CN111695614A - Dynamic monitoring sensor layout and multi-source information fusion method and system - Google Patents
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Abstract
The embodiment of the invention provides a dynamic monitoring sensor layout and multi-source information fusion method and system. The method comprises the following steps: carrying out optimized layout on the environmental sensor and the physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane diagram and the relationship among the positions of the environmental sensor and the physiological sensor; performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion. The embodiment of the invention realizes reasonable layout of the sensors and performs three-level dynamic evaluation decision of 'cultured individual-cultured group-cultured population' on the living animals by utilizing the pre-constructed multi-source information fusion model.
Description
Technical Field
The invention relates to the technical field of cultivation and multi-source information fusion, in particular to a dynamic monitoring sensor layout and multi-source information fusion method and system.
Background
Currently, many researchers use multiple sensors to monitor and collect relevant data, and place the relevant sensors approximately according to subjective consciousness. However, the sensors placed at will are not accurate enough when monitoring and collecting data, so that on one hand, resource waste is caused, and more sensors are needed; on the other hand, data inaccuracy is caused, and problems such as redundancy of collected data also cause difficulty in subsequent data processing.
The existing methods for monitoring and acquiring related data of the sensors have some problems, so how to provide a method can realize reasonable layout of the sensors and perform three-level dynamic evaluation decision on cultured individuals, cultured groups and cultured populations by fusing multi-source information becomes a problem to be solved urgently.
Disclosure of Invention
It is an object of embodiments of the present invention to provide a dynamically monitored sensor layout and multi-source information fusion method and system that overcomes or at least partially addresses the above-mentioned problems.
In order to solve the above technical problem, in one aspect, an embodiment of the present invention provides a dynamic monitoring method for fusing sensor layout and multi-source information, including:
carrying out optimized layout on the environmental sensor and the physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor;
performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
Further, before the optimal layout of the environmental sensor and the physiological sensor according to the preset sensor layout model, the method further includes:
and reducing the dimensions of the three-dimensional space living environment and the three-dimensional geometrical characteristics of the living animals to obtain the two-dimensional plane map.
Further, the optimizing layout of the environmental sensor and the physiological sensor according to the preset sensor layout model specifically includes:
preliminarily matching the positions of the environmental sensor, the physiological sensor and the two-dimensional plane graph to obtain a matching set;
calculating to obtain the maximum monitoring ratio of the environmental sensor and the physiological sensor according to the matching set and the constraint variables;
and determining the optimized layout according to the maximum monitoring ratio of the sensors.
Further, before performing data fusion on the data passing the verification by using a preset multi-source information fusion model, the method further includes:
preliminarily fusing homogeneous sensor data according to the data space-time weight to obtain various homogeneous data;
and determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the various homogeneous data, and constructing to obtain the multi-source information fusion model.
Further, the preliminary fusion of the homogenous sensor data according to the data space-time weight specifically includes:
calculating according to the data space importance and the data time importance to obtain the data space-time weight, and performing data fusion processing on the homogeneous sensor according to the data space-time weight to obtain the various homogeneous data;
the data space importance is:
∑lnis tmTotal number, k, contained in the time data setm,nIs tmA certain data value l at a moment of timem,nThe number of occurrences;
the data time importance is:
Yμ,vis v data contained in a mu time period, n is the number of sensors, k'm,nIs a data value l of the mu time periodm,nThe number of occurrences;
the data space-time weight reconstruction model is as follows:
further, the determining an optimization weight according to the relative time relationship to perform heterogeneous sensor data fusion on the multiple homogeneous data specifically includes:
setting a European space, wherein an optimization variable, a weight optimizer and a classification optimizer exist;
converting the relative time relation required by the optimal path searched by the weight optimizer into the optimizing weight;
and the classification optimizing device classifies the optimizing variables and performs heterogeneous sensor data fusion according to classification results and the optimizing weight.
Further, the three-level dynamic evaluation decision specifically includes:
evaluation of health of breeding individuals: performing data fusion on single animal physiological information data according to the multi-source information fusion model to obtain individual data fusion information and an individual health assessment decision;
evaluating the situation of the breeding population: carrying out data fusion on the individual data fusion information and the physiological information of the current animal group according to the multi-source information fusion model to obtain group data fusion information and a group situation evaluation decision;
and (3) breeding population prediction and evaluation: and performing data fusion on the population data fusion information and the processed environmental sensor data according to the multi-source information fusion model to obtain a population prediction evaluation decision.
Further, before the forecasting evaluation of the breeding population, the method further comprises the following steps:
and pre-classifying the group data fusion information, and performing data fusion according to the data weight sequence of different groups.
Further, still include:
and after each stage of fusion is finished, performing data quality evaluation and data diagnosis on the fusion information data, and if the data quality evaluation and the data diagnosis pass, using the fusion information data as the next stage of input data.
