An intelligent equipment management method and system based on the Happy Forest Belt

Abstract

The present invention relates to an intelligent equipment management method and system based on a happy forest belt. By adding monitoring sensors and making full use of the characteristics of WSN, a monitoring network is constructed in combination with an existing WiFi network. The transmission mode and transmission path are planned in combination with the actual power supply and network status, 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 the equipment abnormality, maintenance information and evaluation, thereby improving the accuracy of the work order.

Description

Technical Field

[0001] The invention relates to an intelligent equipment management method and system based on a happy forest belt. Background Art

[0002] In the Xingfu Forest Belt, hundreds of thousands of subsystem devices are running, and the connections between them are complex. Timely maintenance is required between the equipment systems to ensure reliable services. Discovering abnormal values ​​in the systems and devices, quickly determining the abnormal type and locating abnormal devices are issues that must be addressed.

[0003] However, existing devices generally perform self-inspection, early warning and transmission based on their own detection programs through the existing connected network. Once the device power supply or network is abnormal, it will seriously affect the self-inspection program and data transmission. The current early warning technology is mainly based on the device's own information for detection, which cannot take into account the network, external environment, and user needs, and thus cannot guarantee the accuracy and reliability of device abnormality detection.

[0004] Currently, in terms of exception handling, manual maintenance work orders are usually issued directly and personnel are assigned to handle them. At this time, the accuracy of the manual maintenance work orders cannot be guaranteed. Once the manual maintenance work order information is wrong, the assigned personnel cannot solve the problem, which will lead to a serious reduction in the efficiency of exception handling, affect the user experience, and be detrimental to the promotion of services. Summary of the invention

[0005] In view of the problems existing in the prior art, the present application provides an intelligent equipment management method based on the Happy Forest Belt, characterized in that the management method includes:

[0006] Networking steps: adding monitoring sensors to each service device; the sensors select a data transmission mode according to the power supply status and network status of the service device; the sensors select a path according to the transmission cost; wherein the sensors communicate based on a WiFi network and / or a Zigbee network;

[0007] Monitoring step: The sensor monitors the service equipment according to the signal strength RSSI and data real-time index T 实时 , equipment operation time T, weather status influence coefficient m, historical status evaluation degree p determine the equipment abnormality weight W1, and determine whether to issue an early warning based on the value of W1;

[0008] Abnormal maintenance steps: Perform abnormal evaluation based on whether abnormal information indicating early warning is received, and determine the work order type based on the evaluation results.

[0009] The sensor selects the data transmission mode according to the power supply status and network status of the service device, including:

[0010] When the service equipment is stably powered by the mains electricity, the wifi network transmission is selected;

[0011] When the service device has no mains power supply, the zigbee network transmission is selected;

[0012] When the city power supply is unstable, the network is selected for transmission according to the network weight W2;

[0013] Wherein, W2=(Tz / Tw)×(RSSIw / RSSIz)×(Kw / Kz); Tz and Tw are the network delays of ZigBee network and WiFi network respectively; RSSIw and RSSIz are the signal strengths of WiFi network and ZigBee network respectively; Kw and Kz are the reliability parameters of WiFi network and ZigBee network respectively.

[0014] The sensor selecting a path according to the transmission cost includes: determining a transmission cost C between two sensors, and determining a path transmission cost W based on the transmission cost C;

[0015] Where W = ∑C;

[0016] C = RSSI / d×ln(R)+RSSI / d×ln(S); where R is the signal strength coefficient, S is the signal-to-noise ratio (SNR) coefficient, and d is the distance between sensors.

[0017] Where W1 = m × p × (RSSI / RSSI max +T 实时 / T 网络理想值 +T / T 维护周期 )

[0018] T 网络理想值 Minimum network delay; T 维护周期 Indicates the maintenance cycle of the equipment; RSSI max is the maximum signal strength.

[0019] Among them, when the warning information is received, a maintenance work order is generated according to the evaluation result W3 of the warning information, and the maintenance work order includes an emergency work order and a repair work order, and the repair work order is updated according to the feedback result of the emergency work order;

[0020] Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 )×W1

[0021] 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 frequencies of key components and common components respectively, and q is the poor evaluation degree of the service equipment.

