CN114640591A - Landscape engineering intelligent management method and system - Google Patents
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Abstract
The invention relates to a garden landscape engineering intelligent management method and a system, which realize the monitoring of vegetation, equipment and personnel in gardens through the cooperation of a networking subsystem, a sub-monitoring system and a data center, realize the monitoring of various indexes through a monitoring system, and realize intelligent identification, positioning, tracking, monitoring and management through the Internet of things technology; therefore, the unified management of garden implants, equipment and personnel can be realized, and abnormal information can be found in time; moreover, abundant, real-time and accurate garden data can be fully utilized, personnel configuration of maintenance engineering is realized, and the method is helpful for scientifically guiding and supervising integral deployment and implementation of garden greening planning and construction and assisting leaders in scientific decision-making.
Description
Technical Field
The invention relates to an intelligent management method and system for landscape architecture engineering.
Background
Along with the rapid development of the urbanization process, the quantity, the area, the density and the like of various greenbelts in cities are continuously increased, and the landscaping becomes an important component of the urban environment, is welfare of the personal interests of each person, and is also an important public space for entertainment and social activities.
The urban landscaping has the growing complexity, and the traditional management mode mainly adopts a manual mode to monitor and maintain; various kinds of information of gardens are difficult to accurately obtain, and information service and fine management cannot be realized; although an automatic scheme has been proposed, the automatic management system is independent of vegetation, personnel and equipment, and cannot be uniformly managed by an independent network and an independent monitoring system; moreover, when exception handling is performed, a reasonable personnel fine management scheme is lacked.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a garden landscape engineering intelligent management method and system, which realize the monitoring of vegetation, equipment and personnel in gardens through the cooperation of a networking subsystem, a sub-monitoring system and a data center, can realize the monitoring of various indexes through the monitoring system, and realize intelligent identification, positioning, tracking, monitoring and management through the technology of Internet of things; moreover, abundant, real-time and accurate garden data can be fully utilized, overall deployment and implementation of garden greening planning and construction are scientifically guided and supervised, and scientific decision-making is assisted by leaders.
A landscape engineering wisdom management system, management system includes:
the networking subsystem is used for monitoring network establishment so as to realize monitoring on plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies and perform early warning according to monitoring results;
the data center is used for determining a network establishment strategy according to the state information of plants, equipment and personnel so that the networking subsystem can monitor network establishment according to the network establishment strategy; and the monitoring subsystem is used for establishing a monitoring strategy and sending the monitoring strategy to the monitoring subsystem for executing the monitoring strategy.
Wherein the monitoring network establishment comprises: respectively acquiring plant information, equipment information and personnel information issued by a data center, and different networking strategies corresponding to the plant information, the equipment information and the personnel information; wherein the networking policy comprises a data transmission policy; according to the difference of the monitored objects, different transmission mode combinations are set, and the reliability and the timeliness of different data transmission are guaranteed through different modes.
Wherein the networking policy comprises: the method comprises the steps that sub monitoring networks are established respectively aiming at plant information, equipment information and personnel information, the sub monitoring networks support the same and different data transmission modes, and each sub monitoring network can be used for realizing data transmission of the other two sub monitoring networks;
the data transmission strategy comprises the following steps:
acquiring monitoring data;
identifying the type of the monitoring data according to the monitoring data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
wherein Q ═ Σ ((M)Human being+MDevice+MVegetation) R); r is the degree of deviation of the abnormality index; mHuman being、MDevice、MVegetationRespectively the influence degrees of the R abnormal index on people, equipment and vegetation;
determining a data transmission mode according to the weight Q of the abnormal data, wherein the data transmission mode comprises sub-monitoring network transmission, cross-sub-monitoring network transmission and redundant transmission of the sub-monitoring network and the cross-sub-monitoring network; the cross-sub monitoring network transmission is to merge three sub monitoring networks into one integrated monitoring network, and an abnormal data transmission path is set by using equipment in the integrated monitoring network so as to realize the transmission of abnormal data.