In another aspect, an embodiment of the present invention provides a system for fusing sensor layout and multi-source information for dynamic monitoring, including:
an optimization module: the system comprises an environment sensor, a physiological sensor, a sensor layout model and a control module, wherein the environment sensor and the physiological sensor are optimally distributed according to the preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor;
an evaluation module: the system comprises an environment sensor, a physiological sensor, a multi-source information fusion model and a three-level dynamic evaluation decision-making module, wherein the environment sensor and the physiological sensor are used for carrying out periodic information extraction data on the optimized environment sensor and the optimized physiological sensor, and carrying out data fusion on the data passing the verification by utilizing the preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
According to the dynamic monitoring sensor layout and multi-source information fusion method and system provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and the three-level dynamic evaluation decision on cultured individuals, cultured groups and cultured populations is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a dynamic monitoring sensor layout and multi-source information fusion method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of generating a two-dimensional plane by dimension reduction according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a multi-sensor optimization layout according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a decision flow of three-level dynamic evaluation according to an embodiment of the present invention;
FIG. 5 is a flow chart of a multi-sensor data fusion process provided by an embodiment of the present invention;
fig. 6 is a schematic flow chart of a dynamically monitored sensor layout and multi-source information fusion system according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart of another dynamically monitored sensor layout and multi-source information fusion system according to an embodiment of the present invention;
fig. 8 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a dynamic monitoring sensor layout and multi-source information fusion method, fig. 1 is a schematic flow chart of the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, and as shown in fig. 1, the method includes:
s101, carrying out optimized layout on an environmental sensor and a physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor;
specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, in step S101, basic information of a farm, such as an area and a shape, basic information of a living animal, such as a number and a size, is collected, and a two-dimensional plan is obtained by reducing dimensions according to a three-dimensional space living environment of the living animal and a three-dimensional geometric feature of the living animal; optimizing and distributing the environmental sensors of the farm by using a sensor distribution model which is constructed in advance, calculating the number of animals to be monitored by using a preset animal density model based on farm information, and distributing physiological sensors of the batch of living animals by using the sensor distribution model which is constructed in advance;
the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor, certain constraint is added to the sensor by the sensor layout model, the situation that a plurality of sensors monitor the same grid or the sensors do not monitor the whole two-dimensional plane completely but the monitoring ratio is more than 100 percent is avoided, and comprehensively considers the influence of factors such as the size of the breeding environment, the size of animals, the number of animals, a sensor monitoring mechanism and the like on the layout of the physiological sensors of the living animals, obtaining a matching set by preliminarily matching the positions of the environmental sensor and the physiological sensor with the two-dimensional plane graph, and calculating to obtain the maximum monitoring ratio of the environmental sensor and the physiological sensor according to the matching set and the constraint variable, and obtaining the optimized layout of the sensors according to the maximum monitoring ratio of the sensors.
S102, performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, in step S102, periodic information extraction is performed on data of the environmental sensor and the physiological sensor based on the pre-constructed sensor layout model layout, and data fusion is performed on the data passing verification by using the pre-constructed multi-source information fusion model to perform three-level dynamic evaluation decision on the cultured individuals, the cultured population and the cultured population;
preliminarily fusing homogeneous sensor data according to the data space-time weight to obtain various homogeneous data; then determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the multiple homogeneous data, and constructing and obtaining the multi-source information fusion model according to the method;
and performing multi-source information fusion on the measured data of the environmental sensor and the physiological sensor after the optimized layout according to a preset multi-source information fusion model, and reversely verifying whether the layout of the environmental sensor and the physiological sensor is reasonable or not, wherein the data processed by the multi-source information fusion model can be input by a user.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and three-level dynamic evaluation decision making is carried out on cultured individuals, cultured groups and cultured populations.
Based on any one of the above embodiments, further, before performing the optimized layout on the environmental sensor and the physiological sensor according to the preset sensor layout model, the method further includes:
and reducing the dimensions of the three-dimensional space living environment and the three-dimensional geometrical characteristics of the living animals to obtain the two-dimensional plane map.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the above embodiment, fig. 2 is a two-dimensional plane graphic representation purpose generated by dimension reduction provided by the embodiment of the present invention, as shown in fig. 2, basic information such as area, shape, etc. of a farm is acquired, basic information such as number, size, etc. of living animals is acquired, a two-dimensional grid plan is obtained by dimension reduction according to a three-dimensional living environment of living animals and three-dimensional geometric features of living animals, the dimension reduction method first meshes the three-dimensional living environment of the farm and a 3D model of living animals, i.e. three-dimensional geometric features of living animals, then establishes a mapping relationship between 3D grids and 2D images, adds slits to the three-dimensional grids, expands a disc structure divided into one piece, maps X, Y, Z three-dimensional coordinates of the 3D space to U, V two-dimensional coordinates of the 2D images, therefore, dimension reduction processing is realized on the culture space and the living animal 3D model. The dimension reduction method can enable the three-dimensional model and the two-dimensional plane to establish direct mapping relation, and the positions of the sensors arranged in the two-dimensional plane map can be directly projected to the three-dimensional space to represent the real position.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the three-dimensional model is subjected to two-dimensional processing by reducing the dimensions of the three-dimensional space living environment and the three-dimensional geometrical characteristics of the living animals, the processing difficulty is reduced, and the early preparation is made for reasonable layout of the sensors.