[0022] The present invention also provides an intelligent equipment management system, the management system comprising:

[0023] Networking module: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply status and network status of the service device; the sensor selects a path according to the transmission cost; wherein the sensor communicates based on a WiFi network and / or a Zigbee network;

[0024] Monitoring module: The sensor monitors the service equipment based on the signal strength RSSI and data real-time index T 实时 , equipment operation time T, weather status influence coefficient m, historical status evaluation degree p determine the equipment abnormality weight W1, and determine whether to issue an early warning based on the value of W1;

[0025] Exception maintenance module: Perform exception assessment based on whether exception information is received, and determine the work order type based on the assessment results.

[0026] The beneficial effects of the present invention are that the intelligent equipment management method and system based on the Happy Forest Belt involved in the present invention, by adding monitoring sensors, fully utilizing the characteristics of WSN, building a monitoring network in combination with the existing WiFi network, and planning the transmission mode and transmission path in combination with the actual power supply and network status, thereby improving the reliability of monitoring and the stability of data transmission; the present invention also realizes abnormal monitoring of equipment through comprehensive consideration of the network, equipment, users and environment, thereby improving the accuracy and reliability of monitoring; the present invention also constructs a work order trigger indication through comprehensive consideration of equipment abnormality, maintenance information and evaluation, thereby improving the accuracy of the work order. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0028] Figure 1 is a method flow chart of a preferred embodiment of the present invention;

[0029] Figure 2 It is a system schematic diagram of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0030] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0031] In the Xingfu Forest Belt, there are various service equipment, and most of them need to be powered by AC power. However, due to long-term use, untimely maintenance, network abnormalities and other reasons, not only are the equipment prone to various problems, but sometimes it is also impossible to obtain effective monitoring data. Therefore, an effective equipment management solution is needed.

[0032] like Figure 1 As shown, the present invention provides an intelligent equipment management method based on the Happy Forest Belt, and the management method includes:

[0033] Networking steps: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply status and network status of the service device; the sensor selects a path according to the transmission cost; wherein the sensor communicates based on a WiFi network and / or a Zigbee network; on the basis of the existing network, add a wireless sensor network, and the wireless sensor network communicates based on Zigbee, thereby preventing the problem of failure to provide timely warnings when the service device or network is abnormal.

[0034] Monitoring step: The sensor monitors the service equipment according to the signal strength RSSI and data real-time index T 实时 , equipment operation time T, weather status influence coefficient m, historical status evaluation degree p determine the equipment abnormality weight W1, and determine whether to issue an early warning based on the value of W1;

[0035] W1=m×p×(RSSI / RSSI max +T 实时 / T 网络理想值 +T / T 维护周期 )

[0036] T 网络理想值 Minimum network delay; T 维护周期 Indicates the maintenance cycle of the equipment; RSSI max is the maximum signal strength. The data real-time index can be the time required for data transmission.

[0037] m=((E-Ee) / Ee)+((F-Fe) / Fe)+((G-Ge) / Ge); E is the ambient temperature, F is the humidity, and G is the light intensity; Ee can select the lowest temperature of the current season according to the season; Fe can select the lowest humidity of the current season according to the season; Ge represents the light rating, and for example, the light intensity when the sun sets can be selected.

[0038] Abnormal maintenance steps: Perform abnormal evaluation based on whether abnormal information indicating early warning is received, and determine the work order type based on the evaluation results.

[0039] When providing services, the service equipment 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 when monitoring the equipment. Since data transmission is affected by the network environment and external factors, the present invention takes the above factors into consideration when monitoring the equipment, rather than just focusing on the equipment itself. This can more effectively provide timely warnings of the status of the equipment, while ensuring the reliability and timeliness of the warning transmission.

[0040] After adding WSN, the service equipment can at least transmit data through WiFi and Zigbee networks, so there are multiple options for data transmission. In order to effectively transmit data, the specific transmission mode needs to be set.

