CN116312015A - Traffic infrastructure real-time monitoring and early warning system based on big dipper data cloud platform - Google Patents
Traffic infrastructure real-time monitoring and early warning system based on big dipper data cloud platform Download PDFInfo
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- CN116312015A CN116312015A CN202310298797.7A CN202310298797A CN116312015A CN 116312015 A CN116312015 A CN 116312015A CN 202310298797 A CN202310298797 A CN 202310298797A CN 116312015 A CN116312015 A CN 116312015A
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Abstract
The invention relates to the field of traffic facility supervision and early warning, in particular to a traffic infrastructure real-time monitoring and early warning system based on a big dipper data cloud platform, which comprises a big dipper data cloud platform, wherein a rear end server and a front end server are arranged in the big dipper data cloud platform, an early warning unit is arranged in the rear end server, and a state evaluation unit, a fault analysis unit, a self-checking feedback unit and a recommendation analysis unit are arranged in the front end server; the invention collects the state data of the traffic signal lamp and analyzes and processes the state data to judge whether the running state of the traffic signal lamp is normal, so as to improve the supervision performance of the traffic signal lamp, ensure the normal operation of the traffic signal lamp, analyze the traffic signal lamp in a deep and feedback mode, and accurately judge the early warning of the traffic signal lamp by two dimensions of the total area value occupied and the number of the dangerous electrical elements in real time so as to ensure the integrity and the effectiveness of the early warning of the traffic signal lamp.
Description
Technical Field
The invention relates to the field of traffic facility supervision and early warning, in particular to a traffic infrastructure real-time monitoring and early warning system based on a big dipper data cloud platform.
Background
The infrastructure comprises municipal public engineering facilities, public living service facilities and the like such as traffic, post and telecommunications, water supply and power supply, business service, scientific research and technical service, landscaping, environmental protection, sanitary business and the like, and the perfect infrastructure plays a great promotion role in accelerating social and economic activities and promoting the evolution of spatial distribution forms;
however, in the prior art, the combination efficiency of the traffic signal lamp and the big dipper big data cloud platform is low, the traffic signal lamp cannot be monitored in real time, maintenance personnel cannot be reasonably arranged according to different fault grades, and when the running state of the traffic signal lamp is abnormal, the integrity and the effectiveness of early warning information cannot be ensured, so that the subsequent maintenance management is directly influenced, and the early warning efficiency of the traffic signal lamp is greatly reduced;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a traffic infrastructure real-time monitoring and early warning system based on a big dipper data cloud platform, which solves the technical defect that the combination degree of the traffic signal lamp and the big dipper data cloud platform is low and failure grade cannot be given.
The aim of the invention can be achieved by the following technical scheme:
the traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform comprises a big dipper data cloud platform, wherein a rear end server and a front end server are arranged in the big dipper data cloud platform, an early warning unit is arranged in the rear end server, and a state evaluation unit, a fault analysis unit, a self-checking feedback unit and a recommendation analysis unit are arranged in the front end server;
after the back-end server generates an early warning instruction, the early warning instruction is immediately sent to a state evaluation unit in the front-end server, after the early warning instruction is received, the state evaluation unit immediately collects state data of the traffic signal lamp, the state data comprises average power consumption of the traffic signal lamp and panel frame images of each second, the state data are analyzed and compared, an obtained risk signal is sent to an early warning unit in the back-end server, and meanwhile the risk signal is sent to a fault analysis unit and a self-checking feedback unit;
the fault analysis unit immediately retrieves the state data from the state evaluation unit after receiving the risk signal, analyzes the state data in a fault risk level, and sends the obtained primary fault signal, secondary fault signal and tertiary fault signal to the recommendation analysis unit;
the self-checking feedback unit immediately acquires early warning data of the early warning unit after receiving the risk signal, wherein the early warning data comprises an early warning character feature image corresponding to the risk signal and the running temperature of each electronic element in display equipment corresponding to the early warning character, analyzes the early warning data, sends the obtained early warning signal to the early warning unit through the front-end server, and after receiving the risk signal and the early warning signal, the early warning unit obtains a playing signal to perform early warning in a mode of playing voice equipment risk abnormality.