Based on any one of the above embodiments, further, the performing optimized layout on the environmental sensor and the physiological sensor according to the preset sensor layout model specifically includes:
preliminarily matching the positions of the environmental sensor and the physiological sensor with the two-dimensional plane graph to obtain a matching set;
calculating to obtain the maximum monitoring ratio of the environmental sensor and the physiological sensor according to the matching set and the constraint variables;
and determining the optimized layout according to the maximum monitoring ratio of the sensors.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the above embodiment, fig. 3 is a schematic diagram of a flow of an optimized layout process of a multi-sensor provided in the embodiment of the present invention, as shown in fig. 3, a three-dimensional space living environment and three-dimensional geometric features of living animals are reduced in dimension, after a two-dimensional plane diagram is obtained, the two-dimensional plane diagram is divided into a plurality of key grids, and a grid set is defined as:
and placing the required number of sensors at any position of the two-dimensional plane graph, wherein the positions of the sensors are set as follows:
according to the monitoring mechanism of various sensors and the characteristics of the divided areas, the positions of the sensors are preliminarily matched with the key grids, and the matching result is defined as:
f(R,V)＝{(R(1,1),V(1,1)),(R(2,2),V(2,2)),…(R(i,j),V(i,j))}
wherein (i ═ 1,2 …, m), (j ═ 1,2, …, n);
assume that the monitoring ranges of k sensors are S ═ S, respectively1,S2,…，SkH, then kThe maximum monitoring ratio that can be achieved by the sensor is:
wherein ∑ S represents the maximum monitoring ratio of k sensors, k is the number of sensors measuring the same parameter, STFor the total monitoring grid area, as a constraint variable;
in particular, the situation that excessive monitoring occurs in the arrangement process of the same type of sensors is avoided, namely, a plurality of sensors monitor the same grid or the sensors do not monitor the whole two-dimensional plane, but the monitoring ratio is larger than 100%. Therefore, certain constraint needs to be added, and a constraint variable is specifically added to optimize the layout:
wherein ScRepresenting a grid area simultaneously monitored by k sensors of the same type.
The calculation result of the model is specially limited to obtain the optimal sensor number and the layout position, and the judgment is carried out according to the rules in the table 1:
TABLE 1 corresponding layout level determination for different layout rules
| Layout rules | Maximum monitoring ratio ∑ V | Number of sensors | Level of |
| 1 | 90％-100％ | Optimum number | Optimization of |
| 2 | 80％-90％ | Number of excellent | Superior food |
| 3 | 60％-80％ | Sub-optimal number | Sub-optimal |
| 4 | ＜60％ | Number of differences | Difference (D) |
For the layout of the living animal physiological sensor, factors such as the size of a breeding environment, the size of an animal, the number of animals, a sensor monitoring mechanism and the like need to be comprehensively considered. In addition to following the above-described sensor layout rules, it is noted that: the physiological sensor has a specific monitoring mechanism, and is mostly placed on the abdomen when monitoring the blood sugar of a living animal, on the neck when monitoring the pulse and the heart rate, and on the legs when monitoring the exercise amount. The sensors are arranged on the animal body, and the monitoring range needs to be concentrated as much as possible so as to achieve the aim of accurate data.
Dividing the living animal two-dimensional graph obtained by dimension reduction into specific functional areas according to the rule, and directly matching the specific sensors required to be used with the functional areas according to the working principle of the sensors. The requirement on the layout level is that the number of the set wearable sensors is suitable for basically covering a specific functional area, and the preset functional area range is avoided being exceeded; the number of the set embedded or implanted sensors is required to be selected according to the size and the health degree of the animal due to the damage to the animal body, and the number of the set embedded or implanted sensors for monitoring the same index is not more than three at most when the embedded or implanted sensors reach the maximum monitoring range.
Setting the sampling area as x, the sampling number as m, the sampling animal number as n and the breeding range as STThe method is characterized in that the area occupied by the adult sheep is about 1.5-2 square meters, m squares are taken in the monitoring area according to the quincunx, and the sampling areas are equal when the length and the width of each square are consistent. The total number of the living animals in the sample prescription is n, and the average floor area of the individual animals can be calculated
The special range of motion of the cultured animals is 2 rho0. Then the total breeding range is divided with the average moving area of the individual animals to obtain the density of the animals needing to be monitored in the breeding range
Rho is the density of the animals needing to be monitored by installing a sensor in a specific monitoring range, and the total information of the animals in the breeding area can be analyzed based on the density.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the sensors are reasonably arranged by reasonably setting the sensor layout model and by arranging various sensors of the sensor layout model.
