CN114297018A - Intelligent equipment management method and system based on Happy forest zone - Google Patents
Intelligent equipment management method and system based on Happy forest zone Download PDFInfo
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Abstract
The invention relates to an intelligent equipment management method and system based on a happy forest belt, which are characterized in that a monitoring sensor is additionally arranged, the characteristics of a Wireless Sensor Network (WSN) are fully utilized, a monitoring network is established by combining the existing wireless fidelity (WiFi) network, and the actual power supply and network state are combined to plan a transmission mode and a transmission path, so that the monitoring reliability and the data transmission stability are improved; the invention also realizes the abnormal monitoring of the equipment by comprehensively considering the network, the equipment, the user and the environment, thereby improving the accuracy and the reliability of the monitoring; the invention also constructs the work order triggering indication by comprehensively considering the equipment abnormality degree, the maintenance information and the evaluation, thereby improving the accuracy of the work order.
Description
Technical Field
The invention relates to an intelligent equipment management method and system based on a Happy forest zone.
Background
In the happy forest zone, hundreds of thousands of subsystem devices operate and are complicated to associate with each other. Timely maintenance is needed among equipment systems to ensure reliable service; the problem that the abnormal value existing in the system and the equipment is discovered, the abnormal type is rapidly judged, and the abnormal equipment is positioned is required to be solved.
However, the existing devices generally perform self-test early warning and transmission through the existing networked network based on self-carried detection programs; once the equipment is powered on or the network is abnormal, the self-checking program and data transmission are seriously influenced; the existing early warning technology is mainly based on self information of equipment for detection, and cannot give consideration to the requirements of a network, an external environment and a user, so that the accuracy and reliability of abnormal detection of the equipment cannot be ensured. At present, in the aspect of exception handling, the manpower maintenance work order is generally directly issued, the allocation personnel handle, the accuracy of issuing the manpower work order cannot be guaranteed at the moment, once the manpower maintenance work order information is wrong, the allocation personnel cannot solve the problem, the efficiency of exception handling can be seriously reduced, the experience of a user can be influenced, and the popularization of service is not facilitated.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides an intelligent equipment management method based on a Happy forest zone, which is characterized by comprising the following steps:
networking: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network;
a monitoring step: the sensor monitors the service equipment and performs real-time data index T according to the signal strength RSSIReal timeDetermining equipment abnormal weight W1 according to the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determining whether to perform early warning according to the value of W1;
an abnormal maintenance step: and performing abnormity evaluation according to whether the abnormity information indicating early warning is received or not, and determining the type of the work order according to an evaluation result.
Wherein, the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment, and comprises:
when the commercial power of the service equipment is stably supplied, selecting a wifi network for transmission;
when the service equipment is not powered by mains supply, selecting a zigbee network for transmission;
when the mains supply is unstable, selecting a network for transmission according to the network weight W2;
wherein W2 ═ t/Tw × (RSSIw/RSSIz) × (Kw/Kz); tz and Tw are respectively network time delay of a Zigbee network and a WiFi network; RSSIw and RSSIz are the signal intensity of the WiFi network and the ZigBee network respectively; kw and Kz are reliability parameters of the WiFi network and the ZigBee network respectively.
Wherein the selection of the path by the sensor according to the transmission cost comprises: determining a transmission cost C between the two sensors, and determining a path transmission cost W based on the transmission cost C;
wherein W ═ Σ C;
c ═ RSSI/d × ln (r) + RSSI/d × ln(s); wherein, R is a signal intensity coefficient, and S is a signal-to-noise ratio (SNR) coefficient; d is the distance between the sensors.
Where, W1 ═ m × p × (RSSI/RSSI)max+TReal time/TNetwork ideal value+T/TMaintenance cycle)
TNetwork ideal valueMinimum value of network delay; t isMaintenance cycleIndicating a maintenance cycle of the equipment; RSSImaxIs the maximum signal strength.
After the early warning information is received, generating a maintenance work order according to an evaluation result W3 of the early warning information, wherein the maintenance work order comprises an emergency work order and a maintenance work order, and the maintenance work order is updated according to a feedback result of the emergency work order;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)×w1
Wherein a is the maintenance record coefficient of the key component, b is the maintenance record coefficient of the common component, P1 and P2 are the abnormal frequency of the key component and the common component respectively, and q is the poor evaluation degree of the service equipment.