Preferably, the state data analysis process of the state evaluation unit is as follows:
the method comprises the steps of collecting the duration from the starting time of using the traffic signal lamp to the current time, marking the duration as a time threshold, dividing the time threshold into i sub-time nodes, wherein i is a natural number larger than zero, obtaining the average power consumption of the traffic signal lamp in each sub-time node, comparing the average power consumption with a preset average power consumption threshold, analyzing the average power consumption to obtain the number of the power consumption corresponding to the average power consumption larger than the preset average power consumption threshold, marking the number as an abnormal constant, simultaneously obtaining the number of the average power consumption corresponding to the average power consumption smaller than or equal to the preset average power consumption threshold, marking the number as a positive constant, obtaining the ratio of the abnormal constant to the positive constant, and marking the ratio of the abnormal constant to the positive constant as a risk ratio;
and acquiring each second panel frame image of the traffic signal lamp in the time threshold, marking the second panel frame image as Mo, acquiring a second occupied area corresponding to each second panel frame image of the traffic signal lamp in the time threshold, marking the second occupied area as Do, further acquiring a light area of the second occupied area corresponding to each second panel frame image in the time threshold, marking the light area as a second light area Lo, wherein o refers to each second, o is a natural number larger than zero, comparing and analyzing the second light area Lo with a preset second light area threshold recorded and stored in the second light area Lo, if the second light area Lo is not equal to the preset second light area threshold, constructing a set A of differences between the preset second light area threshold and the second light area Lo, and acquiring a maximum subset and a minimum subset in the set A, and marking differences between the maximum subset and the minimum subset as second missing maximum span values.
Preferably, the comparison and analysis process of the state evaluation unit is as follows:
comparing the second missing maximum span value with the risk ratio, and comparing the second missing maximum span value with a preset second missing maximum span value threshold value and a preset risk ratio threshold value which are recorded and stored in the second missing maximum span value and the risk ratio threshold value:
if the second missing maximum span value is smaller than a preset second missing maximum span value threshold and the risk ratio is smaller than a preset risk ratio threshold, no signal is generated;
and if the second missing maximum span value is greater than or equal to a preset second missing maximum span value threshold or the risk ratio is greater than or equal to a preset risk ratio threshold, generating a risk signal.
Preferably, the fault risk level analysis process of the fault analysis unit is as follows:
the first step: acquiring average power consumption in a time threshold value which is larger than average power consumption corresponding to a preset average power consumption threshold value, and marking the average power consumption as excessive average power consumption PYg, wherein g refers to the number of the average power consumption which is larger than the preset average power consumption threshold value, marking the part of the excessive average power consumption PYg which exceeds the preset average power consumption threshold value as abnormal power consumption, further acquiring the sum of the abnormal power consumption, and marking the sum as the total abnormal power consumption YZ;
and a second step of: acquiring a set A of differences between a preset second light area threshold value and a second light area Lo in a time threshold value, acquiring the sum of all subsets in the set A, and marking the sum as an abnormal area value YM;
and a third step of: and pass through
Obtaining a fault grade coefficient, wherein a1 and a2 are preset proportion coefficients of the total abnormal electricity consumption and the abnormal area value respectively, a3 is a preset correction proportion coefficient, a1, a2 and a3 are positive numbers larger than zero, G is a fault grade coefficient, and the fault grade is obtainedThe coefficient G is compared with a preset fault level coefficient interval recorded and stored in the coefficient G and analyzed:
if the fault grade coefficient G is larger than the maximum value in the preset fault grade coefficient interval, generating a first-level fault signal;
if the fault level coefficient G is located in the preset fault level coefficient interval, generating a secondary fault signal;
and if the fault level coefficient G is smaller than the minimum value in the preset fault level coefficient interval, generating a three-level fault signal.
Preferably, the recommendation analysis unit immediately acquires the geographic coordinates of the current traffic signal lamp from the big data cloud platform after receiving the first-level fault signal, the second-level fault signal and the third-level fault signal, and draws a circle with a preset radius, acquires a preset maintenance personnel list in the preset radius drawing circle, acquires the practitioner data of the preset maintenance personnel list, wherein the practitioner data comprises the practitioner duration and the distance from the current traffic signal lamp, and the recommended sorting process is as follows: the time length of the operation is preferential, and then the distance from the current traffic signal lamp is the distance, and the recommendation analysis unit sends the obtained recommendation preset maintenance personnel list to the early warning unit through the front-end server.