[0041] The sensor selects the data transmission mode according to the power supply status and network status of the service device, including:

[0042] When the service equipment is stably powered by the mains electricity, the wifi network transmission is selected;

[0043] When the service device has no mains power supply, the zigbee network transmission is selected;

[0044] When the city power supply is unstable, the network is selected for transmission according to the network weight W2;

[0045] Wherein, W2=(Tz / Tw)×(RSSIw / RSSIz)×(Kw / Kz); Tz and Tw are the network delays of ZigBee network and WiFi network respectively; RSSIw and RSSIz are the signal strengths of WiFi network and ZigBee network respectively; Kw and Kz are the reliability parameters of WiFi network and ZigBee network respectively.

[0046] When W2 is greater than the first threshold, select wifi, and when it is less than the second threshold, select zigbee. Otherwise, select a hybrid network for transmission. The threshold setting can be based on the historical weight and its corresponding network status. For example, 1 / 3 and 2 / 3 of the historical weight average are set as the first and second thresholds. After completing the network selection, select the appropriate path by calculating the transmission cost. For example, when selecting WiFi, calculate the cost between each node in the WiFi network; when selecting Zigbee, calculate the cost between each node in the Zigbee network; when selecting a hybrid network, it is necessary to calculate the transmission cost of each network connection.

[0047] The sensor selecting a path according to the transmission cost includes: determining a transmission cost C between two sensors, and determining a path transmission cost W based on the transmission cost C;

[0048] Wherein, W=∑C; preferably, by calculating the cost between two nodes on each path, the transmission cost W of the path is determined by the sum of the cost between each two nodes, and the path with the minimum path cost is selected for transmission;

[0049] C = RSSI / d×ln(R)+RSSI / d×ln(S); where R is the signal strength coefficient, S is the signal-to-noise ratio (SNR) coefficient, and d is the distance between sensors.

[0050] Where R = (RSSI-RSSIth) / (RSSImax-RSSIth)

[0051] RSSIth is the received signal strength threshold, and the specific value can be set according to the historical average of the network environment;

[0052] S=(SNR-SNRth) / (SNRmax-SNRth)

[0053] SNR is the current signal-to-noise ratio; SNRth is the signal-to-noise ratio threshold value, and the specific value can be set according to the historical average 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.

[0054] By considering the network transmission mode and transmission cost, the efficiency of network data transmission can be improved, ensuring that the monitoring data can be delivered to the network end in a timely manner. By setting the transmission cost, the reliability of data transmission can be guaranteed.

[0055] Among them, when the warning information is received, a maintenance work order is generated according to the evaluation result W3 of the warning information, and the maintenance work order includes an emergency work order and a repair work order, and the repair work order is updated according to the feedback result of the emergency work order;

[0056] Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 )×W1

[0057] Wherein, a is the maintenance record coefficient of key components (when there is a problem with key components, a=1, otherwise, a=0), b is the maintenance record coefficient of common components (when there is a problem with common components, a=1, otherwise, a=0); P1 and P2 are the abnormal frequencies of key components and common components respectively, and q is the poor evaluation degree of the service equipment.

[0058] By evaluating the equipment maintenance history and evaluation considerations for abnormalities, it is determined whether to enter the late stage of maintenance and generate maintenance work orders, which are then determined based on emergency work orders. This can improve the accuracy and reliability of maintenance work orders.

[0059] When no abnormal information is received, W3 is calculated periodically, and it is determined whether it is necessary to enter the maintenance cycle and perform maintenance on the service equipment according to W3;

[0060] Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 );

[0061] In the prior art, work orders are directly generated based on exception information. However, the actual exception cannot be accurately judged based on the exception information. Therefore, the present invention adds an emergency work order when setting the work order. The emergency work order is confirmed by contacting the staff at the service equipment site for remote information confirmation, and then a maintenance work order is generated based on the confirmation information. This improves the accuracy of the maintenance work order and achieves accurate positioning of maintenance personnel. At the same time, it can reduce the attendance frequency of maintenance personnel and improve work efficiency.

