Municipal facility monitoring management platform based on Internet of Things

Abstract

The invention discloses a municipal facility monitoring management platform based on the Internet of Things, relates to the technical field of facility monitoring, and solves the problem that abnormal signals may be lost due to network congestion or signal interference in the process of sending the abnormal signals to a monitoring platform when the power of lighting equipment is abnormal and the like. According to the invention, omission coefficients in different time periods are accurately calculated, a monitoring strategy and power setting are intelligently adjusted, the monitoring interval time in the intermediate-frequency time period and the high-frequency time period is sequentially reduced, and the efficiency and accuracy of overall lighting management are influenced. According to the method, the power abnormity is monitored, the abnormal signal is sent to the information receiving unit in the management platform at high frequency, and the abnormal signal and response are transmitted more quickly by setting lower preset power, so that the situation that a fault is not found in time due to omission is prevented, and the abnormal condition of the lighting equipment is found earlier.

Description

Technical Field

[0001] The present invention relates to the technical field of facility monitoring, and in particular to a municipal facility monitoring and management platform based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization, the level of intelligence and automation of municipal facilities has gradually improved. As an important part of municipal facilities, efficient and intelligent management and monitoring of lighting equipment is particularly important. At present, many cities have deployed municipal lighting management systems based on Internet of Things technology to achieve remote control, fault detection and maintenance management of lighting equipment. These systems can collect the working status of lighting equipment in real time, such as switch status, brightness, power consumption and other data, through sensors and controllers installed at each lighting point, and transmit the data back to the central monitoring platform through wireless networks.

[0003] Most of the existing municipal lighting management systems adopt a centralized monitoring mode, which can uniformly dispatch and manage the urban lighting network. The central monitoring platform in the system has data processing and analysis capabilities, and can make intelligent judgments based on the collected data, such as automatically adjusting the brightness of street lights, detecting and prompting lighting equipment that needs maintenance, etc. In addition, advanced lighting management systems can also optimize lighting plans through algorithms to reduce energy consumption and extend the service life of equipment. However, although the existing technology has achieved certain results, there are still some shortcomings and challenges in practical applications.

[0004] The number of municipal lighting equipment is usually very large and covers a wide range, which brings great challenges to the monitoring and management of equipment. First, due to the large number of lighting points, the data flow of status information and fault signals of each lighting equipment is huge, which is easy to cause data loss during wireless network transmission. When the lighting equipment has problems such as power anomalies, the abnormal signal may be lost due to network congestion or signal interference during the process of sending to the monitoring platform, resulting in the monitoring platform being unable to accurately receive the real-time status of all equipment, thereby affecting the efficiency and accuracy of the overall lighting management; secondly, the monitoring device in the existing monitoring system may also have failure problems. The stability and reliability of the monitoring device are directly related to the operation quality of the entire lighting management system. If the monitoring device fails, it may not be able to correctly read or parse the data transmitted from the lighting equipment, and may even miss or misreport the fault information. In addition, damage to the lighting equipment itself is also an important factor leading to inaccurate monitoring data. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a municipal facility monitoring and management platform based on the Internet of Things, which solves the problem that when the lighting equipment has power anomalies and other problems, the abnormal signal may be lost due to network congestion or signal interference during the process of sending to the monitoring platform, resulting in the monitoring platform being unable to accurately receive the real-time status of all devices, thereby affecting the efficiency and accuracy of the overall lighting management; secondly, when the monitoring device fails, it may lead to the inability to correctly read or parse the data transmitted from the lighting equipment, and may even miss or misreport the fault information;

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a municipal facility monitoring and management platform based on the Internet of Things, comprising:

[0007] A data acquisition module is used to collect key information of the lighting equipment under working conditions and send the key information to a subsequent data anomaly module, wherein the key information includes working current and rated voltage;

[0008] The data anomaly module is used to extract the working current and rated voltage from the key information, and multiply the two to obtain the specific power usage, and compare the power usage with the preset power M1. When the power usage exceeds the range of the preset power M1, an abnormal record is made and an abnormal signal is generated, and then the generated abnormal signal is sent to the management platform;

[0009] A management platform, used to monitor and manage lighting equipment, including an information receiving unit, a monitoring unit and a display unit;

[0010] An information receiving unit, used for receiving an abnormal signal, converting the abnormal signal into display information and transmitting it to a display unit for display;

[0011] A display unit is used to display abnormal signals and specific lighting equipment corresponding to the abnormal signals, so that the staff can check the corresponding lighting equipment;