Based on any one of the above embodiments, further, before performing data fusion on the data passing the verification by using a preset multi-source information fusion model, the method further includes:
preliminarily fusing homogeneous sensor data according to the data space-time weight to obtain various homogeneous data;
and determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the various homogeneous data, and constructing to obtain the multi-source information fusion model.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, homogeneous sensor data is preliminarily fused according to data space-time weight to obtain various homogeneous data; then determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the multiple homogeneous data, and constructing and obtaining the multi-source information fusion model according to the method;
the multi-source information fusion model also has the functions of data quality evaluation and diagnosis, and comprises the steps of checking whether the data of the previous stage is input or missing; fused data distortion analysis, and the like. The specific process comprises the following steps: the multi-source information fusion model sets self-adaptive dynamic data extraction time to extract sensor data for three-level fusion, fused data are transmitted to a data quality evaluation and diagnosis center for diagnosis after the fusion of each level is finished, and if the fused data are transmitted without errors in diagnosis, the fused data are used as next-level input data; if the data is not received within overtime in a certain level of decision, the data of the sensor is extracted again, if the data is not received successfully, an alarm is sent to a user and is solved by the user, and other levels automatically sleep in a user processing stage and work again after receiving effective data; and the data quality evaluation and diagnosis center compares the fusion data with the true data value, performs weight optimization adjustment on the fusion data with the deviation exceeding a preset threshold value, and then re-fuses the fusion data until the fusion data meets the requirements and then inputs the fusion data into a next-stage decision model.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the data is processed by reasonably constructing the multi-source information fusion model, so that three-level dynamic evaluation decision on cultured individuals, cultured groups and cultured populations is realized.
Based on any of the above embodiments, further, the preliminary fusion of the homogenous sensor data according to the data space-time weight specifically includes:
calculating according to the data space importance and the data time importance to obtain the data space-time weight, and performing data fusion processing on the homogeneous sensor according to the data space-time weight to obtain the various homogeneous data;
the data space importance is:
∑lnis tmTotal number, k, contained in the time data setm,nIs tmA certain data value l at a moment of timem,nThe number of occurrences;
the data time importance is:
Yμ,vis v data contained in a mu time period, n is the number of sensors, k'm,nIs a data value l of the mu time periodm，nThe number of occurrences;
the data space-time weight reconstruction model is as follows:
in particular, according to the dynamic monitoring sensor layout and multi-source information fusion method of the above embodiment,
since the monitored data remains substantially unchanged for a certain period of time, given a data threshold φ, each time the monitored data exceeds the threshold, the data is taken as an inflection point, and the inflection point is taken as an initial value of the next stage.
Therefore, the spatio-temporal weight reconstruction model is:
step 2, assuming that there are n sensors, at tmThe data set monitored at a moment is l ═ lm，1，lm，2，…，lm，nThus tmA certain data value l at a moment of timem，nNumber of occurrences km，nRatio to the entire data set as dataImportance of space
Of which ∑ lnIs tmThe total number of time of day data sets. Particularly, in the process of distinguishing the spatial importance, if the number of times of occurrence of a certain numerical value is large, the measurement results of a plurality of sensors at the same time are more real.
Step 3, making the importance of data time
Wherein Y isμ，νDenotes v data contained in a period of time μ, n is the number of sensors, k'm，nIs a data value l of the mu time periodm，nThe number of occurrences. Particularly, in the process of judging the time importance, if the number of times of occurrence of a certain numerical value is large, the change of the measurement results of a plurality of sensors in a certain time is not obvious.
Step 4, the final data space-time weight reconstruction model is
And (3) carrying out homogeneous sensor data fusion processing by using the weight obtained by the formula: setting the calculated w of the modelQD＝(w1，w2，…，wn) The homogeneous sensor monitoring data is a ═ a (a)1，a2，…，an) If the result x is w1a1+w2a2+…+wnan。
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the fusion processing of the homogeneous sensor data is carried out through the weight, so that the fusion of the homogeneous sensor data is realized.
Based on any of the above embodiments, further, the determining an optimization weight according to a relative time relationship to perform heterogeneous sensor data fusion on the multiple homogeneous data specifically includes:
setting a European space, wherein an optimization variable, a weight optimizer and a classification optimizer exist;
converting the relative time relation required by the optimal path searched by the weight optimizer into the optimizing weight;
and the classification optimizing device classifies the optimizing variables and performs heterogeneous sensor data fusion according to classification results and the optimizing weight.
In the embodiment of the invention, heterogeneous sensor data fusion is carried out after the optimization weight is determined based on the relative time relationship, firstly, an N × D European space is set, and the state of the variable to be optimized is Yi＝(yi1,yi2,…,yiD) The state of the weight optimizer i is Xi＝(xi1,xi2,…,xiD) The state of the classification optimizer j is Xj＝(xj1,xj2,…,xjD) And an output optimizer k. Wherein xidThe position of the nth optimizer in the D (D is 1,2, …, D) dimension variable space to be optimized;
the optimizers and the variables to be optimized are in one-to-one correspondence, and the initial Euclidean distances between each optimizer and the corresponding variables to be optimized are equal. Sequencing variables to be optimized through priori knowledge to obtain initial weight thetan。
Order to
Then l (X)i,Yi) For the optimal path of the optimizer i, the basic optimizing step size of the optimizer is α, and each optimizing requires time t0And the optimizing device i returns to the initial position after the initial position moves forward a step length in any p directions, selects the direction closest to the target variable value and moves forward to the next step length position, and then repeats the steps until the variable state position corresponding to the optimizing device i is reached, and records the time t required by the optimal path.
