CN117689207B - Risk assessment table generation method, system, computer equipment and storage medium - Google Patents
Risk assessment table generation method, system, computer equipment and storage medium Download PDFInfo
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- 238000012502 risk assessment Methods 0.000 title claims abstract description 99
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- 230000009746 freeze damage Effects 0.000 claims abstract description 274
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- 108010053481 Antifreeze Proteins Proteins 0.000 description 1
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Abstract
The application provides a risk assessment table generation method, a system, computer equipment and a storage medium, wherein an environmental data acquisition module acquires historical soil, crop and geological data of each detection point; the AND or graph construction module constructs a spatial relationship and or graph according to the spatial relationship between each detection point and the detection area, and constructs a causal relationship and or graph according to the historical data and the historical freeze injury event of each node in the spatial relationship and or graph; the risk probability determining module determines the prediction probability of the freezing injury event in the detection area according to the causality and the graph or the historical probability; the freeze injury loss determination module determines a total loss value of the freeze injury event according to the prediction probability and the sub-loss value of the freeze injury event of each detection point; the risk assessment table generation module generates a freeze injury risk assessment table according to the prediction probability and the total loss value of the freeze injury event. By adopting the method, the accuracy of the obtained risk assessment table is improved, and the labor cost required by generating the risk assessment table is reduced.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a risk assessment table generating method, a risk assessment table generating system, a risk assessment table generating computer device, and a risk assessment table storage medium.
Background
The freeze injury is one of agricultural meteorological disasters, namely, the crop is frozen at a low temperature below 0 ℃ and is damaged by the freeze injury. The common occurrence of the frost damage of overwintering crops, the frost damage of fruit trees, the frost damage of economic forests and the like. Therefore, in order to avoid damage to crops by freeze injury, it is necessary to evaluate the risk of freeze injury that may occur in a crop area and then to conduct freeze injury protection in advance according to the evaluation result.
In the prior art, when evaluating the possible freeze injury risk of a crop area, management staff usually uses the risk evaluation experience of the management staff to subjectively evaluate the freeze injury risk of the area according to the area information of the area where the farmland is located, and then records the evaluation result manually. However, in the research, it is found that due to experience and professional ability of the manager, defects may exist, when the manager performs the freeze injury risk assessment on the area, the obtained freeze injury risk assessment result and the risk assessment table obtained by recording may be inaccurate, and meanwhile, due to the fact that the manager performs the risk assessment based on the area information of the area where the farmland is located, objective quantitative indexes are lacking, so that accuracy of the risk assessment result and the risk assessment table obtained by recording may be reduced. In addition, the risk assessment and the recording of the assessment result are manually performed by the manager, which also increases the labor cost when performing the freeze injury risk assessment and recording.
Disclosure of Invention
Accordingly, the present invention is directed to a risk assessment table generating method, system, computer device and storage medium, so as to improve the accuracy of the obtained risk assessment table and reduce the labor cost required for generating the risk assessment table.
In a first aspect, an embodiment of the present application provides a risk assessment table generating method, which is applied to a risk assessment table generating system, where the system includes an environmental data acquisition module, an and or graph construction module, a risk probability determination module, a freeze injury loss determination module, and a risk assessment table generating module, and the method includes:
the environment data acquisition module acquires historical soil data, historical crop data and historical geological data of each detection point in the area to be detected;
The AND or graph construction module constructs a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructs a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship and or relationship of each father-son node in the spatial relationship and or graph;
The risk probability determining module determines the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by the graph and each node;
The freeze injury loss determination module determines a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected;
And the risk evaluation table generation module generates a freeze injury risk evaluation table according to the prediction probability of the freeze injury event occurring in each region to be detected and the total loss value of the freeze injury event.
Optionally, the risk probability determining module determines the prediction probability of the freeze injury event in the to-be-detected area according to the causality relation and the historical probability of the freeze injury event of each node, including:
determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node;
And determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node.
Optionally, the risk assessment table generating module generates the risk assessment table according to the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event in each region to be detected, including:
generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents;
And arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
Optionally, the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value and a freeze injury risk level, wherein the soil temperature average value is an average value of soil temperatures of all detection points contained in each region to be detected, and the freeze injury risk level is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table;
The system further comprises a risk assessment table marking module, after the risk assessment table generating module arranges fields of each region to be detected according to the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected from high to low to obtain the freeze injury risk assessment table, the method further comprises the steps of:
and the risk assessment table marking module marks fields corresponding to the areas to be detected with different freezing injury risk levels by using different colors.