[0012] The data analysis module divides a day into 24 time periods, obtains the number of abnormal records and the number of abnormal information displayed in each time period within the preset time T, judges and analyzes the number of abnormal records and the number of information displayed in each time period, and determines the transmission omission coefficient of each time period, where the preset time T is expressed as the time period of n days from the current moment, and the data of the day when the data is obtained is not included, T = ﹛1, 2, ..., n﹜;

[0013] The time period division module is used to divide each time period into a high-frequency time period, a medium-frequency time period and a low-frequency time period according to the omission coefficient of the 24 time periods;

[0014] Monitoring unit, used to formulate different monitoring and management plans for different time periods;

[0015] Preferably, in the data analysis module, the specific method for determining the transmission omission coefficient for each time period is as follows:

[0016] S1: Divide a day into 24 time periods. Then select any one time period as the analysis time period, and obtain the number of abnormal records and the number of display messages for each day of the preset time T, and record them as Hi and Gi respectively, where 1 ≤ i ≤ n;

[0017] S2: Then, through the formula Di = |Hi - Gi|, determine the difference Di between the number of abnormal records and the number of display messages. Then, through the formula determine the average value Ep of the n groups of differences Di. Subsequently, calculate the discrete value U through the formula to obtain the discrete value U;

[0018] S3: Compare the discrete value U with the preset value Q1. When U is greater than the preset value Q1, through the formula Gi = |Di - Ep|, obtain the judgment value Gi. Sort the corresponding Dis in descending order of Gi to obtain an array of arranged values of Di. Select the first x% of the values in the array of arranged values and calculate their average value as the omission value W1 for this analysis time period, where x is a preset value;

[0019] S4: Then, through the formula obtain the omission coefficient corresponding to this time period, where α is an influencing factor;

[0020] S5: Repeat steps S1 - S4 to determine the corresponding omission coefficients Bes for each of the 24 time periods in turn as the analysis time period.

[0021] Preferably, in S3, it further includes:

[0022] When U is less than the preset value Q1, through the formula Gi = |Di - Ep|, obtain the judgment value Gi. Sort the corresponding Dis in descending order of Gi to obtain an array of arranged values of Di. Select the first value and the last value in the array of arranged values, and take the average of the two as the omission value W1 for this analysis time period.

[0023] Preferably, in the time period division module, the specific division method is as follows:

[0024] Obtain the omission coefficients Bes for all time periods. Determine each omission coefficient Bes with the interval [Z1, Z2]. When Bes > Z2, divide the corresponding time period into high-frequency time periods. When Z1 ≤ Bes ≤ Z2, divide the corresponding time period into medium-frequency time periods. When Bes < Z1, divide the corresponding time period into low-frequency time periods, where the interval [Z1, Z2] is a preset value.

[0025] Preferably, in the detection unit, the specific formulation is as follows:

[0026] During the high-frequency period, set an interval time t1 to monitor and detect power anomalies, and adjust the preset power M1 to M2, where M2 is a preset value and M2 < M1;

[0027] During the medium-frequency period, set an interval time t2 to monitor and detect power anomalies, and adjust the preset power M1 to M3, where M3 is a preset value and M2 < M3 < M1;

[0028] During the low-frequency period, set an interval time t3 to monitor and detect power anomalies, and the preset power remains unchanged at M1.

[0029] Specifically, t1 < t2 < t3, and the specific parameters are set by the staff;

[0030] Preferably, it further includes a troubleshooting module for performing special troubleshooting during the high-frequency period and the medium-frequency period to determine damaged lighting devices and good devices.

[0031] Preferably, the specific method of special troubleshooting is as follows:

[0032] When in the high-frequency period and the medium-frequency period, judge the omission coefficient. When the omission coefficient is 0, judge the power usage situation. When the power usage is within the range of the preset power M2 or M3, determine that the lighting device is a good device;

[0033] Preferably, it further includes:

[0034] When the power usage is 0, determine that the lighting device is a damaged device or a non-working lighting device, and send troubleshooting information to the monitoring platform to remind the staff to troubleshoot the lighting device;

[0035] Do not perform any processing in other cases.

