Method and device for intelligent processing of plant and animal data based on environmental information

Abstract

The application discloses a kind of based on environmental information's plant and animal data intelligent processing method and device, the method comprises: obtaining the environmental information of target area;Receive each data collection point in target area The target data reported by;For each target data, the target data is input into the data classification model of pre-set, and the data classification result of the target data is obtained;According to the data classification result of each target data, all target data belonging to the same data category is determined as a target data category set;Based on the environmental information of target area, the data analysis result of each target data is determined;According to the data analysis result of all target data, the target data analysis result of target area is determined;Wherein, target data analysis result includes the plant and animal survival information of target area. Visible, the efficiency of processing plant and animal data can be improved by implementing the application, and the accuracy of processing plant and animal data can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a plant and animal data intelligent processing method and device based on environmental information. BACKGROUND

[0002] An ecological environment is the premise and basis for human survival and development. With the rapid development of science and economy, people's attention to the ecological environment has gradually increased. The ecological environment includes animal data and plant data. Most of the existing processing methods for plant and animal data are to monitor the plant and animal data in a certain environment through an environmental data monitoring device, and to process the monitoring data manually. This not only reduces the efficiency of data processing, but also is prone to processing errors. Therefore, it is particularly important to provide a new processing method for plant and animal data to improve the efficiency and accuracy of processing plant and animal data. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a plant and animal data intelligent processing method and device based on environmental information, which can improve the efficiency of processing plant and animal data and improve the accuracy of processing plant and animal data.

[0004] To solve the above technical problem, the present application discloses a plant and animal data intelligent processing method based on environmental information, which comprises:

[0005] Obtaining environmental information of a target area, the environmental information including one or more of the following: regional position information of the target area, climate information of the target area, animal species information of the target area, plant species information of the target area, data collection point information of the target area, and topographic information of the target area;

[0006] Receiving target data reported by each data collection point in the target area, the target data including at least one plant and animal data;

[0007] For each target data, inputting the target data into a preset data classification model to obtain a data classification result of the target data, the data classification result including a data category of the target data;

[0008] According to the data classification result of each target data, determining all target data belonging to the same data category as a target data category set; the data category includes an animal data category and a plant data category, and each target data category set includes at least one target data;

[0009] determining, based on the environmental information of the target region, a data analysis result of each of the target data included in the set of target data categories;

[0010] determining a target data analysis result of the target region according to the data analysis results of all the target data; wherein the target data analysis result comprises living and plant survival information of the target region.

[0011] As an optional implementation, in the first aspect of the present application, after receiving the target data reported by each data collection point in the target region, the method further comprises:

[0012] determining, according to the target data reported by each data collection point, whether there is an overlapping data group in all the target data, each target data included in the overlapping data group corresponding to the same data monitoring range and each target data included in the overlapping data group coming from different data collection points;

[0013] when it is determined that there is the overlapping data group in all the target data, determining the data monitoring range of each data collection point;

[0014] for each overlapping data group, determining a target data collection point of the overlapping data group according to the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group;

[0015] performing a data deduplication operation on each overlapping data group according to the target data collection point of each overlapping data group, so as to update all the target data.

[0016] As an optional implementation, in the first aspect of the present application, after determining the target data analysis result of the target region according to the data analysis results of all the target data, the method further comprises:

[0017] determining, according to the target data analysis result of the target region, whether there is abnormal data that does not meet a preset data condition in the target data analysis result;

[0018] when it is determined that there is the abnormal data that does not meet the preset data condition in the target data analysis result, determining an abnormal category of each abnormal data;

[0019] for each abnormal data, determining an abnormal processing mode of the abnormal data according to the abnormal category of the abnormal data, the abnormal processing mode comprising one or more of a data expansion processing mode and a data merging processing mode;

[0020] For each of the abnormal data, a data processing operation matching the abnormal processing mode of the abnormal data is performed on the abnormal data.

