CN119025826A - A method and system for intelligent management of grassland monitoring equipment - Google Patents
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Abstract
本发明公开了一种草场的监控设备智能管理方法及系统，具体涉及草场管理技术领域，本发明通过将目标子圈舍的状态划分为异常状态和正常状态，对于异常状态的目标子圈舍，根据实际的圈舍状态特征数据判断监控设备的开启类型和开启数量，并获取目标子圈舍的每一所需监控设备质量数据，根据质量数据得到质量系数，根据质量系数确定目标子圈舍需要开启的监控设备，并针对所有的异常状态的目标子圈舍进行对应的管理，这样一来，能够根据实际的圈舍内的状态智能的对圈舍内的监控设备进行开启管理，使得圈舍的监控管理灵活性较高，节省资源还能提高管理效率。
The present invention discloses an intelligent management method and system for monitoring equipment of a grassland, and specifically relates to the technical field of grassland management. The present invention divides the state of a target sub-sty into an abnormal state and a normal state. For a target sub-sty in an abnormal state, the activation type and activation quantity of the monitoring equipment are determined according to actual characteristic data of the sty state, and the quality data of each required monitoring equipment of the target sub-sty is obtained, and a quality coefficient is obtained according to the quality data. The monitoring equipment that needs to be activated in the target sub-sty is determined according to the quality coefficient, and corresponding management is performed for all target sub-sties in abnormal states. In this way, the activation management of the monitoring equipment in the sty can be intelligently performed according to the actual state in the sty, so that the monitoring management of the sty is more flexible, resources are saved, and management efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of grassland management, in particular to an intelligent management method and system for monitoring equipment of a grassland.
Background
In order to ensure sustainable development of a grassland ecological system and economic benefits of grassland husbandry, intelligent management of the grassland becomes particularly important, particularly, monitoring of livestock on the grassland can be used for timely finding and coping with health problems of the livestock, production efficiency is improved, and livestock are prevented from being lost or stolen. Meanwhile, the monitoring data can help to optimize grazing strategies, prevent overutilization and degradation of the grassland, ensure long-term health of the grassland ecological system and protect ecological balance of the grassland.
In general, when monitoring livestock in a colony house, monitoring the livestock usually by setting monitoring equipment with different functions, for example, monitoring behavior details of the livestock by installing a high-definition camera, such as feeding, drinking, resting and the like; installing an audio monitoring camera to monitor abnormal sounds in the colony house, installing a thermal imaging camera to monitor health problems of livestock, such as heating or inflammation, and detecting potential safety hazards such as overheat of equipment, and the like, and intelligently monitoring the livestock in the colony house by installing different types of monitoring equipment;
In general, when monitoring livestock in the colony house, the monitoring equipment is fixedly started to monitor the livestock in the colony house, and the monitoring equipment in the colony house cannot be intelligently started and managed according to the actual state in the colony house, so that the monitoring management is lack of flexibility, resource waste and low management efficiency are caused.
Disclosure of Invention
The invention aims to solve the problem and provides an intelligent management method and system for monitoring equipment in a grassland.
In a first aspect of the present invention, a method for intelligent management of monitoring equipment in a grassland is first provided, the method comprising the steps of:
Acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
For a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
Acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony; the monitoring device quality data includes a device current stability factor and a usage time factor.
Optionally, matching the colony house state feature data with the historical colony house state feature data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house includes:
Preprocessing the colony house state characteristic data and the historical colony house normal state characteristic data;
Constructing a distance matrix, wherein each element is a distance between a data point and a historical data point;
calculating the shortest path in the distance matrix by using a dynamic programming algorithm to find the best match between the data sequence and the historical data sequence;
According to the shortest path obtained by calculation, aligning the data sequence and the historical data sequence;
Calculating the similarity between the aligned data sequences and the historical data sequences according to the aligned data sequences and the historical data sequences, wherein the similarity is used as a first abnormal degree value of the target colony;
Comparing the first abnormal degree value of the target colony house with a preset first abnormal degree threshold value, and dividing the target colony house into a normal state if the first abnormal degree value is not smaller than the preset first abnormal degree threshold value;
and if the first abnormality degree value is smaller than a preset first abnormality degree threshold value, dividing the target colony house into a normal state.