Converting the relative time relation required by each optimizer i to find the optimal path into the optimization weight wn＝(w1,w2,…,wD)。
The classification optimizing device j corresponds to each variable Y to be optimizediThe classification optimizing device j classifies the data of each variable according to the preset classification rule, and the optimizing weight obtained by the weight optimizing device through optimizing is finally transmitted to the output optimizing device k through the formula
Calculating to obtain an output decision, wherein
The boundary values corresponding to the variables.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, heterogeneous sensor data fusion is carried out after the optimization weight is determined based on the relative time relationship, so that the fusion of the heterogeneous sensor data is realized.
Based on any of the above embodiments, further, the three-level dynamic evaluation decision specifically includes:
evaluation of health of breeding individuals: performing data fusion on single animal physiological information data according to the multi-source information fusion model to obtain individual data fusion information and an individual health assessment decision;
evaluating the situation of the breeding population: carrying out data fusion on the individual data fusion information and the physiological information of the current animal group according to the multi-source information fusion model to obtain group data fusion information and a group situation evaluation decision;
and (3) breeding population prediction and evaluation: and performing data fusion on the population data fusion information and the processed environmental sensor data according to the multi-source information fusion model to obtain a population prediction evaluation decision.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, fig. 4 is a schematic diagram of a three-level dynamic evaluation decision-making process provided by the embodiment of the present invention, and as shown in fig. 4, the health evaluation of cultured individuals: the information monitored by each sensor is used as input data, data fusion decision is carried out on the data monitored by the physiological information of a single animal by using the method, decision evaluation on the health state can be carried out according to the body temperature, the blood oxygen saturation and the like of the animal, and decision evaluation on the behavior state can be carried out according to the heart rate, the motion amount and the like;
evaluating the situation of the breeding population: the number of animals in the breeding environment is monitored by the sensor layout method, information obtained by data fusion of first-level animal individuals is used as second-level input data, data fusion processing is carried out on physiological information of the batch of animal groups, and health conditions of the animal groups in the breeding environment and behavior information of the groups in monitoring time can be obtained through decision making;
and (3) breeding population prediction and evaluation: and simultaneously using the second-stage data fusion information and the processed environmental sensor data information as third-stage input data, and then processing by using a preset data fusion method to obtain the breed animal population condition prediction under the condition of the environmental change of the farm.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and three-level dynamic evaluation decision making is carried out on cultured individuals, cultured groups and cultured populations.
Based on any one of the above embodiments, further, before the forecasting evaluation of the breeding population, the method further includes:
and pre-classifying the group data fusion information, and performing data fusion according to the data weight sequence of different groups.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, particularly when the dynamic evaluation of the breeding population is performed, in different environments of a farm, deviations of physiological states of living animals of different populations affect the accuracy of fusion data, so that the model can pre-classify the data transmitted in the second stage, perform weight sorting on the data importance of different populations, and perform data fusion processing.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the accuracy of three-level dynamic evaluation decision of breeding individuals, breeding groups and breeding populations is further improved by pre-classifying the population data fusion information.
Based on any one of the above embodiments, further, the method further includes:
and after each stage of fusion is finished, performing data quality evaluation and data diagnosis on the fusion information data, and if the data quality evaluation and the data diagnosis pass, using the fusion information data as the next stage of input data.
Specifically, according to the dynamic monitoring sensor layout and multi-source information fusion method of the embodiment, because the fusion result of each stage of data of the three-stage decision model is input as the next stage of data, in order to ensure normal work, the multi-source information fusion model has data quality evaluation and diagnosis functions, including checking whether the previous stage of data is input or missing; fused data distortion analysis, and the like. The specific process comprises the following steps: the multi-source information fusion model sets self-adaptive dynamic data extraction time to extract sensor data for three-level fusion, fused data are transmitted to a data quality evaluation and diagnosis center for diagnosis after the fusion of each level is finished, and if the fused data are transmitted without errors in diagnosis, the fused data are used as next-level input data; if the data is not received within overtime in a certain level of decision, the data of the sensor is extracted again, if the data is not received successfully, an alarm is sent to a user and is solved by the user, and other levels automatically sleep in a user processing stage and work again after receiving effective data; and the data quality evaluation and diagnosis center compares the fusion data with the true data value, performs weight optimization adjustment on the fusion data with the deviation exceeding a preset threshold value, and then re-fuses the fusion data until the fusion data meets the requirements and then inputs the fusion data into a next-stage decision model.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the fused information data is subjected to data quality evaluation and data diagnosis, so that the inspection of each level of data is realized, and the accuracy of three-level dynamic evaluation decision of 'breeding individuals-breeding groups-breeding populations' is further improved.