[0021] As an optional implementation, in the first aspect of the present application, the determining of the abnormal category of each of the abnormal data comprises:

[0022] The data collection information of each of the abnormal data is determined, the data collection information comprising collection time information of each of the abnormal data and collection frequency information of each of the abnormal data.

[0023] For each of the abnormal data, collection interval time period information of the abnormal data is determined according to the collection time information of the abnormal data and the collection frequency information of the abnormal data.

[0024] According to the collection interval time period information of each of the abnormal data, it is judged whether there is first data abnormal information in which the collection interval time period is greater than or equal to a preset first interval time period threshold in all the collection interval time period information.

[0025] When it is judged that there is the first data abnormal information in all the collection interval time period information, the abnormal category of the abnormal data corresponding to each of the first data abnormal information is determined as a data missing abnormal category.

[0026] It is judged whether there is second data abnormal information in which the collection interval time period is less than a preset second interval time period threshold in all the collection interval time period information.

[0027] When it is judged that there is the second data abnormal information in all the data collection information, the abnormal category of the abnormal data corresponding to each of the second data abnormal information is determined as a data repetition abnormal category.

[0028] The first interval time period threshold is greater than the second interval time period threshold.

[0029] As an optional implementation, in the first aspect of the present application, when the abnormal processing mode of the abnormal data comprises the data amplification processing mode, the data processing operation matching the abnormal processing mode of the abnormal data is performed on the abnormal data, comprising:

[0030] For each of the abnormal data, a to-be-expanded data time period of the abnormal data is determined according to the collection interval time period information of the abnormal data, and a similarity between the abnormal data and a data analysis result of each of the target data is calculated according to the data analysis result of each of the target data and the environment information, so as to obtain a data similarity set of the abnormal data, a highest similarity is determined from the data similarity set of the abnormal data, and a target data corresponding to the highest similarity is determined as target similar data of the abnormal data.

[0031] For each of the abnormal data, target associated data matching the to-be-expanded data time period of the abnormal data is determined from the target similar data of the abnormal data, and a data expansion operation is performed on the abnormal data according to the to-be-expanded data time period of the abnormal data and the target associated data corresponding to the abnormal data, so as to complete a data processing operation on the abnormal data.

[0032] As an optional implementation, in the first aspect of the present application, the determining of the target data analysis result of the target region according to the data analysis results of all the target data comprises:

[0033] For each of the target data, biological parameter information of the target data is determined according to the data analysis result of the target data, the biological parameter information comprising one or more of a survival time length of a biological corresponding to the target data and a survival region of the biological corresponding to the target data;

[0034] For each of the target data, a category analysis result of a data category of the target data is determined according to the biological parameter information of the target data and a data classification result of the target data;

[0035] A target data analysis result of the target region is determined based on the category analysis results of the data categories of all the target data.

[0036] As an optional implementation, in the first aspect of the present application, after the determining of the target data analysis result of the target region according to the data analysis results of all the target data, the method further comprises:

[0037] According to the target data analysis result of the target region, animal and plant growth information of the target region is determined, wherein the animal and plant growth information of the target region comprises growth information of each kind of biological in the target region;

[0038] It is judged whether there is a target biological not satisfying a preset growth condition in the animal and plant growth information of the target region;

[0039] When it is judged that there is a target organism in the growth information of animals and plants in the target area that does not meet the preset growth condition, for each target organism, a target growth reason is determined that the target organism does not meet the preset growth condition;

[0040] Based on the target growth reason of each target organism, an environmental improvement parameter of the target area is determined, and an operation matched with the environmental improvement parameter is performed on the target area to improve the living quality of animals and plants in the target area.