Optionally, for the target colony house in the abnormal state, obtaining the abnormal feature type and the second abnormal degree value corresponding to each abnormal feature type through the corresponding colony house state feature data in a preset mode includes:
The processing steps of the preset mode are as follows:
Defining the current colony state characteristic data of the target colony as an input item of a fuzzy rule, and drawing in a fuzzy set;
Dividing the abnormal feature type and a second abnormal degree value corresponding to each abnormal feature type into different fuzzy sets by taking the abnormal feature type and the second abnormal degree value corresponding to each abnormal feature type as output variables;
formulating fuzzy rules for describing influence of input variables on different output variables;
and carrying out fuzzy reasoning according to the fuzzy rule, and outputting the abnormal characteristic type of the target colony and a second abnormal degree value corresponding to each abnormal characteristic type.
Optionally, the method for obtaining the current stability coefficient of the device comprises the following steps:
Acquiring actual current values of each monitoring device at different moments in a preset time period, calibrating the current values as I Real world z, wherein z represents actual current value sequence numbers of each monitoring device at different moments in the preset time period, z epsilon [1, p ], p is the number of the acquired actual current values I Real world z, and p is a positive integer;
Calculating standard deviation of a current value I Real world z in a preset time period, taking the standard deviation as a device current stability coefficient of the monitoring device, wherein the calculated formula is as follows:
Wherein, For the average value of the actual current values of each monitoring device at different moments in the preset time period, the obtained expression is: Optionally, a
Optionally, the method for acquiring the usage time coefficient is as follows:
acquiring the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance of each monitoring device, and obtaining the use time coefficient of the monitoring device according to the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance, wherein the calculated formula is as follows:
Wherein Dsw is a use time coefficient, gd, fd, kl, kh is a total use time length, a preset usable time length, an average maintenance interval time and a time interval of last maintenance of a current time distance of the monitoring device, and a1 and a2 are preset influence factors.
Optionally, the method for obtaining the quality coefficient according to the quality data comprises the following steps:
And carrying out normalization processing on the equipment current stability coefficient and the use time coefficient, and obtaining a quality coefficient according to the normalized equipment current stability coefficient and the normalized use time coefficient.
Optionally, the monitoring device for determining that the target colony house needs to be opened according to the quality coefficient includes:
The method comprises the steps that the required opening types of monitoring equipment and the opening quantity of each opening type of monitoring equipment of a target colony are determined, and the opening types and the corresponding preset opening quantity are used as preset opening types;
Starting the monitoring equipment corresponding to each starting type according to the sequence from the small quality coefficient to the large quality coefficient, and stopping starting other monitoring equipment of the type when the starting quantity of the monitoring equipment corresponding to each starting type reaches the corresponding preset starting quantity;
and starting all required monitoring equipment starting types and corresponding monitoring equipment starting quantity of the target colony house, and monitoring the target colony house.
In a second aspect of the present invention, a system for intelligent management of monitoring devices in a grassland is provided, the system comprising:
The dividing module: acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
an anomaly module: for a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
And the equipment opening module is used for: determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
and a management module: acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony; the monitoring device quality data includes a device current stability factor and a usage time factor.
The invention has the beneficial effects that:
The invention provides an intelligent management method and system for monitoring equipment in a grassland, wherein the states of a target colony house are divided into an abnormal state and a normal state, and the opening type and the opening number of the monitoring equipment are judged according to actual colony house state characteristic data for the target colony house in the abnormal state, and corresponding management is performed, so that the monitoring equipment in the colony house can be intelligently opened and managed according to the actual colony house state, the colony house is high in monitoring management flexibility, resources are saved, and management efficiency is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method for intelligent management of monitoring equipment in a grassland, provided by the invention;
Fig. 2 is a frame diagram of an intelligent management system for monitoring equipment in a grassland.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides an intelligent management method for monitoring equipment in a grassland. Referring to fig. 1, fig. 1 is a flowchart of a method for intelligently managing monitoring equipment in a grassland according to an embodiment of the present invention. The method comprises the following steps:
Acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
For a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
And acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony.
