storage.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311556478.8A CN117440018A (en) | 2023-11-21 | 2023-11-21 | Network camera equipment fortune pipe platform based on thing networking |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117669898A (en) * | 2024-02-01 | 2024-03-08 | 深圳市创泽视科技有限公司 | Intelligent security monitoring management system based on Internet of things |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117669898A (en) * | 2024-02-01 | 2024-03-08 | 深圳市创泽视科技有限公司 | Intelligent security monitoring management system based on Internet of things |
| CN117669898B (en) * | 2024-02-01 | 2024-06-04 | 深圳市创泽视科技有限公司 | Intelligent security monitoring management system based on Internet of things |
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Application publication date: 20240123 |