[0036] The present invention provides an Internet of Things-based monitoring and management platform for municipal facilities. Compared with the prior art, it has the following beneficial effects:

[0037] (1) Through its data acquisition module, the present invention continuously monitors the working status of lighting devices, including working current and rated voltage. Through the acquisition and analysis of real-time data, the platform can immediately detect power anomalies. When the power usage of the device exceeds the preset value, the system will automatically generate an anomaly signal and record it. This real-time monitoring not only helps managers quickly identify problem devices but also enables intervention before the problem becomes a significant failure. In addition, the data anomaly module ensures the precise capture and immediate response to abnormal situations, avoiding a wider range of service interruptions caused by equipment failures.

[0038] (2) The present invention not only monitors abnormalities, but also intelligently adjusts the monitoring strategy and power setting by accurately calculating the omission coefficients in different time periods. The monitoring intervals in the medium frequency period and the high frequency period are reduced successively, that is, the power abnormalities are monitored and the abnormal signals are sent to the information receiving unit in the management platform at a high frequency. By setting a lower preset power, the abnormal signals and responses are transmitted more quickly to prevent the delayed detection of faults due to omissions, so as to detect abnormal conditions of the lighting equipment earlier.

[0039] (3) The platform uses a special screening module to conduct in-depth inspections of lighting equipment during high-risk periods, identify other possible situations of the lighting equipment, and send them to staff. Combined with the monitoring solution, the systematic maintenance method significantly improves the operating efficiency and reliability of municipal facilities, ensuring the continuous operation of the city's lighting system and the safety of citizens. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The present invention will be further described below in conjunction with the accompanying drawings.

[0041] Figure 1 This is a framework diagram of the municipal facility monitoring and management platform based on the Internet of Things of the present invention; DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1

[0044] See also Figure 1 ,The present invention provides a municipal facility monitoring and management platform based on the Internet of Things, including;

[0045] The data acquisition module is used to collect key information of the lighting equipment under working status and send the key information to the subsequent data anomaly module. The key information includes working current and rated voltage. The working current is collected by the current sensor. The rated voltage is a fixed value, which is specifically set by the staff.

[0046] The data anomaly module is used to extract the working current and rated voltage from the key information, and multiply the two to obtain the specific power usage, and compare the power usage with the preset power M1. When the power usage exceeds the range of the preset power M1, an abnormal record is made and an abnormal signal is generated, and then the generated abnormal signal is sent to the management platform;

[0047] A management platform, used to monitor and manage lighting equipment, including an information receiving unit, a monitoring unit and a display unit;

[0048] An information receiving unit, used for receiving an abnormal signal, converting the abnormal signal into display information and transmitting it to a display unit for display;

[0049] A display unit is used to display abnormal signals and specific lighting equipment corresponding to the abnormal signals, so that the staff can check the corresponding lighting equipment;

[0050] The data analysis module divides a day into 24 time periods, obtains the number of abnormal records and the number of abnormal information displayed in each time period within the preset time T, judges and analyzes the number of abnormal records and the number of information displayed in each time period, and determines the transmission omission coefficient of each time period, where the preset time T is expressed as the time period of n days from the current moment, and the data of the day when the data is obtained is not included, T = ﹛1, 2, ..., n﹜;

[0051] The specific method for determining the transmission omission coefficient for each time period is:

[0052] S1: Divide a day into 24 time periods, then select any time period as the analysis period, obtain the number of abnormal records and the number of displayed information per day in the preset time T, and record them as Hi and Gi respectively;

[0053] The preset time T is represented by a time period of n days from the current moment, and in this implementation, n is 45, 1≤i≤n;

[0054] S2: Then, the difference Di between the number of abnormal records and the number of displayed information is determined by the formula Di = |Hi-Gi|, and then the formula Determine the average value Ep of n groups of difference values ​​Di, and then calculate the discrete value U, through the formula Get the discrete value U;

[0055] S3: Compare the discrete value U with the preset value Q1. When U is greater than the preset value Q1, the judgment value Gi is obtained by the formula Gi=|Di-Ep|. According to the order of Gi from large to small, the corresponding Di is sorted to obtain the arrangement value team about Di. The values ​​of the first x% of the arrangement value pairs are selected, and their average value is calculated as the missing value W1 of the analysis period, where x is a preset value, which is specifically set by the staff;

[0056] When U is less than the preset value Q1, the judgment value Gi is obtained through the formula Gi = |Di - Ep|. According to the order from large to small of Gi, the corresponding Dis are sorted to obtain an array of arranged values of Di. The first value and the last value of the arranged value pair are selected, and the average value of the two is taken as the omission value W1 of this analysis period.