The invention also provides an intelligent equipment management system, which comprises:
a networking module: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network;
a monitoring module: the sensor monitors the service equipment and performs real-time data index T according to the signal strength RSSIReal timeDetermining equipment abnormal weight W1 according to the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determining whether to perform early warning according to the value of W1;
an exception maintenance module: and performing abnormity evaluation according to whether the abnormity information is received or not, and determining the type of the work order according to an evaluation result.
The intelligent equipment management method and system based on the Happy forest zone have the advantages that the monitoring sensor is additionally arranged, the characteristics of the WSN are fully utilized, the monitoring network is constructed by combining the existing WiFi network, and the transmission mode and the transmission path are planned by combining the actual power supply and the actual network state, so that the monitoring reliability and the data transmission stability are improved; the invention also realizes the abnormal monitoring of the equipment by comprehensively considering the network, the equipment, the user and the environment, thereby improving the accuracy and the reliability of the monitoring; the invention also constructs the work order triggering indication through the comprehensive consideration of the equipment abnormality degree, the maintenance information and the evaluation, thereby improving the accuracy of the work order.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart of a method of a preferred embodiment of the present invention;
fig. 2 is a system schematic of a preferred embodiment of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
In a happy forest zone, various service devices are provided, and most of the service devices need mains supply, but due to long-time use, untimely maintenance, network abnormality and the like, various problems are easy to occur to the devices, and effective monitoring data cannot be obtained sometimes, so an effective device management scheme is needed.
As shown in fig. 1, the present invention provides an intelligent device management method based on a happy forest belt, which includes:
networking: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network; on the basis of the existing network, a wireless sensor network is additionally arranged and is communicated based on Zigbee, so that the problem that early warning cannot be timely performed when service equipment or the network is abnormal can be solved.
A monitoring step: the sensor monitors the service equipment and performs real-time data index T according to the signal strength RSSIReal timeDetermining equipment abnormal weight W1 according to the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determining whether to perform early warning according to the value of W1;
W1＝m×p×(RSSI/RSSImax+Treal time/TNetwork ideal value+T/TMaintenance cycle)
TNetwork ideal valueMinimum value of network delay; t isMaintenance cycleIndicating a maintenance cycle of the equipment; RSSImaxIs the maximum signal strength. The data real-time index may be a time required for data transmission.
(ii) m ═ ((E-Ee)/Ee) + ((F-Fe)/Fe) + ((G-Ge)/Ge); e is ambient temperature, F is humidity, G is light intensity; ee can select the lowest temperature of the current season according to the season; fe can be selected according to the season, and the lowest humidity of the current season is selected; ge represents a light rating, an exemplary light intensity at which the sun may be selected for setting.
An abnormal maintenance step: and performing abnormity evaluation according to whether the abnormity information indicating early warning is received or not, and determining the type of the work order according to an evaluation result.
When the service equipment provides service, the service equipment is not only influenced by the operation of the equipment, but also has direct relation with the surrounding environment and the feedback of a user, so the factors need to be considered when the equipment is monitored, and because the data transmission is influenced by the network environment and external factors, the invention comprehensively considers the factors instead of only paying attention to the equipment when the equipment is monitored, thereby more effectively early warning the state of the equipment in time and simultaneously ensuring the reliability and timeliness of early warning transmission.
After the WSN is added, the service device may perform data transmission at least through the WiFi and Zigbee networks, so that there may be a variety of options when data transmission is performed, and in order to perform data transmission effectively, a specific transmission mode needs to be set.
Wherein, the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment, and comprises:
when the commercial power of the service equipment is stably supplied, selecting a wifi network for transmission;
when the service equipment is not powered by mains supply, selecting a zigbee network for transmission;
when the mains supply is unstable, selecting a network for transmission according to the network weight W2;
wherein W2 ═ t/Tw × (RSSIw/RSSIz) × (Kw/Kz); tz and Tw are respectively network time delay of a Zigbee network and a WiFi network; RSSIw and RSSIz are the signal intensity of the WiFi network and the ZigBee network respectively; kw and Kz are reliability parameters of the WiFi network and the ZigBee network respectively.