In a second aspect, an embodiment of the present application provides a risk assessment table generating system, where the system includes an environmental data acquisition module, an and or graph construction module, a risk probability determination module, a freeze injury loss determination module, and a risk assessment table generating module:
the environment data acquisition module is used for acquiring historical soil data, historical crop data and historical geological data of each detection point in the region to be detected;
The AND or graph construction module is used for constructing a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructing a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship between each father and son node in the spatial relationship and or graph;
The risk probability determining module is used for determining the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by each node;
the freeze injury loss determination module is used for determining a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected;
the risk evaluation table generation module is used for generating a freeze injury risk evaluation table according to the prediction probability of the freeze injury event and the total loss value of the freeze injury event in each region to be detected.
Optionally, the risk probability determining module is configured to, when determining, according to the causality relation and the historical probabilities of the occurrence of the freeze injury event with each node, the predicted probability of the occurrence of the freeze injury event in the to-be-detected area, specifically:
determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node;
And determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node.
Optionally, the risk assessment table generating module is configured to generate the risk assessment table according to the predicted probability of occurrence of the freeze injury event and the total loss value of the freeze injury event in each area to be detected, and specifically is configured to:
generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents;
And arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
Optionally, the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value and a freeze injury risk level, wherein the soil temperature average value is an average value of soil temperatures of all detection points contained in each region to be detected, and the freeze injury risk level is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table;
the system further comprises a risk assessment table marking module;
The risk evaluation table marking module is configured to mark fields corresponding to the to-be-detected areas with different levels of risk of freezing injury by using different colors after the risk evaluation table generating module arranges the fields of each to-be-detected area in order from high to low according to the prediction probability of the occurrence of the freezing injury event and the total loss value of the freezing injury event in each to-be-detected area to obtain the freezing injury risk evaluation table.
In a third aspect, an embodiment of the present application provides a computer apparatus, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating over the bus when the computer device is running, the machine-readable instructions when executed by the processor performing the steps of the risk assessment table generation method of any of the alternative embodiments of the first aspect described above.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor performs the steps of the risk assessment table generation method described in any of the optional embodiments of the first aspect.
The technical scheme provided by the application comprises the following beneficial effects:
the environment data acquisition module acquires the historical soil data, the historical crop data and the historical geological data of each detection point in the region to be detected, and through the steps, objective and quantized data for performing freeze injury risk assessment can be obtained, so that reliable data support is provided for subsequent freeze injury risk assessment.
The AND or graph construction module constructs a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructs a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship and or relationship of each father-son node in the spatial relationship and or graph; through the steps, the spatial relationship and or graph and the causal relationship and or graph can be constructed according to the spatial position relationship objectively existing between the area and the detection point and the causal relationship between the historical evaluation data and the occurrence condition of the freeze injury event, so that more accurate evaluation of the freeze injury risk can be conveniently carried out according to the hierarchical relationship and the causal relationship between the nodes in the graph.
The risk probability determining module determines the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by the graph and each node; the freeze injury loss determination module determines a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected; through the steps, more accurate assessment of the occurrence probability of the freeze injury risk and the loss value of the freeze injury event can be carried out according to the hierarchical relation and the causality relation between the nodes in the graph.
The risk evaluation table generation module generates a freeze injury risk evaluation table according to the prediction probability of the freeze injury event occurring in each region to be detected and the total loss value of the freeze injury event; through the steps, the generation of the freeze injury risk assessment table based on the more accurate and accurate assessment result can be realized.
By adopting the steps, based on the objectively quantized regional assessment data, the spatial hierarchy relation between the objectively existing region and the detection point and the causal relation between the freezing injury event and the regional assessment event, the probability and the loss value of the freezing injury risk event of the region are predicted and assessed, and the accuracy of the obtained risk assessment table can be improved while the labor cost required by generating the risk assessment table is reduced.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a risk assessment table generation method according to a first embodiment of the present invention;
FIG. 2 is a flow chart of a predictive probability determination method according to a first embodiment of the invention;
FIG. 3 is a flowchart of a second risk assessment table generation method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a risk assessment table generating system according to a second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a second risk assessment table generating system according to a second embodiment of the present invention;
Fig. 6 shows a schematic structural diagram of a computer device according to a third embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
In order to facilitate understanding of the present application, the following describes a detailed description of the first embodiment of the present application in conjunction with the flowchart of the first embodiment of the present application shown in fig. 1.
