CN114297018A - Intelligent equipment management method and system based on Happy forest zone - Google Patents

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

A kind of intelligent equipment management method and system based on happy forest belt

technical field

The invention relates to an intelligent equipment management method and system based on a happy forest belt.

Background technique

In the happy forest belt, hundreds of thousands of sub-system devices operate and are complexly related to each other. Equipment systems need to be maintained in a timely manner to ensure reliable services. Finding abnormal values in systems and equipment, quickly judging the type of abnormality and locating abnormal equipment are problems that must be solved.

However, the existing equipment is generally based on the detection program carried by itself, and carries out self-inspection, early warning and transmission through the existing connected network; once the power supply of the equipment or the network is abnormal, the self-inspection program and data transmission will be seriously affected; the current early warning technology mainly Detection is based on the device's own information, and cannot take into account the needs of the network, external environment, and users, so the accuracy and reliability of device anomaly detection cannot be guaranteed. At present, in terms of exception handling, the manual maintenance work order is usually issued directly, and the personnel are assigned to handle it. At this time, the accuracy of the manual work order issuance cannot be guaranteed. Once the manual maintenance work order information is wrong, the assigned personnel cannot solve the problem. This will seriously reduce the efficiency of exception handling, and also affect the user experience, which is not conducive to the promotion of services.

SUMMARY OF THE INVENTION

In view of the existing problems in the prior art, the application provides an intelligent device management method based on a happy forest belt, wherein the management method includes:

Networking steps: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply state and network state of the service device; the sensor selects a path according to the transmission cost; wherein, the sensor is based on WiFi network and, or Zigbee network to communicate;

Monitoring step: the sensor realizes monitoring of the service equipment, and determines the equipment abnormality weight W1 according to the signal strength RSSI, the data real-time index T _real-time , the equipment running time T, the weather state influence coefficient m, and the historical state evaluation favorable degree p, and according to W1 The value of determines whether to carry out an early warning;

Abnormal maintenance steps: carry out abnormal evaluation according to whether the abnormal information indicating the warning is received, and determine the work order type according to the evaluation result.

The selection of the data transmission mode by the sensor according to the power supply status and network status of the service device includes:

When the mains supply of the service equipment is stable, select wifi network transmission;

When the service equipment has no mains power supply, select zigbee network transmission;

When the mains power is unstable, the network is selected for transmission according to the network weight W2;

Among them, W2=(Tz/Tw)×(RSSIw/RSSIz)×(Kw/Kz); Tz and Tw are the network delay of Zigbee network and WiFi network respectively; RSSIw and RSSIz are the signal strength of WiFi network and ZigBee network respectively ; Kw, Kz are the reliability parameters of WiFi network and ZigBee network respectively.

The selection of the path by the sensor according to the transmission cost includes: determining the transmission cost C between the two sensors, and determining the path transmission cost W based on the transmission cost C;

Among them, W=∑C;

C=RSSI/d×ln(R)+RSSI/d×ln(S); wherein, R is the signal strength coefficient, S is the signal-to-noise ratio SNR coefficient; d is the distance between the sensors.

Among them, W1=m×p×(RSSI/RSSI _max +T _real-time /T _{network ideal value} +T/T _{maintenance cycle} )

The _{ideal value of T network is} the minimum value of network delay; T _{maintenance cycle} represents the maintenance cycle of the device; RSSI _max is the maximum value of signal strength.

Wherein, after receiving the early warning information, a maintenance work order is generated according to the evaluation result W3 of the early warning information, and the maintenance work order includes 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;

Among them, W3=(a×p1×q+b×P2×q)×(T/T _{maintenance cycle} )×w1

Among them, a is the maintenance record coefficient of key components, b is the maintenance record coefficient of common components, P1 and P2 are the abnormal frequency of key components and common components, respectively, and q is the bad evaluation rating of service equipment.

The present invention also provides an intelligent equipment management system, the management system includes:

Networking module: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply state and network state of the service device; the sensor selects the path according to the transmission cost; wherein, the sensor is based on WiFi network and, or Zigbee network to communicate;

Monitoring module: the sensor realizes monitoring of service equipment, and determines the equipment abnormality weight W1 according to the signal strength RSSI, the data real-time index T _real-time , the equipment running time T, the weather state influence coefficient m, and the historical state evaluation favorable degree p, according to W1 The value of determines whether to carry out an early warning;

Abnormal maintenance module: According to whether abnormal information is received, abnormal evaluation is performed, and the type of work order is determined according to the evaluation result.