The data center is also used for maintaining according to the received information; the maintenance includes:
determining a monitoring object according to the received information;
determining an abnormal index according to the monitored object;
determining an abnormal weight according to the abnormal index;
generating a maintenance work order according to the abnormal weight;
the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credit degree of the maintenance personnel according to the timeliness and reliability factors of the maintenance work order;
and matching the maintainers according to the credibility of the maintainers.
Wherein the determining of the abnormality weight according to the abnormality index includes:
determining M from the abnormality indexHuman being、MDevice、MVegetationAnd calculating the influence factor U,
wherein, U ═ MHuman being+MDevice+MVegetation)；
Acquiring an influence shadow U' according to the received weight Q,
comparing the consistency of the sigma U and the sigma U';
and when the abnormal weight is consistent with the abnormal weight, generating a maintenance work order.
The invention also provides a garden landscape engineering intelligent management method, which comprises the following steps:
determining a network establishment strategy according to the state information of plants, equipment and personnel so that a networking subsystem carries out monitoring network establishment according to the network establishment strategy;
establishing a monitoring strategy and transmitting the monitoring strategy to a monitoring subsystem to execute the monitoring strategy;
a monitoring network is established to realize the monitoring of plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies, and perform early warning according to monitoring results.
The intelligent management method and the intelligent management system for the garden landscape engineering have the advantages that monitoring on vegetation, equipment and personnel in a garden is achieved through cooperation of the networking subsystem, the sub-monitoring system and the data center, monitoring of various indexes can be achieved through the monitoring system, and intelligent identification, positioning, tracking, monitoring and management are achieved through the technology of the internet of things; therefore, the unified management of garden implants, equipment and personnel can be realized, and abnormal information can be found in time; moreover, abundant, real-time and accurate garden data can be fully utilized, personnel configuration of maintenance engineering is realized, and the method is helpful for scientifically guiding and supervising integral deployment and implementation of garden greening planning and construction and assisting leaders in scientific decision-making.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a system schematic of a preferred embodiment of the present invention;
fig. 2 is a flow chart of a method 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.
The invention provides a garden landscape engineering intelligent management method and system, which realize the monitoring of vegetation, equipment and personnel in gardens through the cooperation of a networking subsystem, a sub-monitoring system and a data center, can realize the monitoring of various indexes through a monitoring system, and realize intelligent identification, positioning, tracking, monitoring and management through the Internet of things technology; moreover, abundant, real-time and accurate garden data can be fully utilized, overall deployment and implementation of garden greening planning and construction are scientifically guided and supervised, and scientific decision-making is assisted by leaders.
The invention provides a garden landscape engineering intelligent management system, as shown in figure 1, the management system comprises:
the networking subsystem is used for monitoring network establishment so as to realize monitoring on plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies and perform early warning according to monitoring results;
the data center is used for determining a network establishment strategy according to the state information of plants, equipment and personnel so that the networking subsystem can monitor network establishment according to the network establishment strategy; and the monitoring subsystem is used for establishing a monitoring strategy and sending the monitoring strategy to the monitoring subsystem to execute the monitoring strategy.
The network construction and the data integration processing are realized based on the technology of the Internet of things, so that a uniform monitoring network is constructed, and the management of plants, equipment and personnel is realized.
Further, the monitoring network establishment includes: respectively acquiring plant information, equipment information and personnel information issued by a data center, and different networking strategies corresponding to the plant information, the equipment information and the personnel information; wherein the networking policy comprises a data transmission policy; according to the difference of the monitored objects, different transmission mode combinations are set, and the reliability and the timeliness of different data transmission are guaranteed through different modes.