Referring to fig. 1, fig. 1 shows a flowchart of a risk assessment table generating method according to an embodiment of the present invention, where the method is applied to a risk assessment table generating system, the system includes an environmental data collecting module, an and or graph constructing module, a risk probability determining module, a freeze damage loss determining module, and a risk assessment table generating module, and the method includes steps S101 to S105:
S101: the environment data acquisition module acquires historical soil data, historical crop data and historical geological data of each detection point in the area to be detected.
Specifically, the data of the historical soil data, the historical crop data and the historical geological data are in the form of monitoring signals (such as video signals) or data signals (such as data of soil temperature, humidity, freezing depth and the like) collected by the sensors. The monitoring signals can provide real-time states of farmlands and provide basis for subsequent evaluation and early warning. Through image sensing technology, some static crop information of the point location, such as information of crop type, area, density, height and the like, and information of topography, landform, water source and the like of the point location can be extracted from the video signal. This information can be used to calculate the fixed asset value of the spot and the extent of loss after a freeze injury has occurred. In addition, the information such as the growth condition of crops, the condition of snow covering and the like is extracted through image perception, so that whether the crops at the point position are affected by freeze injury or not and the degree and the range of the influence can be identified. Such information may help to formulate effective anti-freeze strategies and take emergency action if necessary. For example, according to the monitoring signal and the early warning information, a suitable sowing time, variety selection, cultivation mode, irrigation mode, coverage mode, and the like can be selected. Through the freeze injury point monitoring signals, data such as temperature, humidity, freezing depth and the like of farmlands, and information such as growth conditions of crops, snow covering conditions and the like can be obtained in real time, so that the freeze injury risk is quantitatively or qualitatively analyzed and evaluated. Thus, the efficiency and the effect of preventing and coping with the freezing injury can be improved, and the risk and the loss of agricultural production are reduced.
The crop information of the freeze injury area is extracted from the video signal by utilizing an advanced image processing algorithm, and the information such as land utilization, meteorological conditions and the like is combined, so that a new technical means is provided for freeze injury risk assessment. Video signals from unmanned aerial vehicles, monitoring cameras or other video acquisition equipment are received in real time or offline, and video frames are preprocessed, segmented, identified, summarized and the like, so that information of crop types, growing periods, damage degrees and the like in a freezing injury area, and information of land utilization types, meteorological condition types and the like are accurately obtained. The information can be used for calculating factors such as possible agricultural income loss and influence ecological system and climate change in the freezing injury event, and plays an important role in the comprehensiveness and accuracy of the risk assessment result. The application can also automatically adjust parameters and methods of image processing and image perception according to different data sources and input modes so as to adapt to different scenes and requirements.
S102: the AND or graph construction module constructs a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructs a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship and or relationship of each father-son node in the spatial relationship and or graph.
Specifically, firstly, according to the close relation between each detection point and the to-be-detected area in the geographic position, the association between the nodes is established, then, causal analysis is carried out according to the historical soil data, the historical crop data and the historical geological data (recorded as the historical perception data) of each detection point and the historical behavior event to construct a causal relation or graph, and finally, probability statistics is carried out on each causal relation in the causal relation or graph to construct a space-causal probability or graph.
Further, the nodes are corresponding to the detection points, a threshold value is set according to Euclidean distance between the points (detection points), and an edge is established between the nodes corresponding to the two points with the distance smaller than the threshold value, so that the spatial neighbor relation of the nodes is represented. For simplicity we restrict the spatial neighbors of each node to no more than 3. And acquiring the historical perception data of each node (each detection point) in the spatial relation or the graph, and determining signal level events of each historical perception data according to each historical perception data and the corresponding preset standard threshold value, such as that the numerical value is higher than the threshold value, the numerical value is lower than the threshold value, the numerical value change rate is overlarge, the numerical value is kept unchanged, and the like. And carrying out causal analysis on the historical events of whether the freezing injury occurs in each node and the signal level events of the historical sensing data to obtain causal association of the historical events and the signal level events of each detection point, namely the causal association of the freezing injury events occurring in each detection point under the historical sensing data, and then constructing causal association and or graphs of the two types of events according to the causal association. The causal relationship and or graph is used for indicating causal association of the spatial relationship and each node in the graph to the occurrence of a freeze injury event and historical perception data.
S103: and the risk probability determining module determines the prediction probability of the freezing injury event in the region to be detected according to the causality relation or the historical probability of the freezing injury event of each node.