The beneficial effect of the present invention is that the intelligent device management method and system based on the happy forest belt involved in the present invention can make full use of the characteristics of WSN by adding monitoring sensors, build a monitoring network in combination with the existing WiFi network, and combine the actual power supply, network The transmission mode and transmission path are planned according to the state, thereby improving the reliability of monitoring and the stability of data transmission; the present invention also realizes abnormal monitoring of equipment by comprehensively considering the network, equipment, users and environment, thereby improving the Accuracy and reliability of monitoring; the present invention also constructs a work order trigger indication by comprehensively considering equipment abnormality, maintenance information and evaluation, thereby improving the accuracy of the work order.

Description of drawings

The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

Fig. 1 is the method flow chart of the preferred embodiment of the present invention;

Figure 2 is a system schematic diagram of a preferred embodiment of the present invention.

Detailed ways

The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are all simplified schematic diagrams, and only illustrate the basic structure of the present invention in a schematic manner, so they only show the structures related to the present invention.

In the happy forest belt, there are various service equipment, and most of them require mains power supply. However, due to long-term use, untimely maintenance, abnormal network and other reasons, not only the equipment is prone to various problems, sometimes It is also impossible to obtain effective monitoring data. Therefore, an effective device management solution is required.

As shown in Figure 1, the present invention provides an intelligent device management method based on a happy forest belt, and the management method includes:

Networking steps: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply state and network state of the service device; the sensor selects a path according to the transmission cost; wherein, the sensor is based on WiFi network and, or Zigbee network for communication; on the basis of the existing network, a wireless sensor network is added, and the wireless sensor network communicates based on Zigbee, thereby preventing the problem that the service equipment or network is abnormal and cannot be warned in time.

Monitoring step: the sensor realizes monitoring of the service equipment, and determines the equipment abnormality weight W1 according to the signal strength RSSI, the data real-time index T _real-time , the equipment running time T, the weather state influence coefficient m, and the historical state evaluation favorable degree p, and according to W1 The value of determines whether to carry out an early warning;

W1=m×p×(RSSI/RSSI _max +T _real-time /T _{network ideal value} +T/T _{maintenance cycle} )

The _{ideal value of T network is} the minimum value of network delay; T _{maintenance cycle} represents the maintenance cycle of the device; RSSI _max is the maximum value of signal strength. The data real-time index can be the time required for data transmission.

m=((E-Ee)/Ee)+((F-Fe)/Fe)+((G-Ge)/Ge); E is ambient temperature, F is humidity, G is light intensity; Ee can be In the season, select the lowest temperature in the current season; Fe can choose the lowest humidity in the current season according to the season; Ge represents the light rating, and the light intensity when the sun sets can be selected as an example.

Abnormal maintenance steps: carry out abnormal evaluation according to whether the abnormal information indicating the warning is received, and determine the work order type according to the evaluation result.

When the service equipment provides services, it is not only affected by the operation of the equipment itself, but also has a direct relationship with the surrounding environment and user feedback. Therefore, the above factors need to be considered during equipment monitoring. Therefore, the present invention comprehensively considers the above factors when monitoring the equipment, instead of only focusing on the equipment itself, so that it can more effectively give a timely early warning to the status of the equipment, and at the same time ensure the reliability of early warning transmission. and timeliness.

After the WSN is added, the service device can at least transmit data through WiFi and Zigbee networks, so there are multiple options for data transmission. In order to effectively transmit data, a specific transmission mode needs to be set.

The selection of the data transmission mode by the sensor according to the power supply status and network status of the service device includes:

When the mains supply of the service equipment is stable, select wifi network transmission;

When the service equipment has no mains power supply, select zigbee network transmission;

When the mains power is unstable, the network is selected for transmission according to the network weight W2;

Among them, W2=(Tz/Tw)×(RSSIw/RSSIz)×(Kw/Kz); Tz and Tw are the network delay of Zigbee network and WiFi network respectively; RSSIw and RSSIz are the signal strength of WiFi network and ZigBee network respectively ; Kw, Kz are the reliability parameters of WiFi network and ZigBee network respectively.