[0062] The present invention also provides an intelligent equipment management system based on the Happy Forest Belt, such as Figure 2 As shown, the management system includes:

[0063] Networking module: add monitoring sensors to each service device; the sensors select data transmission mode according to the power supply status and network status of the service device; the sensors select paths according to the transmission cost; wherein the sensors communicate based on WiFi network and / or Zigbee network; wherein the networking module is located at the service device and the network side server;

[0064] Monitoring module: The sensor monitors the service equipment, determines the equipment abnormality weight W1 according to the signal strength RSSI, data real-time index Treal-time, equipment running time T, weather status influence coefficient m, and historical status evaluation degree p, and determines whether to issue an early warning according to the value of W1; wherein, the monitoring module is located at the service equipment and the network side server;

[0065] W1=m×p×(RSSI / RSSI max +T 实时 / T 网络理想值 +T / T 维护周期 )

[0066] T 网络理想值 The minimum network delay; T maintenance cycle indicates the maintenance cycle of the device; RSSI max is the maximum signal strength.

[0067] Abnormal maintenance module: performs abnormal evaluation based on whether abnormal information is received, and determines the work order type based on the evaluation result. The abnormal maintenance module is located on the network side server.

[0068] The sensor selects the data transmission mode according to the power supply status and network status of the service device, including:

[0069] When the service equipment is stably powered by the mains electricity, the wifi network transmission is selected;

[0070] When the service device has no mains power supply, the zigbee network transmission is selected;

[0071] When the city power supply is unstable, the network is selected for transmission according to the network weight W2;

[0072] Wherein, W2=(Tz / Tw)(RSSIw / RSSIz)(Kw / Kz); Tz and Tw are the network delays of ZigBee network and WiFi network respectively; RSSIw and RSSIz are the signal strengths of WiFi network and ZigBee network respectively; Kw and Kz are the reliability parameters of WiFi network and ZigBee network respectively.

[0073] The sensor selecting a path according to the transmission cost includes: determining a transmission cost C between two sensors, and determining a path transmission cost W based on the transmission cost C;

[0074] Where W = ∑C; preferably, the path with the minimum cost is selected for transmission;

[0075] C = RSSI / d×ln(R)+RSSI / d×ln(S); where R is the signal strength coefficient, S is the signal-to-noise ratio (SNR) coefficient, and d is the distance between sensors.

[0076] Among them, when the abnormal maintenance module receives the warning information, it generates a maintenance work order according to the evaluation result W3 of the warning information. The maintenance work order includes an emergency work order and a repair work order. The repair work order is updated according to the feedback result of the emergency work order;

[0077] Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 )×W1

[0078] 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 frequencies of key components and common components respectively, and q is the poor evaluation degree of the service equipment.

[0079] In the prior art, work orders are directly generated based on exception information. However, the actual exception cannot be accurately judged based on the exception information. Therefore, the present invention adds an emergency work order when setting the work order. The emergency work order is confirmed by contacting the staff at the service equipment site for remote information confirmation, and then a maintenance work order is generated based on the confirmation information. This improves the accuracy of the maintenance work order and achieves accurate positioning of maintenance personnel. At the same time, it can reduce the attendance frequency of maintenance personnel and improve work efficiency.

[0080] The intelligent equipment management method and system involved in the present invention, by adding monitoring sensors, fully utilizing the characteristics of WSN, combining with the existing WiFi network to build a monitoring network, and planning the transmission mode and transmission path in combination with the actual power supply and network status, thereby improving the reliability of monitoring and the stability of data transmission; the present invention also realizes abnormal monitoring of equipment by comprehensive consideration of 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 comprehensive consideration of equipment abnormality, maintenance information and evaluation, thereby improving the accuracy of the work order.