Further, the networking policy includes: the method comprises the steps that sub monitoring networks are established respectively aiming at plant information, equipment information and personnel information, the sub monitoring networks support the same and different data transmission modes, and each sub monitoring network can be used for realizing data transmission of the other two sub monitoring networks;
the data transmission strategy comprises the following steps:
acquiring monitoring data;
identifying the type of the monitoring data according to the monitoring data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
wherein Q ═ Σ ((M)Human being+MDevice+MVegetation) R); r is the degree of deviation of the abnormality index (preferably, as referred to herein)The value of the deviation degree refers to the absolute value of the deviation degree, namely, the absolute value of the index is calculated no matter whether the index is higher than or lower than a standard value); mHuman being、MDevice、MVegetationRespectively the influence degrees of the R abnormal indexes on people, equipment and vegetation; the monitoring data comprises data of each index, the offset of each index is determined through comparison of each index, the abnormal index is determined according to the offset, and then the offset of each abnormal index, namely the offset degree of the abnormal index, is obtained; and determining the influence of the index on maintenance personnel, related equipment and plants according to the abnormal index so as to further determine the related influence degree.
Wherein M isHuman beingThe ratio of the number of persons related to the project to the total number of related persons is referred to, for example, the ratio of the number of maintenance persons required by the project to the total number of maintenance persons; mDeviceThe ratio of the number of currently available devices to the number of total devices is referred to; mVegetationIs the ratio of the area occupied by the affected vegetation to the total area occupied by the vegetation in the garden. The uploaded weight data Q includes values of respective influence degrees.
Determining a data transmission mode according to the weight Q of the abnormal data, wherein the data transmission mode comprises sub-monitoring network transmission, cross-sub-monitoring network transmission and redundant transmission of the sub-monitoring network and the cross-sub-monitoring network; the cross-sub monitoring network transmission is to merge three sub monitoring networks into one integrated monitoring network, and an abnormal data transmission path is set by using equipment in the integrated monitoring network so as to realize the transmission of abnormal data.
After the network is built, a multi-level data transmission network is formed; the upper node distributes a transmission time slot for each lower node, and the lower nodes transmit data on the distributed transmission time slots;
selecting a proper path according to the type of data in the data transmission process; specifically, the method includes respectively obtaining node information on a path, where the node information includes: the total number n of nodes, the Cost between nodes before each adjacent node, the distance D, the network reliability K between two nodes, and the time delay Tdelay(ii) a Calculating the weight W of each path according to the node information, wherein the weight W is a sigma Cost + b sigma Tdelay；
Wherein a ═ ln (n) ((K-K))avg)/(Kavg-Kmin)；
b＝ln(n)*(D-Davg)/(Davg-Dmin)；
Wherein Kavg is the average value of the network reliability between every two nodes on the path; kmin is the minimum value of the network reliability between every two nodes on the path; davg is the average value of the distance between every two nodes on the path; dmin is the minimum value of the distance between every two nodes on the path; n is the total number of nodes on the path. Wherein the time delay TdelayThe length of time that data needs to wait on the node; the node reliability K uses a fault tolerance representation of the network.
In the data transmission process, when the sub-monitoring network path is selected, a reliability path and/or a timeliness path are/is selected according to the type of abnormal data;
when the reliability path is selected, calculating W1 ═ a ═ Sigma Cost, and selecting a plurality of first candidate paths with W1 smaller than a first preset reliability threshold; then calculating the weights W of the plurality of first candidate paths, and selecting the candidate path with the minimum W for data transmission;
when selecting a time-efficient path, calculate W2 ═ b ∑ TdelaySelecting a plurality of second candidate paths with W2 smaller than a first preset timeliness threshold; and then calculating the weights W of the plurality of second candidate paths, and selecting the candidate path with the minimum W for data transmission.