Preferably, the early warning data analysis process of the self-checking feedback unit is as follows:
the method comprises the steps of collecting the time length from the moment when a risk signal starts to be received to the moment when the early warning text is displayed, marking the time length as reaction time length, dividing the reaction time length into m sub-time nodes, wherein m is a natural number larger than zero, acquiring early warning text feature images corresponding to the risk signal in each sub-time node, and acquiring the real-time occupied total area value of the early warning text from the early warning text feature images corresponding to the risk signal;
meanwhile, the operation temperature of each electronic element in the display equipment corresponding to the early warning text in the reaction time is obtained, the electronic element corresponding to the operation temperature greater than the preset operation temperature threshold is obtained and marked as an abnormal electronic element, the temperature value of each abnormal electronic element in each sub-time node is obtained, the temperature value is compared with the preset temperature value threshold to analyze, the number of sub-time nodes corresponding to the temperature value greater than the preset temperature value threshold is obtained, the abnormal value is marked as an abnormal value, the number of abnormal electronic elements corresponding to the abnormal value greater than or equal to the preset abnormal value threshold is obtained, the abnormal electronic element number is marked as the risk electrical element number, and the real-time occupied total area value and the risk electrical element number are compared with the preset real-time occupied total area value threshold and the preset risk electrical element number stored in the real-time electronic element number and the preset risk electrical element number are recorded and analyzed:
if the real-time occupied total area value is equal to the preset real-time occupied total area value threshold value and the number of the risk electrical elements is smaller than the preset risk electrical element number threshold value, no signal is generated;
and if the real-time occupied total area value is not equal to the preset real-time occupied total area value threshold value or the number of the risk electrical elements is greater than or equal to the preset risk electrical element number threshold value, generating an early warning signal.
The beneficial effects of the invention are as follows:
the invention collects and analyzes the state data of the traffic signal lamp, judges whether the running state of the traffic signal lamp is normal, so as to improve the supervision performance of the traffic signal lamp, ensures the normal operation of the traffic signal lamp, analyzes the traffic signal lamp in a deep-type and feedback mode, accurately judges the early warning of the traffic signal lamp through two dimensions of the total area value occupied and the number of dangerous electrical elements in real time, ensures the integrity and the effectiveness of the early warning of the traffic signal lamp, improves the supervision and early warning performance of equipment, judges the fault level of the traffic signal lamp by deeply analyzing the influence condition of the state data on the traffic signal lamp, reasonably arranges maintenance personnel according to different fault levels, combines the geographic coordinates of the traffic signal lamp provided by a big Beidou data cloud platform, reasonably schedules the maintenance personnel, ensures the rapid maintenance of the traffic signal lamp, and further improves the supervision and early warning performance of the traffic signal lamp.
Drawings
The invention is further described below with reference to the accompanying drawings;
FIG. 1 is a flow diagram of the system of the present invention;
FIG. 2 is a flow diagram of the system in the backend server of the present invention;
FIG. 3 is a flow diagram of the system in the front-end server of the present invention;
FIG. 4 is a flow diagram of the analysis steps 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.
Example 1:
referring to fig. 1-4, the invention discloses a traffic infrastructure real-time monitoring and early warning system based on a big dipper data cloud platform, which comprises a big dipper data cloud platform, wherein a rear end server and a front end server are arranged in the big dipper data cloud platform, the rear end server and the front end server are in bidirectional communication connection, an early warning unit is arranged in the rear end server, a state evaluation unit, a fault analysis unit, a self-checking feedback unit and a recommendation analysis unit are arranged in the front end server, the state evaluation unit is in bidirectional communication connection with the fault analysis unit and the self-checking feedback unit, and the fault analysis unit is in unidirectional communication connection with the recommendation analysis unit;
after the rear end server generates the early warning instruction, the early warning instruction is immediately sent to a state evaluation unit in the front end server, the state evaluation unit immediately collects state data of the traffic signal lamp after receiving the early warning instruction, the state data comprises average electricity consumption of the traffic signal lamp and panel frame images of each second, the state data is analyzed, whether the running state of the traffic signal lamp is normal or not is judged, so that the supervision performance of the traffic signal lamp is improved, meanwhile, the normal work of the traffic signal lamp is guaranteed, and the specific state data analysis process is as follows:
the method comprises the steps of collecting the duration from the beginning of using a traffic signal lamp to the current moment, marking the duration as a time threshold, dividing the time threshold into i sub-time nodes, wherein i is a natural number larger than zero, obtaining the average power consumption of the traffic signal lamp in each sub-time node, comparing the average power consumption with a preset average power consumption threshold, analyzing the average power consumption to obtain the number of the power consumption corresponding to the average power consumption larger than the preset average power consumption threshold, marking the number as an abnormal constant, simultaneously obtaining the number of the average power consumption smaller than or equal to the preset average power consumption threshold, marking the number as a positive constant, obtaining the ratio of the abnormal constant to the positive constant, marking the ratio of the abnormal constant to the positive constant as a risk ratio, and the larger the value of the risk ratio is, the greater the safe operation risk of the traffic signal lamp is required to be explained;
acquiring panel frame images of the number of seconds of the traffic signal lamp in the time threshold, marking the panel frame images as Mo, acquiring a region occupied by the panel frame images of the number of seconds of the traffic signal lamp in the time threshold, marking the region occupied by the panel frame images of the number of seconds of the traffic signal lamp in the time threshold as Do, further acquiring a bright area of the region occupied by the panel frame images of the number of seconds in the time threshold, marking the bright area as a bright area Lo of the number of seconds, wherein o refers to the number of seconds, o is a natural number larger than zero, comparing the bright area Lo of the number of seconds with a preset bright area threshold stored in the bright area Lo of the number of seconds, if the bright area Lo of the number of seconds is not equal to the preset bright area threshold of the number of seconds, constructing a set A of differences between the preset bright area threshold of the number of seconds and the bright area Lo of the number of seconds, acquiring a maximum subset and a minimum subset of the bright area between the maximum subset and the minimum subset of the bright area of the number of seconds as a second span value, and comparing the maximum span value of the number of seconds with the preset risk ratio of the maximum span of the number of seconds with the preset risk ratio of the preset risk ratio stored in the preset area of the second span of the maximum span of the seconds with the preset ratio of the preset value of the preset risk ratio of the second:
if the second missing maximum span value is smaller than a preset second missing maximum span value threshold and the risk ratio is smaller than a preset risk ratio threshold, no signal is generated;
if the maximum span value of the second number missing is larger than or equal to a preset maximum span value threshold of the second number missing or the risk ratio is larger than or equal to a preset risk ratio threshold, generating a risk signal, sending the risk signal to an early warning unit in the rear end server, simultaneously sending the risk signal to a fault analysis unit and a self-checking feedback unit, immediately displaying the risk signal in a mode of the word equipment risk abnormality after the early warning unit receives the risk signal, and further reminding a traffic light operator to timely overhaul traffic light equipment so as to ensure normal operation of the traffic light and avoid unnecessary potential safety hazards caused by abnormal traffic light;
the fault analysis unit immediately retrieves the state data from the state evaluation unit after receiving the risk signal, and judges the fault grade of the traffic signal according to the influence condition of the state data on the traffic signal, and further reasonably arranges maintenance personnel according to different fault grades, so as to ensure that the traffic signal can be quickly maintained and reasonably scheduled for the maintenance personnel, and the specific fault risk grade analysis process is as follows:
acquiring average electricity consumption in a time threshold value which is larger than average electricity consumption corresponding to a preset average electricity consumption threshold value, and marking the average electricity consumption as overlarge average electricity consumption PYg, wherein g refers to the number of the average electricity consumption which is larger than the preset average electricity consumption threshold value, and marking the part of the overlarge average electricity consumption PYg which exceeds the preset average electricity consumption threshold value as abnormal electricity consumption, so as to acquire the sum of the abnormal electricity consumption, and marking the sum as abnormal electricity consumption total amount, wherein the label is YZ, and the larger the numerical value of the abnormal electricity consumption total amount YZ is, the more the problems caused by abnormal faults of the traffic signal lamp are, and the greater the maintenance difficulty is;
acquiring a set A of differences between a preset second light area threshold value and a second light area Lo in a time threshold value, acquiring the sum of all subsets in the set A, and marking the sum as an abnormal area value, wherein the mark is YM;
and pass through
Obtaining a failure grade coefficient, wherein a1 and a2 are respectively differentThe method comprises the steps that a3 is a preset correction proportional coefficient, a1, a2 and a3 are positive numbers larger than zero, G is a fault grade coefficient, and the fault grade coefficient G is compared with a preset fault grade coefficient interval recorded and stored in the fault grade coefficient G:
if the fault grade coefficient G is larger than the maximum value in the preset fault grade coefficient interval, generating a first-level fault signal;
if the fault level coefficient G is located in the preset fault level coefficient interval, generating a secondary fault signal;