It should be noted that the currently running monitoring devices refer to various monitoring devices that are already opened inside the target housing and are being used, such as a certain high-definition camera, a certain audio monitoring device, a certain thermal imaging camera, and the like; the equipment is preset by a staff in the beginning of the profession, can comprehensively cover the monitoring of the colony house, and can provide more comprehensive data for monitoring the state in the colony house for subsequent analysis.
According to the intelligent management method for the monitoring equipment in the grassland, provided by the embodiment of the invention, the states of the target colony house are divided into the abnormal state and the normal state, and for the target colony house in the abnormal state, the opening types and the opening quantity of the monitoring equipment are judged according to the actual colony house state characteristic data and are correspondingly managed, so that the monitoring equipment in the colony house can be intelligently opened and managed according to the actual colony house state, the colony house is higher in monitoring management flexibility, resources are saved, and the management efficiency is improved.
In one embodiment, matching the shed state feature data with the historical shed state feature data, determining a first degree of abnormality value of the target shed, and classifying the state of the target shed into an abnormal state and a normal state according to the first degree of abnormality value of the target shed comprises:
Preprocessing the colony house state characteristic data and the historical colony house normal state characteristic data;
Constructing a distance matrix, wherein each element is a distance between a data point and a historical data point;
calculating the shortest path in the distance matrix by using a dynamic programming algorithm to find the best match between the data sequence and the historical data sequence;
According to the shortest path obtained by calculation, aligning the data sequence and the historical data sequence;
Calculating the similarity between the aligned data sequences and the historical data sequences according to the aligned data sequences and the historical data sequences, wherein the similarity is used as a first abnormal degree value of the target colony;
Comparing the first abnormal degree value of the target colony house with a preset first abnormal degree threshold value, and dividing the target colony house into a normal state if the first abnormal degree value is not smaller than the preset first abnormal degree threshold value;
and if the first abnormality degree value is smaller than a preset first abnormality degree threshold value, dividing the target colony house into a normal state.
It should be noted that, the colony house status characteristic data in the target colony house may be data including livestock behavior, diet, rest, exercise, sound, body temperature, etc. in the current time period; the current time period is a time period set by professionals and is used for monitoring the colony house state characteristic data in the target colony house in the time period, the setting of the time period is average, measures taken on livestock in the colony house are not late due to the longer time period, and the accident of colony house state monitoring data in the target colony house is not large due to the shorter time period; the historical colony house normal state characteristic data comprises the same type of data of a past period of time, and the data is judged by a professional, and the reflected colony house state is normal.
It should be noted that, preprocessing data and history data:
firstly, preprocessing colony house state characteristic data and historical colony house normal state characteristic data in a target colony house to ensure consistency and comparability of the data. The preprocessing step may include operations such as data cleansing, outlier processing, data smoothing or normalization, etc.
Building a distance matrix: the distance between each data point and the historical data point is calculated by using a selected distance measurement method (such as Euclidean distance, manhattan distance or dynamic time warping distance, etc.), and a distance matrix is constructed. A distance matrix is set where the distance between a data point and a historical data point is represented.
Calculating the shortest path by dynamic programming: the shortest path in the distance matrix is calculated using a dynamic planning algorithm (e.g., dynamic time warping, DTW). Dynamic time warping allows time alignment to occur within a local region, finding the best matching path between the data sequence and the historical data sequence.
Aligning the data sequence with the historical data sequence: and aligning the data sequence with the historical data sequence according to the shortest path obtained by the dynamic programming algorithm. The alignment operation will ensure that the corresponding data points on the time axis are able to make subsequent similarity calculations.
Calculating similarity: and calculating the similarity between the aligned data sequence and the historical data sequence. Various methods may be used to calculate the similarity, such as cosine similarity, correlation coefficients, or other distance metric methods based on the aligned sequences.
Comparing the similarity to a threshold: and finally, comparing the calculated similarity with a preset similarity threshold. If the similarity is greater than the set threshold, the colony house state characteristic data is considered to be in a normal range; and if the similarity is smaller than or equal to a threshold value, indicating that the colony house state of the target colony house is abnormal.