When W2 is greater than the first threshold, select wifi, when it is less than the second threshold, select zigbee, otherwise select the hybrid network for transmission, where the threshold setting can be selected according to the historical weight and its corresponding network state for transmission, such as setting the historical weight 1/3 of the mean, 2/3 as the first and second thresholds. After completing the selection of the network, select the appropriate path by calculating the transmission cost. For example, when selecting WiFi, calculate the cost between nodes in the WiFi network; when selecting Zigbee, calculate the cost between nodes in the Zigbee network; when selecting When mixing networks, it is necessary to calculate the transmission cost of each network connection.

The selection of the path by the sensor according to the transmission cost includes: determining the transmission cost C between the two sensors, and determining the path transmission cost W based on the transmission cost C;

Wherein, W=∑C; preferably, by calculating the cost before two nodes on each path, and determining the transmission cost W of the path by summing the cost between each two nodes, select the path with the smallest path cost for transmission;

C=RSSI/d×ln(R)+RSSI/d×ln(S); wherein, R is the signal strength coefficient, S is the signal-to-noise ratio SNR coefficient; d is the distance between the sensors.

Among them, R=(RSSI-RSSIth)/(RSSImax-RSSIth)

RSSIth is the received signal strength threshold, the specific value can be set according to the historical average value of the network environment;

S=(SNR-SNRth)/(SNRmax-SNRth)

SNR is the current signal-to-noise ratio. SNRth is the signal-to-noise ratio threshold. The specific value can be set according to the historical average value of the network environment. SNRmax is the ideal value of the signal-to-noise ratio, which refers to the SNR value when the network is in the best environment.

By considering the network transmission mode and transmission cost, the efficiency of network data transmission can be improved, and the monitoring data can be transmitted to the network in time. By setting the transmission cost, the reliability of data transmission can be guaranteed.

Wherein, after receiving the early warning information, a maintenance work order is generated according to the evaluation result W3 of the early warning information, and the maintenance work order includes 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;

Among them, W3=(a×p1×q+b×P2×q)×(T/T _{maintenance cycle} )×W1

Among them, a is the maintenance record coefficient of key components (when there is a problem with the key component, a=1, when there is no problem, a=0), b is the maintenance record coefficient of the common component (when there is a problem with the common component, a=1, If not, a=0); P1 and P2 are the abnormal frequencies of key components and common components, respectively, and q is the bad evaluation rating of the service equipment.

By taking into account the equipment maintenance history and evaluation, abnormal evaluation is performed to determine whether to enter the later stage of maintenance, and a maintenance work order is generated, and then the maintenance work order is determined according to the emergency work order, thus the accuracy and reliability of the maintenance work order can be improved.

When no abnormal information is received, W3 is calculated periodically, and according to W3, it is determined whether it is necessary to enter the maintenance cycle to maintain the service equipment;

Among them, W3=(a×p1×q+b×P2×q)×(T/T _{maintenance cycle} );

In the prior art, work orders are directly generated according to the abnormal information. However, the actual abnormality cannot be accurately determined according to the abnormal information. Therefore, the present invention adds emergency work orders when setting the work orders, and the emergency work orders are contacted by the service equipment. The on-site staff confirms the information remotely, and then regenerates the maintenance work order according to the confirmation information, thereby improving the accuracy of the maintenance work order, realizing the accurate positioning of the maintenance staff, reducing the attendance frequency of the maintenance staff and improving the work efficiency.

The present invention also provides an intelligent equipment management system based on the happy forest belt, as shown in Figure 2, the management system includes:

Networking module: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply state and network state of the service device; the sensor selects the path according to the transmission cost; wherein, the sensor is based on WiFi network and, or Zigbee network for communication; wherein, the networking module is located at the service device and the network side server;

Monitoring module: the sensor realizes monitoring of the service equipment, and determines the equipment abnormality weight W1 according to the signal strength RSSI, the data real-time index T real-time, the equipment running time T, the weather state influence coefficient m, and the historical state evaluation favorable degree p, and according to W1 The value of is to determine whether to carry out an early warning; wherein, the monitoring module is located at the service equipment and the network side server;

W1=m×p×(RSSI/RSSI _max +T _real-time /T _{network ideal value} +T/T _{maintenance cycle} )

The _{ideal value of T network is} the minimum value of network delay; T maintenance cycle represents the maintenance cycle of the device; RSSI _max is the maximum value of signal strength.