if the fault level coefficient G is smaller than the minimum value in the preset fault level coefficient interval, generating a third-level fault signal, wherein the fault difficulty corresponding to the first-level fault signal, the second-level fault signal and the third-level fault signal is sequentially reduced, the first-level fault signal, the second-level fault signal and the third-level fault signal are sent to a recommendation analysis unit, the recommendation analysis unit immediately acquires the geographic coordinates of the current traffic signal lamp from the big dipper data cloud platform after receiving the first-level fault signal, the second-level fault signal and the third-level fault signal, and draws a circle with a preset radius, acquires a preset maintenance personnel list in the preset radius drawing circle, acquires the service data of the preset maintenance personnel list, wherein the service data comprises the service duration and the distance from the current traffic signal lamp, and the recommended sequencing process is as follows: the time length of the operation is preferential, the distance from the current traffic signal lamp is the distance from the traffic signal lamp, the recommendation analysis unit sends the obtained recommendation preset maintenance personnel list to the early warning unit through the front end server, and the early warning unit immediately displays the recommendation preset maintenance personnel list in a text mode after receiving the recommendation preset maintenance personnel list, so that the traffic signal lamp is reminded of reasonably arranging the recommendation preset maintenance personnel in time, and the traffic signal lamp is maintained quickly and effectively.
Example 2:
when the existing traffic signal lamp gives an early warning, the conditions of untimely early warning and incomplete early warning information often appear, so that the maintenance efficiency of the traffic signal lamp is directly affected;
the self-checking feedback unit immediately acquires early warning data of the early warning unit after receiving the risk signal, wherein the early warning data comprises early warning character feature images corresponding to the risk signal and running temperatures of all electronic elements in display equipment corresponding to early warning characters, the early warning data are analyzed, whether equipment early warning is normal or not is judged, so that effectiveness of equipment early warning is guaranteed, and a specific early warning data analysis process is as follows:
acquiring a time length from the moment when the early warning unit starts to receive the risk signal to the moment when the early warning text is displayed, marking the time length as a reaction time length, dividing the reaction time length into m sub-time nodes, wherein m is a natural number larger than zero, acquiring early warning text feature images corresponding to the risk signal in each sub-time node, acquiring a real-time total area value of the early warning text from the early warning text feature images corresponding to the risk signal, simultaneously acquiring an operating temperature of each electronic element in display equipment corresponding to the early warning text in the reaction time length, acquiring the electronic element with the operating temperature larger than a preset operating temperature threshold value, marking the electronic element as an abnormal electronic element, acquiring a temperature value of each abnormal electronic element in each sub-time node, comparing the temperature value with the preset temperature value threshold value, acquiring the number of sub-time nodes with the temperature value larger than the preset temperature value threshold value, marking the abnormal electronic element with the abnormal constant larger than or equal to the preset abnormal constant, marking the abnormal electronic element with the abnormal constant, marking the running temperature in the display equipment corresponding to the real-time total area value is larger than the preset electric element, recording the total area value of the abnormal element with the abnormal element and the preset total area value and the total area of the abnormal element is compared with the preset electric element and the preset value and the total area value is stored with the real-time risk value:
if the real-time occupied total area value is equal to the preset real-time occupied total area value threshold value and the number of the risk electrical elements is smaller than the preset risk electrical element number threshold value, no signal is generated;
if the real-time occupied total area value is not equal to the preset real-time occupied total area value threshold, or the number of the risk electrical elements is greater than or equal to the preset risk electrical element number threshold, generating an early warning signal, sending the early warning signal to an early warning unit through a front-end server, and after receiving the risk signal and the early warning signal, obtaining a playing signal by the early warning unit, and carrying out early warning in a mode of playing voice 'equipment risk abnormality', thereby improving timeliness and integrity of early warning, and improving supervision and early warning performance of equipment;
in summary, the invention collects and analyzes the state data of the traffic signal lamp, judges whether the running state of the traffic signal lamp is normal, so as to improve the supervision performance of the traffic signal lamp, simultaneously ensures the normal operation of the traffic signal lamp, analyzes the traffic signal lamp in a deep-type and feedback mode, accurately judges the early warning of the traffic signal lamp through two dimensions of the total area value occupied and the number of dangerous electrical elements in real time, so as to ensure the integrity and the effectiveness of the early warning of the traffic signal lamp, improve the supervision and early warning performance of equipment, judge the fault level of the traffic signal lamp by further analyzing the influence condition of the state data on the traffic signal lamp in a deep-type, reasonably arrange the maintainer according to different fault levels, reasonably schedule the maintainer according to the geographic coordinates of the traffic signal lamp provided by the big Beidou data cloud platform, and reasonably schedule the maintainer so as to ensure the rapid maintenance of the traffic signal lamp, and further improve the supervision and early warning performance of the traffic signal lamp.