In an implementation mode, the methods of data processing, distance calculation, dynamic planning, similarity analysis and the like are combined, the colony house state characteristic data in the current target colony house and the colony house normal state characteristic data in the history colony house are calculated, the similarity is calculated, and the current target colony house is divided into the normal state or the abnormal state according to the similarity, so that the abnormal condition of the target colony house can be effectively evaluated and managed, the health or behavior abnormality of livestock in the colony house can be timely found and responded, and the efficiency of grassland management and the quality of livestock production are improved.
It should be noted that, if the target colony house is in a normal state, the monitoring device running before use monitors the corresponding target colony house.
In one embodiment, for the target colony house in the abnormal state, obtaining the abnormal feature type and the second abnormal degree value corresponding to each abnormal feature type through the corresponding colony house state feature data in a preset mode includes:
The processing steps of the preset mode are as follows:
Defining the current colony state characteristic data of the target colony as an input item of a fuzzy rule, and drawing in a fuzzy set;
Dividing the abnormal feature type and a second abnormal degree value corresponding to each abnormal feature type into different fuzzy sets by taking the abnormal feature type and the second abnormal degree value corresponding to each abnormal feature type as output variables;
formulating fuzzy rules for describing influence of input variables on different output variables;
and carrying out fuzzy reasoning according to the fuzzy rule, and outputting the abnormal characteristic type of the target colony and a second abnormal degree value corresponding to each abnormal characteristic type.
It should be noted that the processing steps of the preset mode may be:
Input variables and fuzzy sets: the monitored colony house status characteristic data is assumed to include three variables:
A nutritional problem; health problems; abnormal behavior; the second abnormality degree value of each abnormality feature type may be divided into: mild, moderate, severe;
Making a fuzzy rule: the fuzzy rule may describe the effect of an input variable on an output variable. For example:
If the feeding time is low and the number of drinking times is low and the exercise time is low, the nutritional problems are serious.
If the feeding time is medium and the number of drinking times is medium and the exercise time is medium, the health problem is medium.
If the feeding time is high and the number of drinking times is high and the exercise time is high, the behavior is unusual slight.
Fuzzy reasoning: through fuzzy reasoning, the abnormal characteristic type of the target colony house and the second abnormal degree value corresponding to each abnormal characteristic type can be determined.
For example: the following colony house status feature data are assumed: feeding time: 10 minutes; the number of drinking times: 2 times; exercise time: 5 minutes (Low);
1. blurring of input variables:
Based on the input data, the variables are drawn into fuzzy sets: feeding time: low (Low); the number of drinking times: medium (Medium); exercise time: low (Low)
2. Fuzzy rule matching: according to the fuzzy rule: if the feeding time is low and the number of drinking times is moderate and the exercise time is low, the nutritional problems are moderate.
3. Fuzzy reasoning: by fuzzy reasoning, it can be obtained: the type of abnormality is a nutritional problem and the corresponding second degree of abnormality is medium.
Similarly, other types of abnormal characteristics and corresponding values of the second degree of abnormality, such as health problems, behavioral abnormalities, environmental problems, etc., may be outputted in the same manner, and specific examples thereof will not be explained herein.
In an implementation manner, by the method, the abnormal characteristic type of the target colony house and the second abnormal degree value corresponding to each abnormal characteristic type are obtained through fuzzy logic, so that a grassland manager can be helped to more accurately identify and process abnormal conditions in the colony house, and the management efficiency and the livestock health level are improved.
In one embodiment, it should be noted that, determining the required monitoring device opening types of the target housing and the number of monitoring device openings of each opening type according to the abnormal feature type of the target housing and the second abnormality degree value corresponding to each abnormal feature type may be: for example, if nutritional problems are detected and severe, multiple high definition cameras need to be turned on to view the feeding behavior and intake of livestock in detail; if the health problem is detected and the degree is serious, a plurality of thermal imaging cameras are required to be started so as to monitor the body temperature and the physiological index of the livestock in real time; for abnormal behaviors, a plurality of panoramic cameras and audio monitoring equipment may need to be started to comprehensively capture and analyze behaviors and sounds of livestock, secondly, the number of the monitoring equipment is determined according to the colony house area and abnormal spatial distribution, all areas needing important monitoring are ensured to be covered, monitoring blind areas are avoided, and through the mode, the monitoring equipment can be intelligently managed and started according to the actual colony house state, and accurate monitoring and effective management of the livestock in the colony house are achieved.