Further, on the basis of the above embodiments, fig. 5 is a flow chart schematic diagram of a multi-sensor data fusion flow chart provided by the embodiment of the present invention, and in the third-level breeding risk prediction evaluation, the embodiment of the present invention obtains the optimization weights w of the environmental temperature, the relative humidity, the animal skin temperature, the heart rate, and the likenIf the value of the decision value Y is (0.4,0.3,0.2,0.1), the decision value Y for the breeding risk decision to be safe is in the range of
Therefore, when the decision value Y is between 26.2 and 53.3, the output decision of the output optimizer k is safe. In particular, in order to avoid that the data calculated by the decision value accords with the safety decision, a certain evaluation factor reaches the severe condition. A decision protection mechanism needs to be added into the classification optimizer, and if at least one variable data in a certain decision is bad, the classification optimizer does not perform subsequent decision calculation, but directly outputs a bad decision, as shown in table 2.
Table 2 is a table of output decision rules
Further, on the basis of the foregoing embodiments, an embodiment of the present invention provides a dynamically monitored sensor layout and multi-source information fusion system, where the system is configured to execute the dynamically monitored sensor layout and multi-source information fusion method in the foregoing method embodiments. Fig. 6 is a schematic flow diagram of a dynamically monitored sensor layout and multi-source information fusion system according to an embodiment of the present invention, and as shown in fig. 6, the system includes: an optimization module 601 and an evaluation module 602; wherein,
the optimization module 601: the system comprises an environment sensor, a physiological sensor, a sensor layout model and a control module, wherein the environment sensor and the physiological sensor are optimally distributed according to the preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor;
specifically, according to the dynamic monitoring sensor layout and multi-source information fusion system of the embodiment, in the optimization module 601, basic information of a farm, such as an area and a shape, and basic information of living animals, such as a number and a size, are collected, and a two-dimensional plan is obtained by reducing dimensions according to a three-dimensional space living environment of the living animals and three-dimensional geometric features of the living animals; the optimization module 601 utilizes a pre-constructed sensor layout model to perform optimized layout on the farm environmental sensors, calculates the number of animals to be monitored based on farm information by utilizing a preset animal density model, and then utilizes the pre-constructed sensor layout model to perform physiological sensor layout on the batch of living animals;
the sensor layout model adds certain constraint on the sensors to avoid the situation that a plurality of sensors monitor the same grid or the sensors do not monitor the whole two-dimensional plane completely but the monitoring ratio is more than 100%, and comprehensively considers the influences of factors such as the size of a breeding environment, the size of animals, the number of animals, the monitoring mechanism of the sensors and the like on the layout of the physiological sensors of living animals, and obtains a matching set by preliminarily matching the positions of the environmental sensors and the physiological sensors with the two-dimensional plane graph; calculating to obtain the maximum monitoring ratio of the environmental sensor and the physiological sensor according to the matching set and the constraint variables; and obtaining the sensor layout model according to the maximum monitoring ratio of the sensor.
The evaluation module 602: the system comprises an environment sensor, a physiological sensor, a multi-source information fusion model and a three-level dynamic evaluation decision-making module, wherein the environment sensor and the physiological sensor are used for carrying out periodic information extraction data on the optimized environment sensor and the optimized physiological sensor, and carrying out data fusion on the data passing the verification by utilizing the preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
Specifically, according to the dynamically monitored sensor layout and multi-source information fusion system of the above embodiment, in the evaluation module 602, periodic information extraction is performed on data of the environmental sensor and the physiological sensor based on the pre-constructed sensor layout model layout, and the evaluation module 602 performs data fusion on the verified data by using the pre-constructed multi-source information fusion model to perform three-level dynamic evaluation decision on the cultured individuals, the cultured population and the cultured population;
preliminarily fusing homogeneous sensor data according to the data space-time weight to obtain various homogeneous data; then determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the multiple homogeneous data, and constructing and obtaining the multi-source information fusion model according to the method;
the evaluation module 602 performs multi-source information fusion on the measured data of the environmental sensor and the physiological sensor after the optimized layout according to a preset multi-source information fusion model, and can verify whether the layout of the environmental sensor and the physiological sensor is reasonable or not in turn.
It should be noted that the system according to the embodiment of the present invention may be used to implement the technical solution of the embodiment of the dynamic monitoring sensor layout and multi-source information fusion method shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
According to the dynamic monitoring sensor layout and multi-source information fusion system provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and three-level dynamic evaluation decision making is carried out on cultured individuals, cultured groups and cultured populations.
Based on any one of the above embodiments, further, the method further includes: the system comprises an interaction module, a knowledge base and a quality evaluation and diagnosis center module.
An interaction module: the user can input data through the interaction module, observe and output decision results, and display the explanation of the conclusion and the solving process according to the question of the user.
A knowledge processing module: the knowledge processing module can expand and modify the content in the knowledge base and can also realize the automatic learning function.