When a path crossing the sub monitoring network is selected for data transmission, a reliability path and/or a timeliness path are selected according to the type of abnormal data;
when a reliability path is selected, calculating W1 ═ a ∑ Cost, and selecting a plurality of first candidate paths with W1 smaller than a first preset reliability threshold; then calculating the weights W of the plurality of second candidate paths;
calculating a third weight W3 of the path according to the weights W of the plurality of second candidate paths;
W3＝(Nmax/Nmin)*W，
selecting a candidate path with the minimum W3 for data transmission;
wherein Nmax is max (N1, N2, N3);
nmin is the value of non-0 closest to 0 of N1, N2, and N3 (e.g., when the value is: 5,9,4, N is 4; when the value is 7,8,0, N is 7); wherein, N1, N2, N3 are the number of nodes of each sub monitoring network on the transmission path;
when selecting a time-efficient path, calculate W2 ═ b ∑ TdelaySelecting a plurality of second candidate paths with W2 smaller than a first preset timeliness threshold; then calculating the weights W of the plurality of second candidate paths;
calculating a fourth weight W4 of the path according to the weights W of the plurality of second candidate paths;
W4＝(Nmax/Nmin)*W；
selecting a candidate path with the minimum W4 for data transmission;
wherein Nmax is max (N1, N2, N3);
nmin is a value other than 0, which is closest to 0, of N1, N2 and N3 (e.g., when the value is 5,9 and 4, N is 4, and when the value is 7,8 and 0, N is 7); wherein, N1, N2, N3 are the number of nodes of each sub monitoring network on the transmission path;
when setting and selecting various threshold values related to the scheme, workers determine the threshold values according to analysis of historical data.
Through the arrangement of the transmission mode, a proper path can be selected for transmission according to the characteristics of the data so as to ensure the reliability and/or timeliness requirements of the data. When two paths are selected for transmission at the same time, the reliable and time-efficient paths are selected for transmission at the same time according to the method.
Determining the data transmission mode according to the weight Q of the abnormal data comprises the following steps: training and modeling are carried out according to parameters such as the type X, the weight Q and the like of abnormal data through analysis of historical data, and the incidence relation of the type X, the weight Q and the output information path information of input information is established according to a model; determining and selecting a reliability and/or timeliness path according to the type X and the weight Q of the abnormal data; the specific training method is not limited herein, and may be a training method based on deep learning, a training method of a support vector machine, or the like.
Through the setting of the abnormal data and the transmission path, unified management of plants, equipment and personnel in the garden can be realized, and meanwhile, the reliability and timely transmission of the data can be guaranteed.
Further, the data center is also used for maintaining according to the received information; the maintenance includes:
determining a monitoring object according to the received information;
determining an abnormal index according to the monitored object;
determining an abnormal weight according to the abnormal index;
generating a maintenance work order according to the abnormal weight;
the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credit degree of the maintenance personnel according to the timeliness and reliability factors of the maintenance work order;
and matching the maintainers according to the credibility of the maintainers.
The determining of the abnormal weight according to the abnormal index comprises:
determining M from the abnormality indexHuman being、MDevice、MVegetationAnd calculating the influence factor U,
wherein, U ═ MHuman being+MDevice+MVegetation)；
Acquiring an influence shadow U' according to the received weight Q,
comparing the consistency of the sigma U and the sigma U';
and when the abnormal weight is consistent with the abnormal weight, generating a maintenance work order.
The accuracy of an abnormal monitoring result and early warning can be achieved through comparison of abnormal weights, moreover, an algorithm is simple in a verification process, the accuracy can be guaranteed through simple operation, the burden of a server can be effectively relieved, and the reliability of a system is improved.
Further, the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credibility G of the maintenance personnel according to the factors of timeliness S and reliability H of the maintenance work order; matching the maintainers according to the credibility of the maintainers; the method specifically comprises the following steps: acquiring the estimated maintenance time, the duration of historical projects and the working quality information of each maintenance worker, and determining the timeliness S and the reliability H of the maintenance workers according to the duration and the working quality of the historical projects; and determining the credibility G of the maintainers according to the timeliness S and the reliability H of the maintainers and the information uploaded by the maintainers.
Wherein,
wherein, TAir conditionerIs the idle time of the person, TavgThe average value of the idle time of all the current personnel is obtained; t is the project completion time estimated by the personnel, tnowMeans early warning time; t is tavgThe average value of the estimated completion time of all the workers is referred to; timeliness S is the average maintenance duration for each person to complete the same type of project over a period of time in the past, e.g., one month, one quarter; h is the quality assessment value of each person completing the same type of project within a past period of time, such as one month, one quarter.