In one embodiment, the quality data of each required monitoring device of the target colony is obtained, the quality coefficient is obtained according to the quality data, the quality data of the monitoring device comprises a device current stability coefficient and a use time coefficient, and the method for obtaining the device current stability coefficient is as follows:
Acquiring actual current values of each monitoring device at different moments in a preset time period, calibrating the current values as I Real world z, wherein z represents actual current value sequence numbers of each monitoring device at different moments in the preset time period, z epsilon [1, p ], p is the number of the acquired actual current values I Real world z, and p is a positive integer;
Calculating standard deviation of a current value I Real world z in a preset time period, taking the standard deviation as a device current stability coefficient of the monitoring device, wherein the calculated formula is as follows:
Wherein, For the average value of the actual current values of each monitoring device at different moments in the preset time period, the obtained expression is:
It should be noted that, the preset time period is set by a professional according to the actual situation, and is not limited in particular; in addition, the actual current value of each monitoring device at different moments in the preset time period can be directly obtained by calling the historical operation record of the monitoring device, or can be obtained in other modes, and the method is not limited in particular;
The relation between the equipment current stability coefficient and the quality of the monitoring equipment is that when the equipment current stability coefficient is smaller, the current of the monitoring equipment is more stable, and the quality of the monitoring equipment is better; when the current stability coefficient of the device is larger, the current of the monitoring device is unstable, and the quality of the monitoring device is poor.
In an implementation mode, through the mode, the current stability coefficient of the monitoring equipment can be obtained more accurately, and the quality of the monitoring equipment can be evaluated more accurately.
In one embodiment, the method for obtaining the time coefficient is as follows:
acquiring the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance of each monitoring device, and obtaining the use time coefficient of the monitoring device according to the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance, wherein the calculated formula is as follows:
wherein Dsw is a use time coefficient, gd, fd, kl, kh is the total use time length, the preset usable time length, the average maintenance interval time and the time interval of the last maintenance of the current time distance of the monitoring equipment, and a1 and a2 are preset influence factors;
The method for obtaining the quality coefficient according to the quality data comprises the following steps:
And carrying out normalization processing on the equipment current stability coefficient and the use time coefficient, and obtaining a quality coefficient according to the normalized equipment current stability coefficient and the normalized use time coefficient.
It should be noted that, a1 and a2 are set by a professional according to actual situations, and are not specifically limited;
It should be noted that, for example, the present invention may calculate the mass coefficient by using the following formula:
wherein Bvy is a mass coefficient, wax is a device current stability coefficient, dsw is a use time coefficient, f1 and f2 are proportional coefficients of the device current stability coefficient and the use time coefficient, and f1 and f2 are both larger than 0;
It should be noted that, the total duration of use, the average maintenance interval time and the time interval of last maintenance of the current time distance of each monitoring device may be obtained according to the maintenance log of the monitoring device, or may be other manners, which is not specifically limited; in addition, the preset usable time length can be obtained according to a factory log of the monitoring device, or can be obtained in other modes, and the method is not limited specifically.
It should be noted that, the relationship between the usage time coefficient and the quality of the monitoring device is that, when the usage time coefficient is smaller, the quality of the monitoring device is better; the larger the current stability coefficient of the device, the less good the quality of the monitoring device.
It should be noted that, when the quality coefficient of the monitoring device is smaller, the quality of the monitoring device is better, and when the quality coefficient of the monitoring device is larger, the quality of the monitoring device is worse.
It should be noted that the following benefits are obtained by selecting the current stability coefficient of the device and using the time coefficient to determine the quality of the monitoring device: the stability coefficient of the current of the equipment can directly reflect the working state and the stability of the electrical performance of the equipment, and monitoring errors and equipment damage caused by current fluctuation are avoided; the use time coefficient provides comprehensive evaluation of service life and maintenance condition of the equipment, predicts reliability and future fault risk of the equipment, and can comprehensively and accurately reflect actual quality and stability of the monitoring equipment, thereby ensuring effective operation of the equipment and long-term stability of a monitoring system in a complex monitoring environment.