Specifically, the historical probabilities of the causal relationship and or the graph and the freezing injury events of all the nodes are priori data, and before the risk probability determining module determines the prediction probability of the freezing injury events of the to-be-detected area according to the causal relationship and the historical probabilities of the freezing injury events of all the nodes, the causal relationship and the historical probabilities of the freezing injury events of all the nodes are obtained from an priori database. Here, the prediction probability of the freezing injury event in the area to be detected can be determined directly according to the causality relation and the historical probability of the freezing injury event generated by the graph and each node; or constructing a space-causal probability and or graph according to the causal relation and or graph and the historical probability of the freezing injury event of each node, wherein the space-causal probability and or graph are used for indicating the historical probability of the freezing injury event of each node in the causal relation and or graph under the historical perception data.
Determining the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by the graph and each node, wherein the prediction probability of the freezing injury event in the region to be detected can be realized by the following steps of: and (3) inputting the causality and the historical probability of the freezing injury event generated by the graph and each node into a trained regional prediction probability determination model to obtain the prediction probability of the freezing injury event generated by the region to be detected. Or the determination of the prediction probability is performed with reference to the method in steps S201 to S202.
S104: and the freeze injury loss determination module determines the total loss value of the freeze injury event of the area to be detected according to the prediction probability of the freeze injury event of the area to be detected and the sub-loss value of the freeze injury event of each detection point in the area to be detected.
Specifically, the freeze injury loss determination includes two parts: static loss and dynamic loss. The static loss needs to be calculated once in a period of time, while the dynamic loss needs to be calculated continuously in real time according to the input signal.
Two methods for determining the total loss value of the freezing injury event of the area to be detected are provided. The method comprises the following steps: and multiplying the predicted probability of the occurrence of the freeze injury event of the to-be-detected area obtained in the step S103 by the probability loss value of the to-be-detected area to obtain the total loss value of the freeze injury event of the to-be-detected area, wherein the maximum value in the sub-loss values (priori data) of the freeze injury event of each detection point in the to-be-detected area is the probability loss value of the to-be-detected area. The second method is as follows: when a certain detection point is frozen, the corresponding leaf node is set as a loss value, the leaf node corresponding to the point where the frozen injury does not occur is set as 0, and then the loss value of the frozen injury of each node is calculated from bottom to top. Assuming that the detection point corresponding to the child node 1 has a freeze injury, the expected loss value C (node a) of the area to be detected corresponding to the node a to which the child node 1 belongs is calculated as follows: c (node a) =c (child node 1) ×p (child node 1) +c (child node 2) ×p (child node 2) + … +c (child node k) ×p (child node k), wherein C (child node 1) represents a freeze injury event child loss value of a detection point corresponding to the 1 st child node, C (child node 2) represents a freeze injury event child loss value of a detection point corresponding to the 2 nd child node, and C (child node k) represents a freeze injury event child loss value of a detection point corresponding to the kth node; p (child node 1) represents the prediction probability of the detection point corresponding to the 1 st child node, P (child node 2) represents the prediction probability of the detection point corresponding to the 2 nd child node, and P (child node k) represents the prediction probability of the detection point corresponding to the kth child node.
The mathematical expectation of the loss value comprises two items, wherein the first item is the sum of all child node losses of the child node which have suffered from freeze injury; the second term is the expected loss of nodes that have a close relationship to nodes that have suffered a freeze injury. It should be noted that if a child node (point location) is associated with a child node of another node, then the other node should also account for the expected loss.
S105: and the risk evaluation table generation module generates a freeze injury risk evaluation table according to the prediction probability of the freeze injury event occurring in each region to be detected and the total loss value of the freeze injury event.
Specifically, taking the predicted probability of the occurrence of the freeze injury event in each area to be detected as a column in a table, and taking the total loss value of the freeze injury event of the occurrence of the freeze injury event in each area to be detected as a row in the table to construct a freeze injury risk assessment table; or taking the predicted probability of the occurrence of the freeze injury event of each area to be detected as a row in the table, and taking the total loss value of the freeze injury event of the occurrence of the freeze injury event of each area to be detected as a column in the table to construct a freeze injury risk assessment table.
In an alternative implementation manner, referring to fig. 2, fig. 2 shows a flowchart of a method for determining a prediction probability according to an embodiment of the present invention, where the risk probability determining module determines, according to the causal relationship and/or the historical probabilities of occurrence of a freeze injury event with each node, a prediction probability of occurrence of a freeze injury event in the to-be-detected area, and includes steps S201 to S202:
s201: and determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node.
Specifically, determining the current probability of the occurrence of the freeze injury event of each node according to the historical probability of the occurrence of the freeze injury event of each node comprises: training an initial model for predicting the occurrence probability of the freeze injury event by utilizing the historical probability of the freeze injury event of each node and the historical perception data of the freeze injury event of each node to obtain a target model; and (3) inputting the current perception data (the current soil data, the current crop data and the current geological data acquired by the environment data acquisition module) of each node into the target model to obtain the current probability of the freezing injury event of each node.