Furthermore, through the monitoring results of the sub-monitoring systems, the data center can timely obtain various data of various gardens, such as construction information, plant state information, personnel information and related equipment information, and effective garden engineering management can be carried out according to the information.
The invention fully considers the factors of the working quantity, the working quality and the like of each person and selects the person with the maximum credibility G to complete the maintenance work order. Therefore, the maintenance personnel can be accurately managed, and the attention of the maintenance personnel to the working quality can be effectively improved through the mode. Meanwhile, various garden data can be acquired in real time through the management system, unified management is carried out on the garden data, overall deployment and implementation of landscaping planning and construction can be guided and supervised scientifically, and scientific decision-making of leaders is assisted.
Based on the management system, the invention also provides an intelligent management method for the garden landscape engineering, as shown in fig. 2, the method comprises the following steps:
determining a network establishment strategy according to the state information of plants, equipment and personnel so that a networking subsystem carries out monitoring network establishment according to the network establishment strategy;
establishing a monitoring strategy and transmitting the monitoring strategy to a monitoring subsystem to execute the monitoring strategy;
a monitoring network is established to realize the monitoring of plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies, and perform early warning according to monitoring results.
Wherein the monitoring network establishment comprises: respectively acquiring plant information, equipment information and personnel information issued by a data center, and different networking strategies corresponding to the plant information, the equipment information and the personnel information; wherein the networking policy comprises a data transmission policy; according to the difference of the monitored objects, different transmission mode combinations are set, and the reliability and the timeliness of different data transmission are guaranteed through different modes.
The networking strategy comprises the following steps: the method comprises the steps that sub monitoring networks are established respectively aiming at plant information, equipment information and personnel information, the sub monitoring networks support the same and different data transmission modes, and each sub monitoring network can be used for realizing data transmission of the other two sub monitoring networks;
the data transmission strategy comprises the following steps:
acquiring monitoring data;
identifying the type of the monitoring data according to the monitoring data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
wherein Q ═ Σ ((M)Human being+MDevice+MVegetation) R); r is an abnormality indexA degree of offset; mHuman being、MDevice、MVegetationRespectively the influence degrees of the R abnormal index on people, equipment and vegetation;
wherein M isHuman beingThe ratio of the number of persons related to the project to the total number of related persons is referred to, for example, the ratio of the number of maintenance persons required by the project to the total number of maintenance persons; mDeviceThe ratio of the number of currently available devices to the number of total devices is referred to; mVegetationIs the ratio of the area occupied by the affected vegetation to the total area occupied by the vegetation in the garden. The uploaded weight data Q includes values of respective influence degrees.
Determining a data transmission mode according to the weight Q of the abnormal data, wherein the data transmission mode comprises sub-monitoring network transmission, cross-sub-monitoring network transmission and redundant transmission of the sub-monitoring network and the cross-sub-monitoring network; the cross-sub monitoring network transmission is to merge three sub monitoring networks into one integrated monitoring network, and an abnormal data transmission path is set by using equipment in the integrated monitoring network so as to realize the transmission of abnormal data.
After the network is built, a multi-level data transmission network is formed; the superior node distributes a transmission time slot for each subordinate node, and the subordinate nodes transmit data on the distributed transmission time slots;
selecting a proper path according to the type of data in the data transmission process; specifically, the method includes respectively obtaining node information on a path, where the node information includes: the Cost between nodes before each adjacent node, the distance D, the network reliability K between two nodes and the time delay Tdelay; calculating the weight W of each path according to the node information, wherein the weight W is a sigma Cost + b sigma Tdelay；
Wherein a ═ ln (n) ((K-K))avg)/(Kavg-Kmin)；
b＝ln(n)*(D-Davg)/(Davg-Dmin)；
Wherein Kavg is the average value of the network reliability between every two nodes on the path; kmin is the minimum value of network reliability between every two nodes on the path(ii) a Davg is the average value of the distance between every two nodes on the path; dmin is the minimum value of the distance between every two nodes on the path; n is the total number of nodes on the path. Wherein the time delay TdelayThe length of time that data needs to wait on the node; the node reliability K uses a fault tolerance representation of the network.