In one embodiment, the monitoring device for determining that the target colony house needs to be opened according to the quality coefficient comprises:
The method comprises the steps that the required opening types of monitoring equipment and the opening quantity of each opening type of monitoring equipment of a target colony are determined, and the opening types and the corresponding preset opening quantity are used as preset opening types;
Starting the monitoring equipment corresponding to each starting type according to the sequence from the small quality coefficient to the large quality coefficient, and stopping starting other monitoring equipment of the type when the starting quantity of the monitoring equipment corresponding to each starting type reaches the corresponding preset starting quantity;
and starting all required monitoring equipment starting types and corresponding monitoring equipment starting quantity of the target colony house, and monitoring the target colony house.
In one embodiment, after the starting state of the monitoring device of each target colony house is determined, the monitoring devices of the target colony houses are managed in the same manner, so that the monitoring devices of the target colony houses are managed flexibly enough, the states of livestock in the colony houses can be timely and accurately found, and relevant measures can be timely taken, so that the management efficiency of the livestock in the colony houses is higher.
In an implementation mode, through the mode, the monitoring equipment in the colony house can be intelligently started and managed according to the state in the actual colony house, so that the monitoring management flexibility of the colony house is higher, resources are saved, and the management efficiency can be improved.
The embodiment of the invention also provides an intelligent management system for the monitoring equipment in the grassland based on the same inventive concept. Referring to fig. 2, fig. 2 is a schematic structural diagram of a system for intelligent management of monitoring devices in a grassland, which is provided by an embodiment of the present invention, and the system includes:
The dividing module: acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
an anomaly module: for a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
And the equipment opening module is used for: determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
And a management module: and acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony.
According to the intelligent management system for the monitoring equipment in the grassland, provided by the embodiment of the invention, the states of the target colony house are divided into the abnormal state and the normal state, and the opening types and the opening quantity of the monitoring equipment are judged according to the actual colony house state characteristic data for the target colony house in the abnormal state, and the corresponding management is performed, so that the monitoring equipment in the colony house can be intelligently opened and managed according to the actual colony house state, the colony house is higher in monitoring management flexibility, resources are saved, and the management efficiency is improved.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (8)
1. The intelligent management method for the monitoring equipment of the grassland is characterized by comprising the following steps of:
Acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
For a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
Acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony; the monitoring device quality data includes a device current stability factor and a usage time factor.
2. The intelligent management method of a grassland monitoring device according to claim 1, wherein the matching of the colony house status feature data and the historical colony house status feature data, determining the first abnormality degree value of the target colony house, and dividing the status of the target colony house into an abnormal status and a normal status according to the first abnormality degree value of the target colony house comprises:
Preprocessing the colony house state characteristic data and the historical colony house normal state characteristic data;
Constructing a distance matrix, wherein each element is a distance between a data point and a historical data point;
calculating the shortest path in the distance matrix by using a dynamic programming algorithm to find the best match between the data sequence and the historical data sequence;
According to the shortest path obtained by calculation, aligning the data sequence and the historical data sequence;
Calculating the similarity between the aligned data sequences and the historical data sequences according to the aligned data sequences and the historical data sequences, wherein the similarity is used as a first abnormal degree value of the target colony;
Comparing the first abnormal degree value of the target colony house with a preset first abnormal degree threshold value, and dividing the target colony house into a normal state if the first abnormal degree value is not smaller than the preset first abnormal degree threshold value;
and if the first abnormality degree value is smaller than a preset first abnormality degree threshold value, dividing the target colony house into a normal state.