The quality evaluation and diagnosis center module: the system is used for transmitting the relevant basic knowledge to the quality evaluation and diagnosis center module after the relevant basic knowledge is transmitted to the knowledge processing module for processing, qualitatively evaluating the acquired expert knowledge, determining the applicability, performing feedback correction according to the actual operation condition, retransmitting the corrected data to the knowledge acquisition and processing system, and transmitting the corrected data to the knowledge base by the system for storage and standby;
in the multi-sensor data fusion process, a quality evaluation and diagnosis center module dynamically adjusts data extraction time according to data (extraction interval time is adaptively changed according to dynamic characteristics of monitoring data) to extract multi-sensor data obtained after layout. If the effective data input still does not exist in the system after the extraction period is exceeded, the system sends a dormancy application to the user terminal, and the user can select whether the system is dormant or not, check the reason that the data is not input in time and enable the system to extract and process the sensor data again;
the quality evaluation and diagnosis center module is specifically used for checking whether the previous-stage data is input or missing; fused data distortion analysis, and the like. The specific process comprises the following steps: the multi-source information fusion model sets self-adaptive dynamic data extraction time to extract sensor data for three-level fusion, fused data are transmitted to a data quality evaluation and diagnosis center module for diagnosis after the fusion of each level is finished, and if the fused data are transmitted without errors in diagnosis, the fused data are used as next-level input data; if the data is not received within overtime in a certain level of decision, the data of the sensor is extracted again, if the data is not received successfully, an alarm is sent to a user and is solved by the user, and other levels automatically sleep in a user processing stage and work again after receiving effective data; and the data quality evaluation and diagnosis center compares the fusion data with the true data value, performs weight optimization adjustment on the fusion data with the deviation exceeding a preset threshold value, and then re-fuses the fusion data until the fusion data meets the requirements and then inputs the fusion data into a next-stage decision model.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and three-level dynamic evaluation decision making is carried out on cultured individuals, cultured groups and cultured populations.
Based on any of the above embodiments, further, fig. 7 is a schematic flow diagram of another dynamically monitored sensor layout and multi-source information fusion system provided in the embodiment of the present invention, as shown in fig. 7, the system includes: the system comprises an input/output module, a comprehensive database, an interpreter, a knowledge base, a knowledge acquisition and processing system and a data quality evaluation and diagnosis center; wherein,
an input-output module: the user can input data and observe and output decision results through the module.
A comprehensive database: the method is specially used for storing raw data, intermediate results and final conclusions required in the reasoning process and is often used as a temporary storage area.
An interpreter: the conclusion and the solving process can be explained according to the questions of the user.
Knowledge base, knowledge acquisition and processing system: the system is provided with relevant basic knowledge, the knowledge is acquired and processed and then stored in a knowledge base, and the knowledge acquisition and processing system can expand and modify the content in the knowledge base and can also realize the automatic learning function.
Data quality assessment and diagnosis center: the functions of the device comprise that,
(1) after the relevant basic knowledge is transmitted to a knowledge acquisition and processing system for processing, the relevant basic knowledge is transmitted to a data quality evaluation and diagnosis center, the acquired expert knowledge is qualitatively evaluated, the applicability is determined, feedback correction is carried out according to the actual operation condition, the corrected data is retransmitted to the knowledge acquisition and processing system, and the corrected data is transmitted to a knowledge base by the system for storage and standby;
(2) in the multi-sensor data fusion process, a data quality evaluation and diagnosis center dynamically adjusts data extraction time (extraction interval time is adaptively changed according to dynamic characteristics of monitored data) according to data to extract multi-sensor data obtained after layout. If the effective data input in the system is still not available after the extraction period is exceeded, the system sends a dormancy application to the user terminal, and the user can select whether the system is dormant or not, check the reason that the data is not input in time and enable the system to extract and process the sensor data again.
According to the dynamic monitoring sensor layout and multi-source information fusion method provided by the embodiment of the invention, the preset sensor layout model is adopted to layout the sensors, and the obtained data is processed through the multi-source information fusion model, so that the sensors are reasonably arranged, and three-level dynamic evaluation decision making is carried out on cultured individuals, cultured groups and cultured populations.
An example is as follows:
fig. 8 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 8, the electronic device may include: a processor (processor)801, a communication Interface (Communications Interface)802, a memory (memory)803 and a communication bus 804, wherein the processor 801, the communication Interface 802 and the memory 803 complete communication with each other through the communication bus 804. The processor 801 may call logic instructions in the memory 803 to perform the following method: carrying out optimized layout on the environmental sensor and the physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph, the positions of the environmental sensor and the physiological sensor; performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
In addition, the logic instructions in the memory 803 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: carrying out optimized layout on the environmental sensor and the physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph, the positions of the environmental sensor and the physiological sensor; performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A dynamic monitoring sensor layout and multi-source information fusion method is characterized by comprising the following steps:
carrying out optimized layout on the environmental sensor and the physiological sensor according to a preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph and the relationship between the positions of the environmental sensor and the physiological sensor;
performing periodic information extraction data on the optimized environmental sensor and physiological sensor, and performing data fusion on the data passing the verification by using a preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
2. The method of claim 1, wherein before the optimal layout of the environmental sensors and the physiological sensors according to the preset sensor layout model, the method further comprises:
and reducing the dimensions of the three-dimensional space living environment and the three-dimensional geometrical characteristics of the living animals to obtain the two-dimensional plane map.