In the data transmission process, when the sub monitoring network path is selected, a reliability path and/or a timeliness path are selected according to the type of abnormal data;
when the reliability path is selected, calculating W1 ═ a ═ Sigma Cost, and selecting a plurality of first candidate paths with W1 smaller than a first preset reliability threshold; then calculating the weights W of the plurality of first candidate paths, and selecting the candidate path with the minimum W for data transmission;
when selecting a time-critical path, calculate W2 ═ b ∑ TdelaySelecting a plurality of second candidate paths with W2 smaller than a first preset timeliness threshold; and then calculating the weights W of the plurality of second candidate paths, and selecting the candidate path with the minimum W for data transmission.
When a path crossing the sub monitoring network is selected for data transmission, a reliability path and/or a timeliness path are selected according to the type of abnormal data;
when a reliability path is selected, calculating W1 ═ a ∑ Cost, and selecting a plurality of first candidate paths with W1 smaller than a first preset reliability threshold; then calculating the weights W of the plurality of second candidate paths;
calculating a third weight W3 of the path according to the weights W of the plurality of second candidate paths;
W3＝(Nmax/Nmin)*W，
selecting a candidate path with the minimum W3 for data transmission;
wherein Nmax ═ max (N1, N2, N3);
nmin is the value of non-0 closest to 0 of N1, N2, and N3 (e.g., when the value is: 5,9,4, N is 4; when the value is 7,8,0, N is 7); wherein, N1, N2, N3 are the number of nodes of each sub-monitoring network on the transmission path;
when selecting a time-sensitive path, calculate W2 ═ b ∑ ΣTdelaySelecting a plurality of second candidate paths with W2 smaller than a first preset timeliness threshold; then calculating the weights W of the plurality of second candidate paths;
calculating a fourth weight W4 of the path according to the weights W of the plurality of second candidate paths;
W4＝(Nmax/Nmin)*W；
selecting a candidate path with the minimum W4 for data transmission;
wherein Nmax is max (N1, N2, N3);
nmin is a value other than 0, which is closest to 0, of N1, N2 and N3 (e.g., when the value is 5,9 and 4, N is 4, and when the value is 7,8 and 0, N is 7); wherein, N1, N2, N3 are the number of nodes of each sub monitoring network on the transmission path;
through the arrangement of the transmission mode, a proper path can be selected for transmission according to the characteristics of the data so as to ensure the reliability and/or timeliness requirements of the data. When two paths are selected for transmission at the same time, the reliable and time-efficient paths are selected for transmission at the same time according to the method.
Determining the data transmission mode according to the weight Q of the abnormal data comprises the following steps: training and modeling are carried out according to parameters such as the type X, the weight Q and the like of abnormal data through analysis of historical data, and the incidence relation of the type X, the weight Q and the output information path information of input information is established according to a model; determining and selecting a reliability and/or timeliness path according to the type X and the weight Q of the abnormal data; the specific training method is not limited herein, and may be a training method based on deep learning, a training method of a support vector machine, or the like.
Through the setting of the abnormal data and the transmission path, unified management of plants, equipment and personnel in the garden can be realized, and meanwhile, the reliability and timely transmission of the data can be guaranteed.
The data center maintains according to the received information; the maintenance includes:
determining a monitoring object according to the received information;
determining an abnormal index according to the monitored object;
determining an abnormal weight according to the abnormal index;
generating a maintenance work order according to the abnormal weight;
the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credit degree of the maintenance personnel according to the timeliness and reliability factors of the maintenance work order;
and matching the maintainers according to the credibility of the maintainers.
The determining of the abnormal weight according to the abnormal index comprises the following steps:
determining M from the abnormality indexHuman being、MDevice、MVegetationAnd calculating the influence factor U,
wherein, U ═ MHuman being+MDevice+MVegetation)；
Acquiring an influence shadow U' according to the received weight Q,
comparing the consistency of the sigma U and the sigma U';
and when the abnormal weight is consistent with the abnormal weight, generating a maintenance work order.