The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (6)
1. The traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform is characterized by comprising the big dipper data cloud platform, wherein a rear-end server and a front-end server are arranged in the big dipper data cloud platform, an early warning unit is arranged in the rear-end server, and a state evaluation unit, a fault analysis unit, a self-checking feedback unit and a recommendation analysis unit are arranged in the front-end server;
after the back-end server generates an early warning instruction, the early warning instruction is immediately sent to a state evaluation unit in the front-end server, after the early warning instruction is received, the state evaluation unit immediately collects state data of the traffic signal lamp, the state data comprises average power consumption of the traffic signal lamp and panel frame images of each second, the state data are analyzed and compared, an obtained risk signal is sent to an early warning unit in the back-end server, and meanwhile the risk signal is sent to a fault analysis unit and a self-checking feedback unit;
the fault analysis unit immediately retrieves the state data from the state evaluation unit after receiving the risk signal, analyzes the state data in a fault risk level, and sends the obtained primary fault signal, secondary fault signal and tertiary fault signal to the recommendation analysis unit;
the self-checking feedback unit immediately acquires early warning data of the early warning unit after receiving the risk signal, wherein the early warning data comprises an early warning character feature image corresponding to the risk signal and the running temperature of each electronic element in display equipment corresponding to the early warning character, analyzes the early warning data, sends the obtained early warning signal to the early warning unit through the front-end server, and after receiving the risk signal and the early warning signal, the early warning unit obtains a playing signal to perform early warning in a mode of playing voice equipment risk abnormality.
2. The traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform of claim 1, wherein the state data analysis process of the state evaluation unit is as follows:
the method comprises the steps of collecting the duration from the starting time of using the traffic signal lamp to the current time, marking the duration as a time threshold, dividing the time threshold into i sub-time nodes, wherein i is a natural number larger than zero, obtaining the average power consumption of the traffic signal lamp in each sub-time node, comparing the average power consumption with a preset average power consumption threshold, analyzing the average power consumption to obtain the number of the power consumption corresponding to the average power consumption larger than the preset average power consumption threshold, marking the number as an abnormal constant, simultaneously obtaining the number of the average power consumption corresponding to the average power consumption smaller than or equal to the preset average power consumption threshold, marking the number as a positive constant, obtaining the ratio of the abnormal constant to the positive constant, and marking the ratio of the abnormal constant to the positive constant as a risk ratio;
and acquiring each second panel frame image of the traffic signal lamp in the time threshold, marking the second panel frame image as Mo, acquiring a second occupied area corresponding to each second panel frame image of the traffic signal lamp in the time threshold, marking the second occupied area as Do, further acquiring a light area of the second occupied area corresponding to each second panel frame image in the time threshold, marking the light area as a second light area Lo, wherein o refers to each second, o is a natural number larger than zero, comparing and analyzing the second light area Lo with a preset second light area threshold recorded and stored in the second light area Lo, if the second light area Lo is not equal to the preset second light area threshold, constructing a set A of differences between the preset second light area threshold and the second light area Lo, and acquiring a maximum subset and a minimum subset in the set A, and marking differences between the maximum subset and the minimum subset as second missing maximum span values.
3. The traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform of claim 2, wherein the comparison and analysis process of the state evaluation unit is as follows:
comparing the second missing maximum span value with the risk ratio, and comparing the second missing maximum span value with a preset second missing maximum span value threshold value and a preset risk ratio threshold value which are recorded and stored in the second missing maximum span value and the risk ratio threshold value:
if the second missing maximum span value is smaller than a preset second missing maximum span value threshold and the risk ratio is smaller than a preset risk ratio threshold, no signal is generated;
and if the second missing maximum span value is greater than or equal to a preset second missing maximum span value threshold or the risk ratio is greater than or equal to a preset risk ratio threshold, generating a risk signal.