[0057] S4: Then through the formula the omission coefficient corresponding to this period is obtained, where α is the influence factor.

[0058] S5: Repeat steps S1 - S4 to determine 24 periods as analysis periods in turn to obtain their corresponding omission coefficients Bes.

[0059] The period division module is used to divide each period into high - frequency periods, medium - frequency periods and low - frequency periods according to the omission coefficients of 24 periods. The specific division method is as follows:

[0060] Obtain the omission coefficients Bes of all periods. Determine each omission coefficient Bes with the interval [Z1, Z2]. When Bes > Z2, the corresponding period is divided into a high - frequency period. When Z1 ≤ Bes ≤ Z2, the corresponding period is divided into a medium - frequency period. When Bes < Z1, the corresponding period is divided into a low - frequency period. Among them, the interval [Z1, Z2] is a preset value, and the specific parameters are set by the staff.

[0061] Specifically, the high - frequency period means the period in which the omission coefficient is relatively large, resulting in a relatively large number of omissions. The medium - frequency period means the period in which the omission coefficient is relatively large, resulting in a relatively large number of omissions. The low - frequency period means the period in which the omission coefficient is relatively small, resulting in a relatively small number of omissions.

[0062] Embodiment 2

[0063] In the specific implementation process of this embodiment, on the basis of Embodiment 1, and the difference from Embodiment 1 is that this embodiment is for the formulation of the monitoring and management plan:

[0064] The monitoring unit is used to formulate different monitoring and management plans for different periods. The specific formulation plan is as follows:

[0065] In the high - frequency period, set the interval time t1 to monitor and detect power anomalies, and adjust the preset power M1 to M2, where M2 < M1. The specific parameter setting is set by the staff.

[0066] In the medium - frequency period, set the interval time t2 to monitor and detect power anomalies, and adjust the preset power M1 to M3, where M2 < M3 < M1. The specific parameter setting is set by the staff.

[0067] During the low-frequency period, the monitoring and detection of abnormal power are carried out at the set interval time t3, and the preset power remains unchanged at M1;

[0068] Specifically, t1 < t2 < t3, and the specific parameters are set by the staff;

[0069] Through the above solution formulation, the interval time in the medium-frequency period and the high-frequency period decreases in turn, that is, the abnormal power is monitored, and the abnormal signal is sent to the information receiving unit in the management platform at a high frequency. By setting a lower preset power, the abnormal signal and response are transmitted more quickly to prevent the failure from being discovered in time due to omission, so as to detect the abnormal situation of the lighting equipment earlier;

[0070] Embodiment Three

[0071] In the specific implementation process of this embodiment, on the basis of Embodiment One and Embodiment Two, and the difference from Embodiment One and Embodiment Two is that

[0072] The troubleshooting module is used to perform special troubleshooting during the high-frequency period and the medium-frequency period to determine the damaged lighting equipment and the good equipment. The specific method is as follows:

[0073] When in the high-frequency period and the medium-frequency period, judge the omission coefficient. When the omission coefficient is 0, judge the power usage situation. When the power usage is within the range of the preset power M2 or M3, determine that the lighting equipment is good equipment;

[0074] When the power usage is 0, determine that the lighting equipment is damaged equipment or non-working lighting equipment, and send the troubleshooting information to the monitoring platform to remind the staff to troubleshoot the lighting equipment;

[0075] Do not perform any processing in other cases;

[0076] Embodiment Four

[0077] In the specific implementation process of this embodiment, it includes all the implementation processes of the above three groups of embodiments.

[0078] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0079] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The municipal facilities monitoring and management platform based on the Internet of Things is characterized by: include: A data acquisition module is used to collect key information of the lighting equipment under working conditions and send the key information to a subsequent data anomaly module, wherein the key information includes working current and rated voltage; The data anomaly module is used to extract the working current and rated voltage from the key information, and multiply the two to obtain the specific power usage, and compare the power usage with the preset power M1. When the power usage exceeds the range of the preset power M1, an abnormal record is made and an abnormal signal is generated, and then the generated abnormal signal is sent to the management platform; A management platform, used to monitor and manage lighting equipment, including an information receiving unit, a monitoring unit and a display unit; An information receiving unit, used for receiving an abnormal signal, converting the abnormal signal into display information and transmitting it to a display unit for display; A display unit is used to display abnormal signals and specific lighting equipment corresponding to the abnormal signals, so that the staff can check the corresponding lighting equipment; The data analysis module divides a day into 24 time periods, obtains the number of abnormal records and the number of abnormal information displayed in each time period within the preset time T, judges and analyzes the number of abnormal records and the number of information displayed in each time period, and determines the transmission omission coefficient of each time period, where the preset time T is expressed as the time period of n days from the current moment, and the data of the day when the data is obtained is not included, T = ﹛1, 2, ..., n﹜; The time period division module is used to divide each time period into a high-frequency time period, a medium-frequency time period and a low-frequency time period according to the omission coefficient of the 24 time periods; The monitoring unit is used to formulate different monitoring and management plans for different time periods.