S202: and determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node.
Specifically, the calculation of the prediction probability is performed in two cases. The first case is: when the node attribute of the area to be detected is causal relation or AND node in the graph, determining the prediction probability of the occurrence of the freezing injury event of the area to be detected by using a full probability formula according to the current probability of the occurrence of the freezing injury event of the child node (the indicated detection point or the sub-region). For example, when k detection points are included in the area to be detected, each of the k detection points is corresponding to a causal relationship sum or graph, so as to obtain a node A indicating the area to be detected and k child nodes (child node 1, child node 2, …, child node k) indicating the k detection points; at this time, the calculation method of the prediction probability P (node a) of the occurrence of the freeze injury event in the region to be detected corresponding to the node a is as follows: p (node a) =p (child node 1, current) ×p (child node 1, history) +p (child node 2, current) ×p (child node 2, history) + … +p (child node k, current) ×p (child node k, history); wherein, P (child node 1, current) is the current probability of the occurrence of the freeze injury event of the 1 st child node, P (child node 1, history) is the history probability of the occurrence of the freeze injury event of the 1 st child node, P (child node 2, current) is the current probability of the occurrence of the freeze injury event of the 2 nd child node, P (child node 2, history) is the history probability of the occurrence of the freeze injury event of the 2 nd child node, P (child node k, current) is the current probability of the occurrence of the freeze injury event of the k th child node, and P (child node k, history) is the history probability of the occurrence of the freeze injury event of the k th child node.
When the node attribute of the area to be detected is causal relation or in the graph or node, the prediction probability is equal to the maximum value in the current probability of the freezing damage event of all the child nodes (the indicated detection points or the child areas). For example, when g detection points are included in the area to be detected, each of the g detection points is corresponding to a causal relationship and/or graph, so as to obtain a node B indicating the area to be detected and g nodes (node 1, node 2, …, node g) indicating the g detection points; at this time, the calculation method of the prediction probability P (node B) of the occurrence of the freeze injury event in the region to be detected corresponding to the node B is as follows: p (node B) =max { P (node 1), P (node 2), …, P (node g) }; wherein P (node 1) is the predicted probability of the occurrence of the freeze injury event of the 1 st node, P (node 2) is the predicted probability of the occurrence of the freeze injury event of the 2 nd node, P (node g) is the predicted probability of the occurrence of the freeze injury event of the g-th node, and the predicted probabilities of the occurrence of the freeze injury event of the respective nodes can be calculated by referring to the calculation method of P (node a) in the first case.
For simplicity, the probability of occurrence of the point location itself is considered to be unchanged due to the change of the states of the peripheral point locations, so that the probability of occurrence of the point location itself is not updated according to the spatial neighbor relation of the point location.
In an alternative implementation manner, referring to fig. 3, fig. 3 shows a flowchart of a second risk assessment table generating method according to an embodiment of the present invention, where the risk assessment table generating module generates the freeze injury risk assessment table according to the predicted probability of occurrence of a freeze injury event and the total loss value of the freeze injury event in each area to be detected, and the method includes steps S301 to 302:
S301: and generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents.
S302: and arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
Specifically, the areas to be detected are ranked according to the prediction probability of the occurrence of the freeze injury event in the areas to be detected from high to low, and for the areas with the same prediction probability, the areas are ranked according to the sequence of the total loss value of the freeze injury event from high to low, and the ranked results are output as the freeze injury risk assessment table.
In an alternative embodiment, the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value, and a freeze injury risk level, wherein the soil temperature average value is an average value of soil temperatures of all detection points included in each region to be detected, and the freeze injury risk level is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table.
Specifically, the soil temperature average value and the freezing injury risk level are related, when the soil temperature average value is higher than 0 ℃, no obvious freezing injury is shown, and the freezing injury risk level is a low risk area; when the average value of the soil temperature is lower than or equal to 0 ℃ and higher than-5 ℃, the freezing injury risk grade is a medium risk area; when the average value of the soil temperature is lower than or equal to minus 5 ℃ and higher than minus 10 ℃, the freezing injury risk grade is a high risk area; when the average value of the soil temperature is lower than or equal to minus 10 ℃, the freezing injury risk grade is a high risk area.
The system further comprises a risk assessment table marking module, after the risk assessment table generating module arranges fields of each region to be detected according to the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected from high to low to obtain the freeze injury risk assessment table, the method further comprises the steps of:
and the risk assessment table marking module marks fields corresponding to the areas to be detected with different freezing injury risk levels by using different colors.