3. The intelligent management method of a grassland monitoring device according to claim 1, wherein, for a target colony house in an abnormal state, obtaining an abnormal feature type and a second abnormal degree value corresponding to each abnormal feature type by a preset manner from corresponding colony house state feature data comprises:
The processing steps of the preset mode are as follows:
Defining the current colony state characteristic data of the target colony as an input item of a fuzzy rule, and drawing in a fuzzy set;
Dividing the abnormal feature type and a second abnormal degree value corresponding to each abnormal feature type into different fuzzy sets by taking the abnormal feature type and the second abnormal degree value corresponding to each abnormal feature type as output variables;
formulating fuzzy rules for describing influence of input variables on different output variables;
and carrying out fuzzy reasoning according to the fuzzy rule, and outputting the abnormal characteristic type of the target colony and a second abnormal degree value corresponding to each abnormal characteristic type.
4. The intelligent management method and system for monitoring equipment in a grassland according to claim 1, wherein the method for obtaining the current stability coefficient of the equipment is as follows:
Acquiring actual current values of each monitoring device at different moments in a preset time period, calibrating the current values as I Real world z, wherein z represents actual current value sequence numbers of each monitoring device at different moments in the preset time period, z epsilon [1, p ], p is the number of the acquired actual current values I Real world z, and p is a positive integer;
Calculating standard deviation of a current value I Real world z in a preset time period, taking the standard deviation as a device current stability coefficient of the monitoring device, wherein the calculated formula is as follows:
Wherein, For the average value of the actual current values of each monitoring device at different moments in the preset time period, the obtained expression is:
5. The intelligent management method for a monitoring device in a grassland according to claim 4, wherein the obtaining method of the usage time coefficient is as follows:
acquiring the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance of each monitoring device, and obtaining the use time coefficient of the monitoring device according to the total use duration, the preset usable duration, the average maintenance interval time and the time interval of the last maintenance of the current time distance, wherein the calculated formula is as follows:
Wherein Dsw is a use time coefficient, gd, fd, kl, kh is a total use time length, a preset usable time length, an average maintenance interval time and a time interval of last maintenance of a current time distance of the monitoring device, and a1 and a2 are preset influence factors.
6. The intelligent management method for monitoring equipment in a grassland according to claim 1, wherein the method for obtaining the quality coefficient according to the quality data comprises the steps of:
And carrying out normalization processing on the equipment current stability coefficient and the use time coefficient, and obtaining a quality coefficient according to the normalized equipment current stability coefficient and the normalized use time coefficient.
7. The intelligent management method for monitoring equipment in a grassland according to claim 1, wherein determining, according to a quality coefficient, the monitoring equipment that needs to be turned on for the target colony comprises:
The method comprises the steps that the required opening types of monitoring equipment and the opening quantity of each opening type of monitoring equipment of a target colony are determined, and the opening types and the corresponding preset opening quantity are used as preset opening types;
Starting the monitoring equipment corresponding to each starting type according to the sequence from the small quality coefficient to the large quality coefficient, and stopping starting other monitoring equipment of the type when the starting quantity of the monitoring equipment corresponding to each starting type reaches the corresponding preset starting quantity;
and starting all required monitoring equipment starting types and corresponding monitoring equipment starting quantity of the target colony house, and monitoring the target colony house.
8. A system for intelligent management of monitoring equipment in a grassland, the system comprising:
The dividing module: acquiring colony house state characteristics of a target colony house through monitoring equipment which operates currently, matching colony house state characteristic data with historical colony house state characteristic data, determining a first abnormal degree value of the target colony house, and dividing the state of the target colony house into an abnormal state and a normal state according to the first abnormal degree value of the target colony house;
an anomaly module: for a target colony in an abnormal state, obtaining an abnormal characteristic type and a second abnormal degree value corresponding to each abnormal characteristic type through a preset mode according to corresponding colony state characteristic data;
And the equipment opening module is used for: determining the required monitoring equipment opening types of the target colony and the monitoring equipment opening quantity of each opening type according to the abnormal characteristic type of the target colony and the second abnormal degree value corresponding to each abnormal characteristic type;
and a management module: acquiring quality data of each required monitoring device of the target colony, obtaining a quality coefficient according to the quality data, determining the monitoring device of the target colony, which needs to be opened, according to the quality coefficient, and managing the monitoring device of the target colony; the monitoring device quality data includes a device current stability factor and a usage time factor.
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