4. The traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform of claim 1, wherein the fault risk level analysis process of the fault analysis unit is as follows:
the first step: acquiring average power consumption in a time threshold value which is larger than average power consumption corresponding to a preset average power consumption threshold value, and marking the average power consumption as excessive average power consumption PYg, wherein g refers to the number of the average power consumption which is larger than the preset average power consumption threshold value, marking the part of the excessive average power consumption PYg which exceeds the preset average power consumption threshold value as abnormal power consumption, further acquiring the sum of the abnormal power consumption, and marking the sum as the total abnormal power consumption YZ;
and a second step of: acquiring a set A of differences between a preset second light area threshold value and a second light area Lo in a time threshold value, acquiring the sum of all subsets in the set A, and marking the sum as an abnormal area value YM;
and a third step of: and pass through
Obtaining fault grade coefficients, wherein a1 and a2 are preset proportion coefficients of abnormal electricity consumption total quantity and abnormal area value respectively, a3 is a preset correction proportion coefficient, a1, a2 and a3 are positive numbers larger than zero, G is a fault grade coefficient, and the fault grade coefficient G is compared with a preset fault grade coefficient interval recorded and stored in the fault grade coefficient G:
if the fault grade coefficient G is larger than the maximum value in the preset fault grade coefficient interval, generating a first-level fault signal;
if the fault level coefficient G is located in the preset fault level coefficient interval, generating a secondary fault signal;
and if the fault level coefficient G is smaller than the minimum value in the preset fault level coefficient interval, generating a three-level fault signal.
5. The real-time traffic infrastructure monitoring and early warning system based on the big dipper data cloud platform according to claim 1, wherein the recommendation analysis unit immediately acquires the geographic coordinates of the current traffic signal lamp from the big dipper data cloud platform after receiving the primary fault signal, the secondary fault signal and the tertiary fault signal, and draws a circle with a preset radius, acquires a preset maintenance personnel list in the preset radius drawing circle, acquires the service data of the preset maintenance personnel list, wherein the service data comprises the service duration and the distance from the current traffic signal lamp, and the recommended sequencing process is as follows: the time length of the operation is preferential, and then the distance from the current traffic signal lamp is the distance, and the recommendation analysis unit sends the obtained recommendation preset maintenance personnel list to the early warning unit through the front-end server.
6. The traffic infrastructure real-time monitoring and early warning system based on the big dipper data cloud platform of claim 1, wherein the early warning data analysis process of the self-checking feedback unit is as follows:
the method comprises the steps of collecting the time length from the moment when a risk signal starts to be received to the moment when the early warning text is displayed, marking the time length as reaction time length, dividing the reaction time length into m sub-time nodes, wherein m is a natural number larger than zero, acquiring early warning text feature images corresponding to the risk signal in each sub-time node, and acquiring the real-time occupied total area value of the early warning text from the early warning text feature images corresponding to the risk signal;
meanwhile, the operation temperature of each electronic element in the display equipment corresponding to the early warning text in the reaction time is obtained, the electronic element corresponding to the operation temperature greater than the preset operation temperature threshold is obtained and marked as an abnormal electronic element, the temperature value of each abnormal electronic element in each sub-time node is obtained, the temperature value is compared with the preset temperature value threshold to analyze, the number of sub-time nodes corresponding to the temperature value greater than the preset temperature value threshold is obtained, the abnormal value is marked as an abnormal value, the number of abnormal electronic elements corresponding to the abnormal value greater than or equal to the preset abnormal value threshold is obtained, the abnormal electronic element number is marked as the risk electrical element number, and the real-time occupied total area value and the risk electrical element number are compared with the preset real-time occupied total area value threshold and the preset risk electrical element number stored in the real-time electronic element number and the preset risk electrical element number are recorded and analyzed:
if the real-time occupied total area value is equal to the preset real-time occupied total area value threshold value and the number of the risk electrical elements is smaller than the preset risk electrical element number threshold value, no signal is generated;
and if the real-time occupied total area value is not equal to the preset real-time occupied total area value threshold value or the number of the risk electrical elements is greater than or equal to the preset risk electrical element number threshold value, generating an early warning signal.
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| CN116800203B (en) * | 2023-06-27 | 2024-06-04 | 深圳市安科讯实业有限公司 | Photovoltaic inverter with operation supervision function based on Internet of things |
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