When the W2 is greater than the first threshold, wifi is selected, and when the W2 is less than the second threshold, zigbee is selected, otherwise, a hybrid network is selected for transmission, wherein the setting of the threshold may be selected for transmission according to the historical weight and the corresponding network status, for example, 1/3 and 2/3 of the average value of the historical weight are set as the first and second thresholds. After the selection of the network is completed, selecting a proper path by calculating transmission cost, for example, calculating the cost among nodes in a WiFi network when WiFi is selected; when Zigbee is selected, calculating the cost among all nodes in the Zigbee network; when selecting a hybrid network, the transmission costs of the individual network connections need to be calculated.
Wherein the selection of the path by the sensor according to the transmission cost comprises: determining a transmission cost C between the two sensors, and determining a path transmission cost W based on the transmission cost C;
wherein W ═ Σ C; preferably, the cost before two nodes on each path is calculated, the transmission cost W of the path is determined by the sum of the costs between every two nodes, and the path with the minimum path cost is selected for transmission;
c ═ RSSI/d × ln (r) + RSSI/d × ln(s); wherein, R is a signal intensity coefficient, and S is a signal-to-noise ratio (SNR) coefficient; d is the distance between the sensors.
Wherein, R is (RSSI-RSSIth)/(RSSImax-RSSIth)
RSSIth is a received signal strength threshold value, a specific value can be set according to a network environment history mean value;
S＝(SNR-SNRth)/(SNRmax-SNRth)
the SNR is the threshold value of the current signal-to-noise ratio SNRth, and the specific value can be set according to the historical average value of the network environment; SNRmax is an ideal value of the signal-to-noise ratio, and refers to a value of the SNR of the network under the optimal environment.
By considering the network transmission mode and the transmission cost, the efficiency of network data transmission can be improved, the monitoring data can be timely transmitted to a network terminal, and the reliability of data transmission can be ensured by setting the transmission cost.
After the early warning information is received, generating a maintenance work order according to an evaluation result W3 of the early warning information, wherein the maintenance work order comprises an emergency work order and a maintenance work order, and the maintenance work order is updated according to a feedback result of the emergency work order;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)×W1
Wherein a is a maintenance record coefficient of a key component (when there is a problem with the key component, a is 1, and when there is no problem with the key component, a is 0), and b is a maintenance record coefficient of a general component (when there is a problem with the general component, a is 1, and when there is no problem with the general component, a is 0); p1 and P2 are abnormal frequencies of key components and common components respectively, and q is poor evaluation degree of the service equipment.
The equipment maintenance history and evaluation are considered to be subjected to abnormal evaluation to determine whether the equipment enters a maintenance later period or not, a maintenance work order is generated, and then a maintenance work order is determined according to the emergency work order, so that the accuracy and the reliability of the maintenance work order can be improved.
When the abnormal information is not received, periodically calculating W3, determining whether a maintenance period needs to be entered according to W3, and maintaining the service equipment;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)；
In the prior art, work orders are directly generated according to abnormal information, however, actual abnormity cannot be accurately judged according to the abnormal information, so that an emergency work order is added when the work order is set, the emergency work order carries out remote information confirmation by contacting with workers on the site of service equipment, and then a maintenance work order is generated according to the confirmation information, so that the accuracy of the maintenance work order is improved, the accurate positioning of maintenance personnel is realized, the attendance frequency of the maintenance personnel can be reduced, and the working efficiency is improved.
The invention also provides an intelligent equipment management system based on the happy forest belt, as shown in fig. 2, the management system comprises:
a networking module: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network; the networking module is positioned at the service equipment and the network side server;
a monitoring module: the sensor monitors the service equipment, determines the abnormal weight W1 of the equipment according to the signal strength RSSI, the real-time data real-time index T, the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determines whether to carry out early warning according to the value of W1; the monitoring module is positioned at the service equipment and the network side server;
W1＝m×p×(RSSI/RSSImax+Treal time/TNetwork ideal value+T/TMaintenance cycle)
TNetwork ideal valueMinimum value of network delay; the T maintenance period represents a maintenance period of the device; RSSImaxIs the maximum signal strength.