Specifically, the fields with low risk levels of the freeze injury in the freeze injury risk assessment table are marked by blue, the fields with medium risk levels of the freeze injury in the freeze injury risk assessment table are marked by yellow, the fields with higher risk levels of the freeze injury in the freeze injury risk assessment table are marked by orange, and the fields with high risk levels of the freeze injury in the freeze injury risk assessment table are marked by red.
Example two
Referring to fig. 4, fig. 4 shows a schematic structural diagram of a risk assessment table generating system according to a second embodiment of the present invention, where the system includes an environmental data acquisition module 401, an and or graph construction module 402, a risk probability determination module 403, a freeze injury loss determination module 404, and a risk assessment table generating module 405:
the environment data acquisition module is used for acquiring historical soil data, historical crop data and historical geological data of each detection point in the region to be detected;
The AND or graph construction module is used for constructing a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructing a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship between each father and son node in the spatial relationship and or graph;
The risk probability determining module is used for determining the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by each node;
the freeze injury loss determination module is used for determining a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected;
the risk evaluation table generation module is used for generating a freeze injury risk evaluation table according to the prediction probability of the freeze injury event and the total loss value of the freeze injury event in each region to be detected.
In an optional embodiment, the risk probability determining module is specifically configured to, when determining, according to the causal relationship and the historical probabilities of occurrence of the freeze injury events of the nodes and the graph, determine a predicted probability of occurrence of the freeze injury event of the area to be detected:
determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node;
And determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node.
In an alternative embodiment, the risk assessment table generating module is specifically configured to, when generating the freeze injury risk assessment table according to the predicted probability of occurrence of a freeze injury event and the total loss value of the freeze injury event in each area to be detected:
generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents;
And arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
In an alternative embodiment, the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value, and a freeze injury risk level, wherein the soil temperature average value is an average value of soil temperatures of all detection points included in each region to be detected, and the freeze injury risk level is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table;
Referring to fig. 5, fig. 5 is a schematic structural diagram of a second risk assessment table generating system according to a second embodiment of the present invention, where the system further includes a risk assessment table marking module 501;
The risk evaluation table marking module is configured to mark fields corresponding to the to-be-detected areas with different levels of risk of freezing injury by using different colors after the risk evaluation table generating module arranges the fields of each to-be-detected area in order from high to low according to the prediction probability of the occurrence of the freezing injury event and the total loss value of the freezing injury event in each to-be-detected area to obtain the freezing injury risk evaluation table.
Example III
Based on the same application concept, referring to fig. 6, fig. 6 shows a schematic structural diagram of a computer device provided in a third embodiment of the present application, where, as shown in fig. 6, a computer device 600 provided in the third embodiment of the present application includes:
The risk assessment table generating method comprises a processor 601, a memory 602 and a bus 603, wherein the memory 602 stores machine-readable instructions executable by the processor 601, and when the computer device 600 is running, the processor 601 and the memory 602 communicate through the bus 603, and the machine-readable instructions are executed by the processor 601 to perform the steps of the risk assessment table generating method according to the first embodiment.
Example IV
Based on the same application concept, the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the risk assessment table generating method in any one of the foregoing embodiments are executed.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system and apparatus may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
The computer program product for performing the risk assessment table generating method provided by the embodiment of the present invention includes a computer readable storage medium storing program codes, where the instructions included in the program codes may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment and will not be described herein.