The accuracy of an abnormal monitoring result and early warning can be achieved through comparison of abnormal weights, moreover, an algorithm is simple in a verification process, the accuracy can be guaranteed through simple operation, the burden of a server can be effectively relieved, and the reliability of a system is improved.
Further, the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credibility G of the maintenance personnel according to the factors of timeliness S and reliability H of the maintenance work order; matching the maintainers according to the credibility of the maintainers; the method specifically comprises the following steps: acquiring the estimated maintenance time, the duration of historical projects and the working quality information of each maintenance worker, and determining the timeliness S and the reliability H of the maintenance workers according to the duration and the working quality of the historical projects; and determining the credibility G of the maintainers according to the timeliness S and the reliability H of the maintainers and the information uploaded by the maintainers.
Wherein,
wherein, TAir conditionerIs the idle time of the person, TavgThe average value of the idle time of all the current personnel is obtained; t is the project completion time estimated by the personnel, tnowMeans early warning time; t is tavgThe average value of the estimated completion time of all the workers is referred to; timeliness S is the average maintenance duration for each person to complete the same type of project over a period of time, such as one month, one quarter; h is the quality assessment value of each person completing the same type of project over a period of time, such as one month, one quarter.
Furthermore, through the monitoring results of the sub-monitoring systems, the data center can timely obtain various data of various gardens, such as construction information, plant state information, personnel information and related equipment information, and effective garden engineering management can be carried out according to the information.
The invention fully considers the factors of the working quantity, the working quality and the like of each person and selects the person with the maximum credibility G to complete the maintenance work order. Therefore, the maintenance personnel can be accurately managed, and the attention of the maintenance personnel to the working quality can be effectively improved through the mode. Meanwhile, various garden data can be acquired in real time through the management system, unified management is carried out on the garden data, overall deployment and implementation of landscaping planning and construction can be guided and supervised scientifically, and scientific decision-making of leaders is assisted.
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. The utility model provides a landscape engineering wisdom management system which characterized in that, management system includes:
the networking subsystem is used for monitoring network establishment so as to realize monitoring on plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies and perform early warning according to monitoring results;
the data center is used for determining a network construction strategy according to the state information of plants, equipment and personnel so that the networking subsystem can monitor the network construction according to the network construction strategy; and the monitoring subsystem is used for establishing a monitoring strategy and sending the monitoring strategy to the monitoring subsystem for executing the monitoring strategy.
2. The management system of claim 1, wherein the monitoring network setup comprises: respectively acquiring plant information, equipment information and personnel information issued by a data center, and different networking strategies corresponding to the plant information, the equipment information and the personnel information; the networking strategy comprises a data transmission strategy; according to the difference of the monitored objects, different transmission mode combinations are set, and the reliability and the timeliness of different data transmission are guaranteed through different modes.
3. The management system of claim 2,
the networking strategy comprises the following steps: sub-monitoring networks are respectively established aiming at plant information, equipment information and personnel information, the sub-monitoring networks support the same or different data transmission modes, and each sub-monitoring network can be used for realizing data transmission of the other two sub-monitoring networks;
the data transmission strategy comprises:
acquiring monitoring data;
identifying the type of the monitoring data according to the monitoring data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
wherein Q ═ Σ ((M)Human being+MDevice+MVegetation) R); r is the degree of deviation of the abnormality index; m is a group ofHuman being、MDevice、MVegetationRespectively the influence degrees of the R abnormal index on people, equipment and vegetation;
determining a data transmission mode according to the weight Q of the abnormal data, wherein the data transmission mode comprises sub-monitoring network transmission, cross-sub-monitoring network transmission and redundant transmission of the sub-monitoring network and the cross-sub-monitoring network; the cross-sub monitoring network transmission is to merge three sub monitoring networks into one integrated monitoring network, and an abnormal data transmission path is set by using equipment in the integrated monitoring network so as to realize the transmission of abnormal data.