3. The method for fusing the sensor layout and the multi-source information for dynamic monitoring according to claim 2, wherein the optimal layout of the environmental sensor and the physiological sensor according to the preset sensor layout model specifically comprises:
preliminarily matching the positions of the environmental sensor, the physiological sensor and the two-dimensional plane graph to obtain a matching set;
calculating to obtain the maximum monitoring ratio of the environmental sensor and the physiological sensor according to the matching set and the constraint variables;
and determining the optimized layout according to the maximum monitoring ratio of the sensors.
4. The method of claim 1, wherein before the fusing the data passing the verification by using a preset multi-source information fusion model, the method further comprises:
preliminarily fusing homogeneous sensor data according to the data space-time weight to obtain various homogeneous data;
and determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the various homogeneous data, and constructing to obtain the multi-source information fusion model.
5. The method for fusing sensor layout and multi-source information for dynamic monitoring according to claim 4, wherein the preliminary fusing of the homogenous sensor data according to the data spatiotemporal weight specifically comprises:
calculating according to the data space importance and the data time importance to obtain the data space-time weight, and performing data fusion processing on the homogeneous sensor according to the data space-time weight to obtain the various homogeneous data;
the data space importance is:
∑lnis tmTotal number, k, contained in the time data setm,nIs tmA certain data value l at a moment of timem,nThe number of occurrences;
the data time importance is:
Yμ,νv data included in a mu time period, n is the number of sensors, k'm,nIs a data value l of the mu time periodm,nThe number of occurrences;
the data space-time weight reconstruction model is as follows:
6. the method of claim 4, wherein the determining optimization weights according to the relative time relationship to perform heterogeneous sensor data fusion on the plurality of homogeneous data comprises:
setting a European space, wherein an optimization variable, a weight optimizer and a classification optimizer exist;
converting the relative time relation required by the optimal path searched by the weight optimizer into the optimizing weight;
and the classification optimizing device classifies the optimizing variables and performs heterogeneous sensor data fusion according to classification results and the optimizing weight.
7. The dynamically monitored sensor layout and multi-source information fusion method according to claim 1, wherein the three-level dynamic evaluation decision specifically comprises:
evaluation of health of breeding individuals: performing data fusion on single animal physiological information data according to the multi-source information fusion model to obtain individual data fusion information and an individual health assessment decision;
evaluating the situation of the breeding population: carrying out data fusion on the individual data fusion information and the physiological information of the current animal group according to the multi-source information fusion model to obtain group data fusion information and a group situation evaluation decision;
and (3) breeding population prediction and evaluation: and performing data fusion on the population data fusion information and the processed environmental sensor data according to the multi-source information fusion model to obtain a population prediction evaluation decision.
8. The dynamic monitoring sensor layout and multi-source information fusion method of claim 7, further comprising, before the estimation of the breeding population prediction:
and pre-classifying the group data fusion information, and performing data fusion according to the data weight sequence of different groups.
9. The method of claim 7, further comprising:
and after each stage of fusion is finished, performing data quality evaluation and data diagnosis on the fusion information data, and if the data quality evaluation and the data diagnosis pass, using the fusion information data as the next stage of input data.
10. A dynamically monitored sensor layout and multi-source information fusion system, comprising:
an optimization module: the system comprises an environment sensor, a physiological sensor, a sensor layout model and a control module, wherein the environment sensor and the physiological sensor are optimally distributed according to the preset sensor layout model; the sensor layout model is constructed based on a two-dimensional plane graph, the positions of the environmental sensor and the physiological sensor;
an evaluation module: the system comprises an environment sensor, a physiological sensor, a multi-source information fusion model and a three-level dynamic evaluation decision-making module, wherein the environment sensor and the physiological sensor are used for carrying out periodic information extraction data on the optimized environment sensor and the optimized physiological sensor, and carrying out data fusion on the data passing the verification by utilizing the preset multi-source information fusion model to obtain a three-level dynamic evaluation decision; the multi-source information fusion model is obtained based on combination of homogeneous sensor data fusion and heterogeneous sensor data fusion.
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| CN109357696A (en) * | 2018-09-28 | 2019-02-19 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multiple Source Sensor information merges closed loop test framework |
| CN111178617A (en) * | 2019-12-24 | 2020-05-19 | 嘉兴恒创电力设计研究院有限公司 | Multi-sensor management method based on perception decision guidance |
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| CN114113487A (en) * | 2021-12-02 | 2022-03-01 | 江苏云聚汇科技有限公司 | Volatile organic compound online monitoring system and method |
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