The risk assessment table generation system provided by the embodiment of the invention can be specific hardware on equipment or software or firmware installed on the equipment. The system provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the system embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method may be implemented in other manners. The system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, and e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A risk assessment table generation method, which is applied to a risk assessment table generation system, wherein the system comprises an environment data acquisition module, an and or graph construction module, a risk probability determination module, a freeze injury loss determination module and a risk assessment table generation module, and the method comprises the following steps:
the environment data acquisition module acquires historical soil data, historical crop data and historical geological data of each detection point in the area to be detected;
The AND or graph construction module constructs a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructs a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship and or relationship of each father-son node in the spatial relationship and or graph;
The risk probability determining module determines the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by the graph and each node;
The freeze injury loss determination module determines a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected;
The risk evaluation table generation module generates a freeze injury risk evaluation table according to the prediction probability of the freeze injury event occurring in each region to be detected and the total loss value of the freeze injury event;
The determining the prediction probability of the freezing injury event in the area to be detected according to the causality relation and the historical probability of the freezing injury event generated by the causality relation or the graph and each node comprises the following steps:
determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node;
Determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node;
The determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node comprises the following steps:
Training an initial model for predicting the occurrence probability of the freeze injury event by utilizing the historical probability of the freeze injury event of each node and the historical perception data of the freeze injury event of each node to obtain a target model; the current perception data of each node is input into a target model to obtain the current probability of the occurrence of a freeze injury event of each node;
Determining the prediction probability of the freeze injury event in the region to be detected according to the current probability of the freeze injury event in each node and the node attribute of each node in the causal relationship sum or graph, wherein the node attribute comprises a sum node and or node, and the method comprises the following steps:
when the node attribute of the area to be detected is causal relation or node in the graph, determining the prediction probability of the occurrence of the freezing injury event of the area to be detected by utilizing a full probability formula according to the current probability of the occurrence of the freezing injury event of the child node; when the node attribute of the region to be detected is causal relation or in the graph or node, the prediction probability of the node attribute is equal to the maximum value in the current probability of the occurrence of the freezing injury event of all the child nodes;
Determining a total loss value of the freeze injury event of the to-be-detected area according to the predicted probability of the freeze injury event of the to-be-detected area and the sub-loss value of the freeze injury event of each detection point in the to-be-detected area, including:
Multiplying the predicted probability of the occurrence of the freeze injury event in the area to be detected and the probability loss value thereof to obtain the total loss value of the freeze injury event in the area to be detected, wherein the maximum value in the sub-loss values of the freeze injury event of each detection point in the area to be detected is the probability loss value of the area to be detected;
or the leaf nodes corresponding to the detection points with the freezing damage are set as loss values, the leaf nodes corresponding to the detection points without the freezing damage are set as 0, and the loss values of the freezing damage of each node are calculated from bottom to top; determining the total loss value of the freezing injury event of the region to be detected based on the following expression:
C (node a) =c (child node 1) ×p (child node 1) +c (child node 2) ×p (child node 2) + … +c (child node k) ×p (child node k), wherein C (child node 1) represents a freeze injury event child loss value of a detection point corresponding to the 1 st child node, C (child node 2) represents a freeze injury event child loss value of a detection point corresponding to the 2 nd child node, and C (child node k) represents a freeze injury event child loss value of a detection point corresponding to the kth node; p (child node 1) represents the prediction probability of the detection point corresponding to the 1 st child node, P (child node 2) represents the prediction probability of the detection point corresponding to the 2 nd child node, and P (child node k) represents the prediction probability of the detection point corresponding to the kth child node.
2. The method of claim 1, wherein the risk assessment table generation module generates the freeze injury risk assessment table based on a predicted probability of a freeze injury event occurring for each region to be detected and a total loss value of freeze injury events, comprising:
generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents;
And arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
3. The method according to claim 2, wherein the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value, which is an average value of soil temperatures of all detection points included in each region to be detected, and a freeze injury risk level, which is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table;
The system further comprises a risk assessment table marking module, after the risk assessment table generating module arranges fields of each region to be detected according to the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected from high to low to obtain the freeze injury risk assessment table, the method further comprises the steps of:
and the risk assessment table marking module marks fields corresponding to the areas to be detected with different freezing injury risk levels by using different colors.