4. The management system of claim 1, wherein the data center is further configured to perform maintenance based on the received information; the maintenance comprises the following steps:
determining a monitoring object according to the received information;
determining an abnormal index according to the monitored object;
determining the abnormal weight according to the abnormal index;
generating a maintenance work order according to the abnormal weight;
the maintenance work order comprises a personnel matching strategy, and concretely comprises the steps of calculating the credit degree of the maintenance personnel according to the timeliness and reliability factors of the maintenance work order;
and matching the maintainers according to the credibility of the maintainers.
5. The management system of claim 4,
the determining of the abnormal weight according to the abnormal index comprises:
determining M from the abnormality indexHuman being、MDevice、MVegetationAnd calculating the influence factor U,
wherein, U ═ MHuman being+MDevice+MVegetation)；
Acquiring an influence shadow U' according to the received weight Q,
comparing the consistency of the sigma U and the sigma U';
and when the abnormal weight is consistent with the abnormal weight, generating a maintenance work order.
6. A garden landscape engineering intelligent management method is characterized by comprising the following steps:
determining a network establishment strategy according to the state information of plants, equipment and personnel so that a networking subsystem carries out monitoring network establishment according to the network establishment strategy;
establishing a monitoring strategy and transmitting the monitoring strategy to a monitoring subsystem to execute the monitoring strategy;
a monitoring network is established to realize the monitoring of plants, equipment and personnel;
the plurality of sub-monitoring systems monitor plants, equipment and personnel respectively based on different monitoring strategies, and perform early warning according to monitoring results.
7. The method of managing of claim 6, wherein the monitoring network set-up comprises: respectively acquiring plant information, equipment information and personnel information issued by a data center, and different networking strategies corresponding to the plant information, the equipment information and the personnel information; wherein the networking policy comprises a data transmission policy; according to the difference of the monitored objects, different transmission mode combinations are set, and the reliability and the timeliness of different data transmission are guaranteed through different modes.
8. The management method according to claim 7,
the networking strategy comprises the following steps: the method comprises the steps that sub monitoring networks are established respectively aiming at plant information, equipment information and personnel information, the sub monitoring networks support the same and different data transmission modes, and each sub monitoring network can be used for realizing data transmission of the other two sub monitoring networks;
the data transmission strategy comprises:
acquiring monitoring data;
identifying the type of the monitoring data according to the monitoring data;
when the monitoring result of the monitoring data is abnormal data, calculating the weight Q of the abnormal data;
wherein,Q＝∑((Mhuman being+MDevice+MVegetation) R); r is the degree of deviation of the abnormality index; mHuman being、MDevice、MVegetationRespectively the influence degrees of the R abnormal index on people, equipment and vegetation;
determining a data transmission mode according to the weight Q of the abnormal data, wherein the data transmission mode comprises sub-monitoring network transmission, cross-sub-monitoring network transmission and redundant transmission of the sub-monitoring network and the cross-sub-monitoring network; the cross-sub monitoring network transmission is to merge three sub monitoring networks into one integrated monitoring network, and an abnormal data transmission path is set by using equipment in the integrated monitoring network so as to realize the transmission of abnormal data.
9. The management method according to claim 6, wherein the data center performs maintenance based on the received information; the maintenance includes:
determining a monitored object according to the received information;
determining an abnormal index according to the monitored object;
determining an abnormal weight according to the abnormal index;
generating a maintenance work order according to the abnormal weight;
the maintenance work order comprises a matching strategy of personnel, and concretely comprises the steps of calculating the credit degree of the maintenance personnel according to the timeliness and reliability factors of the maintenance work order;
and matching the maintainers according to the credibility of the maintainers.
10. The management method according to claim 9,
the determining of the abnormal weight according to the abnormal index comprises:
determining M from the abnormality indexHuman being、MDevice、MVegetationCalculating the influence factor U',
wherein, U ═ MHuman being+MDevice+MVegetation)；
Acquiring an influence shadow U' according to the received weight Q,
comparing the consistency of the sigma U and the sigma U';
and when the abnormal weight is consistent with the abnormal weight, generating a maintenance work order.
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