Abnormal maintenance module: According to whether abnormal information is received, abnormal evaluation is performed, and the type of work order is determined according to the evaluation result. The abnormal maintenance module is located on the network side server.

The selection of the data transmission mode by the sensor according to the power supply status and network status of the service device includes:

When the mains supply of the service equipment is stable, select wifi network transmission;

When the service equipment has no mains power supply, select zigbee network transmission;

When the mains power is unstable, the network is selected for transmission according to the network weight W2;

Among them, W2=(Tz/Tw)(RSSIw/RSSIz)(Kw/Kz); Tz and Tw are the network delay of Zigbee network and WiFi network respectively; RSSIw and RSSIz are the signal strength of WiFi network and ZigBee network respectively; Kw , Kz are the reliability parameters of WiFi network and ZigBee network, respectively.

The selection of the path by the sensor according to the transmission cost includes: determining the transmission cost C between the two sensors, and determining the path transmission cost W based on the transmission cost C;

Wherein, W=∑C; preferably, the path with the least cost is selected for transmission;

C=RSSI/d×ln(R)+RSSI/d×ln(S); wherein, R is the signal strength coefficient, S is the signal-to-noise ratio SNR coefficient; d is the distance between the sensors.

Wherein, after the abnormal maintenance module receives the warning information, it generates a maintenance work order according to the evaluation result W3 of the early warning information. The maintenance work order includes an emergency work order and a repair work order, and the maintenance work order is updated according to the feedback result of the emergency work order. ;

Among them, W3=(a×p1×q+b×P2×q)×(T/T _{maintenance cycle} )×w1

Among them, a is the maintenance record coefficient of key components, b is the maintenance record coefficient of common components, P1 and P2 are the abnormal frequency of key components and common components, respectively, and q is the bad evaluation rating of service equipment.

In the prior art, work orders are directly generated according to the abnormal information. However, the actual abnormality cannot be accurately determined according to the abnormal information. Therefore, the present invention adds emergency work orders when setting the work orders, and the emergency work orders are contacted by the service equipment. The on-site staff confirms the information remotely, and then regenerates the maintenance work order according to the confirmation information, thereby improving the accuracy of the maintenance work order, realizing the accurate positioning of the maintenance staff, reducing the attendance frequency of the maintenance staff and improving the work efficiency.

The intelligent device management method and system involved in the present invention make full use of the characteristics of WSN by adding monitoring sensors, construct a monitoring network in combination with the existing WiFi network, and plan the transmission mode and transmission path in combination with the actual power supply and network status. This improves the reliability of monitoring and the stability of data transmission; the present invention also realizes abnormal monitoring of equipment by comprehensively considering the network, equipment, users and the environment, thereby improving the accuracy and reliability of monitoring; the present invention also The work order trigger indication is constructed by comprehensively considering the equipment abnormality, maintenance information and evaluation, thereby improving the accuracy of the work order.

Taking the above ideal embodiments according to the present invention as inspiration, and through the above description, relevant personnel can make various changes and modifications without departing from the technical idea of the present invention. The technical scope of the present invention is not limited to the contents in the specification, and the technical scope 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 RSSI_{Real time}Determining 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/RSSI_max+T_{real time}/T_{Network ideal value}+T/T_{Maintenance cycle})

T_{Network ideal value}Minimum value of network delay; t is_{Maintenance cycle}Indicating a maintenance cycle of the equipment; RSSI_maxIs 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 RSSI_{Real time}Determining 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/RSSI_max+T_{real time}/T_{Network ideal value}+T/T_{Maintenance cycle})

T_{Network ideal value}Minimum value of network delay; t is_{Maintenance cycle}Indicating a maintenance cycle of the equipment; RSSI_maxIs 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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