[0081] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. An intelligent equipment management method based on the Happy Forest Belt, characterized in that: The management method comprises: Networking steps: adding monitoring sensors to each service device; the sensors select a data transmission mode according to the power supply status and network status of the service device; the sensors select a path according to the transmission cost; wherein the sensors communicate based on a WiFi network and / or a Zigbee network; Monitoring step: The sensor monitors the service equipment according to the signal strength RSSI and data real-time index T 实时 , equipment operation time T, weather status influence coefficient m, historical status evaluation degree p determine the equipment abnormality weight W1, and determine whether to issue an early warning based on the value of W1; Abnormal maintenance steps: perform abnormal evaluation based on whether abnormal information indicating early warning is received, and determine the work order type based on the evaluation results; The sensor selects a data transmission mode according to the power supply status and network status of the service device, including: When the service equipment is stably powered by the mains electricity, the wifi network transmission is selected; When the service device has no mains power supply, the zigbee network transmission is selected; When the city power supply is unstable, the network is selected for transmission according to the network weight W2; Wherein, W2=(Tz / Tw)×(RSSIw / RSSIz)×(Kw / Kz); Tz and Tw are the network delays of Zigbee network and WiFi network respectively; RSSIw and RSSIz are the signal strengths of WiFi network and ZigBee network respectively; Kw and Kz are the reliability parameters of WiFi network and ZigBee network respectively; The sensor selecting a path according to the transmission cost includes: determining a transmission cost C between two sensors, and determining a path transmission cost W based on the transmission cost C; Where W = ∑C; C = RSSI / d×ln(R)+RSSI / d×ln(S); where R is the signal strength coefficient, S is the signal-to-noise ratio (SNR) coefficient, and d is the distance between sensors.

2. The method according to claim 1, characterized in that The method comprises: W1=m×p×(RSSI / RSSI max +T 实时 / T 网络理想值 +T / T 维护周期 ) T 网络理想值 Minimum network delay; T 维护周期 Indicates the maintenance cycle of the equipment; RSSI max is the maximum signal strength.

3. The method according to claim 1, characterized in that The method includes: after receiving the early warning information, generating a maintenance work order according to the evaluation result W3 of the early warning information, wherein the maintenance work order includes an emergency work order and a repair work order, and the repair work order is updated according to the feedback result of the emergency work order; Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 )×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 frequencies of key components and common components respectively, q is the poor evaluation degree of service equipment, T 维护周期 Indicates the maintenance period of the equipment.

4. An intelligent equipment management system based on the Happy Forest Belt, characterized in that: The management system comprises: Networking module: add monitoring sensors to each service device; the sensor selects a data transmission mode according to the power supply status and network status of the service device; the sensor selects a path according to the transmission cost; wherein the sensor communicates based on a WiFi network and / or a Zigbee network; Monitoring module: The sensor monitors the service equipment based on the signal strength RSSI and data real-time index T 实时 , equipment operation time T, weather status influence coefficient m, historical status evaluation degree p determine the equipment abnormality weight W1, and determine whether to issue an early warning based on the value of W1; Abnormal maintenance module: conduct abnormal evaluation based on whether abnormal information indicating early warning is received, and determine the work order type based on the evaluation results; The sensor selects a data transmission mode according to the power supply status and network status of the service device, including: When the service equipment is stably powered by the mains electricity, the wifi network transmission is selected; When the service device has no mains power supply, the zigbee network transmission is selected; When the city power supply is unstable, the network is selected for transmission according to the network weight W2; Wherein, W2=(Tz / Tw)×(RSSIw / RSSIz)×(Kw / Kz); Tz and Tw are the network delays of Zigbee network and WiFi network respectively; RSSIw and RSSIz are the signal strengths of WiFi network and ZigBee network respectively; Kw and Kz are the reliability parameters of WiFi network and ZigBee network respectively; The sensor selecting a path according to the transmission cost includes: determining a transmission cost C between two sensors, and determining a path transmission cost W based on the transmission cost C; Where W = ∑C; C = RSSI / d×ln(R)+RSSI / d×ln(S); where R is the signal strength coefficient, S is the signal-to-noise ratio (SNR) coefficient, and d is the distance between sensors.

5. The system according to claim 4, characterized in that W1=m×p×(RSSI / RSSI max +T 实时 / T 网络理想值 +T / T 维护周期 ) T 网络理想值 Minimum network delay; T 维护周期 Indicates the maintenance cycle of the equipment; RSSI max is the maximum signal strength.

6. The system according to claim 4, characterized in that When the abnormal maintenance module receives the warning information, it generates a maintenance work order according to the evaluation result W3 of the warning information. The maintenance work order includes an emergency work order and a repair work order. The repair work order is updated according to the feedback result of the emergency work order. Where W3=(a×P1×q+b×P2×q)×(T / T 维护周期 )×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 frequencies of key components and common components respectively, q is the poor evaluation degree of service equipment, T 维护周期 Indicates the maintenance period of the equipment.

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