An exception maintenance module: and performing abnormity evaluation according to whether the abnormity information is received or not, and determining the type of the work order according to an evaluation result. The abnormal maintenance module is positioned on the network side server.
Wherein, the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment, and comprises:
when the commercial power of the service equipment is stably supplied, selecting a wifi network for transmission;
when the service equipment is not powered by mains supply, selecting a zigbee network for transmission;
when the mains supply is unstable, selecting a network for transmission according to the network weight W2;
wherein W2 ═ (Tz/Tw) (RSSIw/RSSIz) (Kw/Kz); tz and Tw are respectively network time delay of a Zigbee network and a WiFi network; RSSIw and RSSIz are the signal intensity of the WiFi network and the ZigBee network respectively; kw and Kz are reliability parameters of the WiFi network and the ZigBee network respectively.
Wherein the selection of the path by the sensor according to the transmission cost comprises: determining a transmission cost C between the two sensors, and determining a path transmission cost W based on the transmission cost C;
wherein W ═ Σ C; preferably, a path with the minimum cost is selected for transmission;
c ═ RSSI/d × ln (r) + RSSI/d × ln(s); wherein, R is a signal intensity coefficient, and S is a signal-to-noise ratio (SNR) coefficient; d is the distance between the sensors.
After the abnormal maintenance module receives the early warning information, a maintenance work order is generated according to an evaluation result W3 of the early warning information, the maintenance work order comprises an emergency work order and a maintenance work order, and the maintenance work order is updated according to a feedback result of the emergency work order;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)×w1
Wherein a is the maintenance record coefficient of the key component, b is the maintenance record coefficient of the common component, P1 and P2 are the abnormal frequency of the key component and the common component respectively, and q is the poor evaluation degree of the service equipment.
In the prior art, work orders are directly generated according to abnormal information, however, actual abnormity cannot be accurately judged according to the abnormal information, so that an emergency work order is added when the work order is set, the emergency work order carries out remote information confirmation by contacting with workers on the site of service equipment, and then a maintenance work order is generated according to the confirmation information, so that the accuracy of the maintenance work order is improved, the accurate positioning of maintenance personnel is realized, the attendance frequency of the maintenance personnel can be reduced, and the working efficiency is improved.
According to the intelligent equipment management method and system, the monitoring sensor is additionally arranged, the characteristics of the WSN are fully utilized, the monitoring network is constructed by combining the existing WiFi network, and the transmission mode and the transmission path are planned by combining the actual power supply and the actual network state, so that the monitoring reliability and the data transmission stability are improved; the invention also realizes the abnormal monitoring of the equipment by comprehensively considering the network, the equipment, the user and the environment, thereby improving the accuracy and the reliability of the monitoring; the invention also constructs the work order triggering indication by comprehensively considering the equipment abnormality degree, the maintenance information and the evaluation, thereby improving the accuracy of the work order.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (10)
1. An intelligent equipment management method based on a happy forest belt is characterized by comprising the following steps:
networking: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network;
a monitoring step: the sensor monitors the service equipment and performs real-time data index T according to the signal strength RSSIReal timeDetermining equipment abnormal weight W1 according to the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determining whether to perform early warning according to the value of W1;
an abnormal maintenance step: and performing abnormity evaluation according to whether the abnormity information indicating early warning is received or not, and determining the type of the work order according to an evaluation result.
2. The method of claim 1, wherein the sensor selecting a data transmission mode based on the power state of the service device and the network state comprises:
when the commercial power of the service equipment is stably supplied, selecting a wifi network for transmission;
when the service equipment is not powered by mains supply, selecting a zigbee network for transmission;
when the mains supply is unstable, selecting a network for transmission according to the network weight W2;
wherein W2 ═ t/Tw × (RSSIw/RSSIz) × (Kw/Kz); tz and Tw are respectively network time delay of a Zigbee network and a WiFi network; RSSIw and RSSIz are the signal intensity of the WiFi network and the ZigBee network respectively; kw and Kz are reliability parameters of the WiFi network and the ZigBee network respectively.