4. A risk assessment table generation system, which is characterized by comprising an environment data acquisition module, an AND or graph construction module, a risk probability determination module, a freeze injury loss determination module and a risk assessment table generation module:
the environment data acquisition module is used for acquiring historical soil data, historical crop data and historical geological data of each detection point in the region to be detected;
The AND or graph construction module is used for constructing a spatial relationship and or graph according to the spatial relationship between each detection point and the region to be detected, and constructing a causal relationship and or graph according to the historical soil data, the historical crop data, the historical geological data and the historical freeze injury event result of each node in the spatial relationship and or graph, wherein the root node of the spatial relationship and or graph corresponds to the region to be detected, the leaf node of the spatial relationship and or graph corresponds to each detection point, and the causal relationship and or graph comprises the causal relationship between each father and son node in the spatial relationship and or graph;
The risk probability determining module is used for determining the prediction probability of the freezing injury event in the region to be detected according to the causality relation and the historical probability of the freezing injury event generated by each node;
the freeze injury loss determination module is used for determining a total freeze injury event loss value of the area to be detected according to the prediction probability of the freeze injury event occurring in the area to be detected and the freeze injury event sub-loss value of each detection point in the area to be detected;
The risk evaluation table generation module is used for generating a freeze injury risk evaluation table according to the prediction probability of the freeze injury event and the total loss value of the freeze injury event in each region to be detected;
The determining the prediction probability of the freezing injury event in the area to be detected according to the causality relation and the historical probability of the freezing injury event generated by the causality relation or the graph and each node comprises the following steps:
determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node;
Determining the prediction probability of the freezing injury event in the region to be detected according to the current probability of the freezing injury event of each node and the node attribute of each node in the causality relation and/or graph, wherein the node attribute comprises a node and/or node;
The determining the current probability of the freeze injury event of each node according to the historical probability of the freeze injury event of each node comprises the following steps:
Training an initial model for predicting the occurrence probability of the freeze injury event by utilizing the historical probability of the freeze injury event of each node and the historical perception data of the freeze injury event of each node to obtain a target model; the current perception data of each node is input into a target model to obtain the current probability of the occurrence of a freeze injury event of each node;
Determining the prediction probability of the freeze injury event in the region to be detected according to the current probability of the freeze injury event in each node and the node attribute of each node in the causal relationship sum or graph, wherein the node attribute comprises a sum node and or node, and the method comprises the following steps:
when the node attribute of the area to be detected is causal relation or node in the graph, determining the prediction probability of the occurrence of the freezing injury event of the area to be detected by utilizing a full probability formula according to the current probability of the occurrence of the freezing injury event of the child node; when the node attribute of the region to be detected is causal relation or in the graph or node, the prediction probability of the node attribute is equal to the maximum value in the current probability of the occurrence of the freezing injury event of all the child nodes;
Determining a total loss value of the freeze injury event of the to-be-detected area according to the predicted probability of the freeze injury event of the to-be-detected area and the sub-loss value of the freeze injury event of each detection point in the to-be-detected area, including:
Multiplying the predicted probability of the occurrence of the freeze injury event in the area to be detected and the probability loss value thereof to obtain the total loss value of the freeze injury event in the area to be detected, wherein the maximum value in the sub-loss values of the freeze injury event of each detection point in the area to be detected is the probability loss value of the area to be detected;
or the leaf nodes corresponding to the detection points with the freezing damage are set as loss values, the leaf nodes corresponding to the detection points without the freezing damage are set as 0, and the loss values of the freezing damage of each node are calculated from bottom to top; determining the total loss value of the freezing injury event of the region to be detected based on the following expression:
C (node a) =c (child node 1) ×p (child node 1) +c (child node 2) ×p (child node 2) + … +c (child node k) ×p (child node k), wherein C (child node 1) represents a freeze injury event child loss value of a detection point corresponding to the 1 st child node, C (child node 2) represents a freeze injury event child loss value of a detection point corresponding to the 2 nd child node, and C (child node k) represents a freeze injury event child loss value of a detection point corresponding to the kth node; p (child node 1) represents the prediction probability of the detection point corresponding to the 1 st child node, P (child node 2) represents the prediction probability of the detection point corresponding to the 2 nd child node, and P (child node k) represents the prediction probability of the detection point corresponding to the kth child node.
5. The system according to claim 4, wherein the risk assessment table generation module is configured to, when generating the risk assessment table according to the predicted probability of occurrence of a freeze injury event and the total loss value of the freeze injury event in each region to be detected, specifically:
generating fields of the areas to be detected by taking the area names of the areas to be detected as field names and the freeze injury information as field contents;
And arranging fields of each region to be detected according to the sequence from high to low of the predicted probability of the occurrence of the freeze injury event and the total loss value of the freeze injury event of each region to be detected to obtain the freeze injury risk assessment table.
6. The system according to claim 5, wherein the freeze injury information includes a predicted probability of occurrence of a freeze injury event, a total loss value of the freeze injury event, a soil temperature average value, which is an average value of soil temperatures of all detection points included in each region to be detected, and a freeze injury risk level, which is a risk level corresponding to the predicted probability of occurrence of the freeze injury event in each region to be detected in a risk level comparison table;
the system further comprises a risk assessment table marking module;
The risk evaluation table marking module is configured to mark fields corresponding to the to-be-detected areas with different levels of risk of freezing injury by using different colors after the risk evaluation table generating module arranges the fields of each to-be-detected area in order from high to low according to the prediction probability of the occurrence of the freezing injury event and the total loss value of the freezing injury event in each to-be-detected area to obtain the freezing injury risk evaluation table.
7. A computer device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating over the bus when the computer device is running, said machine readable instructions when executed by said processor performing the steps of the risk assessment table generation method according to any one of claims 1 to 3.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the risk assessment table generation method according to any one of claims 1 to 3.
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