2. The municipal facility monitoring and management platform based on the Internet of Things according to claim 1 is characterized in that: In the data analysis module, the specific method for determining the transmission omission coefficient for each period is: S1: Divide a day into 24 time periods, then select any time period as the analysis period, obtain the number of abnormal records and the number of displayed information per day in the preset time T, and record them as Hi and Gi, respectively, where 1≤i≤n; S2: Then, the difference Di between the number of abnormal records and the number of displayed information is determined by the formula Di = |Hi-Gi|, and then the formula Determine the average value Ep of n groups of difference values ​​Di, and then calculate the discrete value U, through the formula Get the discrete value U; S3: Compare the discrete value U with the preset value Q1. When U is greater than the preset value Q1, the judgment value Gi is obtained by the formula Gi=|Di-Ep|. According to the order of Gi from large to small, the corresponding Di is sorted to obtain the arrangement value team about Di. The values ​​of the first x% of the arrangement value pairs are selected, and their average value is calculated as the missing value W1 of the analysis period, where x is the preset value; S4: Then use the formula Get the omission coefficient corresponding to the period, where α is the impact factor; S5: Repeat steps S1-S4 to determine 24 time periods as analysis time periods in turn to obtain their corresponding omission coefficients Bes.

3. The municipal facility monitoring and management platform based on the Internet of Things according to claim 2 is characterized in that: Also included in S3: When U is less than the preset value Q1, the judgment value Gi is obtained through the formula Gi=|Di-Ep|, and the corresponding Di are sorted in order from large to small to obtain the arrangement value team about Di. The first value and the last value of the arrangement value pair are selected, and the average of the two is taken as the missing value W1 of the analysis period.

4. The municipal facility monitoring and management platform based on the Internet of Things according to claim 1 is characterized in that: In the time period division module, the specific division method is: Obtain the omission coefficient Bes for all time periods, and determine each omission coefficient Bes with the interval [Z1, Z2]. When Bes > Z2, divide the corresponding time period into a high-frequency time period. When Z1 ≤ Bes ≤ Z2, divide the corresponding time period into a medium-frequency time period. When Bes < Z1, divide the corresponding time period into a low-frequency time period, where the interval [Z1, Z2] is a preset value.

5. The municipal facility monitoring and management platform based on the Internet of Things according to claim 1 is characterized in that: In the detection unit, the specific formulation scheme is as follows: During the high-frequency time period, set the interval time t1 to monitor and detect power abnormalities, and adjust the preset power M1 to M2, where M2 is a preset value and M2 < M1; During the medium-frequency time period, set the interval time t2 to monitor and detect power abnormalities, and adjust the preset power M1 to M3, where M3 is a preset value and M2 < M3 < M1; During the low-frequency time period, set the interval time t3 to monitor and detect power abnormalities, and keep the preset power unchanged at M1. Specifically, t1 < t2 < t3, and the specific parameters are set by the staff.

6. The municipal facility monitoring and management platform based on the Internet of Things according to claim 1 is characterized in that: It also includes a troubleshooting module for performing special troubleshooting during the high-frequency and medium-frequency time periods to determine damaged lighting devices and good devices.

7. The municipal facility monitoring and management platform based on the Internet of Things according to claim 6 is characterized in that: The specific method of special troubleshooting is as follows: When in the high-frequency and medium-frequency time periods, judge the omission coefficient. When the omission coefficient is 0, judge the power usage situation. When the power usage is within the range of the preset power M2 or M3, determine that the lighting device is a good device.

8. The municipal facility monitoring and management platform based on the Internet of Things according to claim 7 is characterized in that: It also includes: When the power usage is 0, determine that the lighting device is a damaged device or a non-working lighting device, and send troubleshooting information to the monitoring platform to remind the staff to troubleshoot the lighting device; Do not perform any processing in other cases.

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