3. The method of claim 1 or 2, wherein the sensor making the path selection based on transmission cost comprises: determining a transmission cost C between the two sensors, and determining a path transmission cost W based on the transmission cost C;
wherein W ═ Σ C;
c ═ RSSI/d × ln (r) + RSSI/d × ln(s); wherein, R is a signal intensity coefficient, and S is a signal-to-noise ratio (SNR) coefficient; d is the distance between the sensors.
4. The method of claim 1, wherein the method comprises:
W1＝m×p×(RSSI/RSSImax+Treal time/TNetwork ideal value+T/TMaintenance cycle)
TNetwork ideal valueMinimum value of network delay; t isMaintenance cycleIndicating a maintenance cycle of the equipment; RSSImaxIs the maximum signal strength.
5. The method of claim 1, wherein the method comprises: after the early warning information is received, generating a maintenance work order according to an evaluation result W3 of the early warning information, wherein the maintenance work order comprises an emergency work order and a maintenance work order, and the maintenance work order is updated according to a feedback result of the emergency work order;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)×w1
Wherein a is the maintenance record coefficient of the key component, b is the maintenance record coefficient of the common component, P1 and P2 are the abnormal frequency of the key component and the common component respectively, and q is the poor evaluation degree of the service equipment.
6. An intelligent equipment management system based on a happy forest zone, characterized in that, the management system includes:
a networking module: each service device is additionally provided with a monitoring sensor; the sensor selects a data transmission mode according to the power supply state and the network state of the service equipment; the sensor selects a path according to the transmission cost; the sensors communicate based on a WiFi network and/or a Zigbee network;
a monitoring module: the sensor monitors the service equipment and performs real-time data index T according to the signal strength RSSIReal timeDetermining equipment abnormal weight W1 according to the equipment running time T, the weather state influence coefficient m and the evaluation goodness p of the historical state, and determining whether to perform early warning according to the value of W1;
an exception maintenance module: and performing abnormity evaluation according to whether the abnormity information indicating early warning is received or not, and determining the type of the work order according to an evaluation result.
7. The system of claim 6, wherein the sensor selecting a data transmission mode based on the power state of the service device and the network state comprises:
when the commercial power of the service equipment is stably supplied, selecting a wifi network for transmission;
when the service equipment is not powered by mains supply, selecting a zigbee network for transmission;
when the mains supply is unstable, selecting a network for transmission according to the network weight W2;
wherein W2 ═ t/Tw × (RSSIw/RSSIz) × (Kw/Kz); tz and Tw are respectively network time delay of a Zigbee network and a WiFi network; RSSIw and RSSIz are the signal intensity of the WiFi network and the ZigBee network respectively; kw and Kz are reliability parameters of the WiFi network and the ZigBee network respectively.
8. The system of claim 6 or 7, wherein the sensor making the path selection based on transmission cost comprises: determining a transmission cost C between the two sensors, and determining a path transmission cost W based on the transmission cost C;
wherein W ═ Σ C;
c ═ RSSI/d × ln (r) + RSSI/d × ln(s); wherein, R is a signal intensity coefficient, and S is a signal-to-noise ratio (SNR) coefficient; d is the distance between the sensors.
9. The system of claim 6,
W1＝m×p×(RSSI/RSSImax+Treal time/TNetwork ideal value+T/TMaintenance cycle)
TNetwork ideal valueMinimum value of network delay; t isMaintenance cycleIndicating a maintenance cycle of the equipment; RSSImaxIs the maximum signal strength.
10. The system of claim 6, wherein after the abnormal maintenance module receives the warning information, a maintenance work order is generated according to the evaluation result W3 of the warning information, the maintenance work order comprises an emergency work order and a maintenance work order, and the maintenance work order is updated according to the feedback result of the emergency work order;
wherein W3 ═ a × P1 × q + b × P2 × q × (T/T)Maintenance cycle)×w1
Wherein a is the maintenance record coefficient of the key component, b is the maintenance record coefficient of the common component, P1 and P2 are the abnormal frequency of the key component and the common component respectively, and q is the poor evaluation degree of the service equipment.
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| CN116761202A (en) * | 2023-07-06 | 2023-09-15 | 武昌理工学院 | Network stability monitoring maintenance system based on 5G communication technology |
| CN116761202B (en) * | 2023-07-06 | 2024-05-10 | 武昌理工学院 | Network stability monitoring maintenance system based on 5G communication technology |
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