[0041] The second aspect of the present application discloses an intelligent processing device for animal and plant data based on environmental information, which comprises:

[0042] An acquisition module is configured to acquire environmental information of a target area, wherein the environmental information comprises one or more of the following: regional location information of the target area, climate information of the target area, animal species information of the target area, plant species information of the target area, data collection point information of the target area, and terrain information of the target area;

[0043] A receiving module is configured to receive target data reported by each data collection point in the target area, wherein the target data comprises at least one animal and plant data;

[0044] An input module is configured to, for each target data, input the target data into a preset data classification model to obtain a data classification result of the target data, wherein the data classification result comprises a data category of the target data;

[0045] A determination module is configured to, according to the data classification result of each target data, determine all target data belonging to the same data category as a target data category set; the data category comprises an animal data category and a plant data category, and each target data category set comprises at least one target data;

[0046] The determination module is further configured to, based on the environmental information of the target area, determine, for each target data included in each target data category set, a data analysis result of the target data;

[0047] The determination module is further configured to, according to the data analysis result of all target data, determine a target data analysis result of the target area; wherein the target data analysis result comprises animal and plant living information of the target area.

[0048] As an optional implementation, in the second aspect of the present application, the device further comprises:

[0049] a judging module, configured to, after the receiving module receives the target data reported by each data collection point in the target region, judge whether there is an overlapping data group in all the target data according to the target data reported by each data collection point, each target data included in the overlapping data group corresponding to a same data monitoring range and each target data included in the overlapping data group coming from different data collection points;

[0050] The determining module is further configured to determine the data monitoring range of each data collection point when the judging module judges that there is the overlapping data group in all the target data.

[0051] The determining module is further configured to, for each overlapping data group, determine the target data collection point of the overlapping data group according to the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group.

[0052] an updating module, configured to perform a data deduplication operation on each overlapping data group according to the target data collection point of each overlapping data group, so as to update all the target data.

[0053] As an optional implementation, in the second aspect of the present application, the judging module is further configured to, after the determining module determines the target data analysis result of the target region according to the data analysis result of all the target data, judge whether there is abnormal data that does not meet the preset data condition in the target data analysis result according to the target data analysis result of the target region.

[0054] The determining module is further configured to determine the abnormal category of each abnormal data when the judging module judges that there is the abnormal data that does not meet the preset data condition in the target data analysis result.

[0055] The determining module is further configured to, for each abnormal data, determine the abnormal processing mode of the abnormal data according to the abnormal category of the abnormal data, the abnormal processing mode including one or more of a data expansion processing mode and a data merging processing mode.

[0056] The device further comprises:

[0057] an executing module, configured to, for each abnormal data, perform a data processing operation matched with the abnormal processing mode of the abnormal data on the abnormal data.

[0058] As an optional implementation, in the second aspect of the present application, the specific manner in which the determining module determines the abnormal category of each abnormal data includes:

[0059] determining data collection information of each of the abnormal data, the data collection information comprising collection time information of each of the abnormal data and collection frequency information of each of the abnormal data;

[0060] For each of the abnormal data, determining collection interval time period information of the abnormal data according to the collection time information of the abnormal data and the collection frequency information of the abnormal data;

[0061] According to the collection interval time period information of each of the abnormal data, determining whether there is first data abnormal information in which a collection interval time period is greater than or equal to a preset first interval time period threshold in all the collection interval time period information;

[0062] When it is determined that there is the first data abnormal information in all the collection interval time period information, determining an abnormal category of the abnormal data corresponding to each of the first data abnormal information as a data missing abnormal category;

[0063] Determining whether there is second data abnormal information in which a collection interval time period is less than a preset second interval time period threshold in all the collection interval time period information;

[0064] When it is determined that there is the second data abnormal information in all the data collection information, determining an abnormal category of the abnormal data corresponding to each of the second data abnormal information as a data repetition abnormal category;

[0065] The first interval time period threshold is greater than the second interval time period threshold.

[0066] As an optional implementation, in the second aspect of the present application, when the abnormal processing mode of the abnormal data comprises the data amplification processing mode, the specific mode of the execution module for performing, for each of the abnormal data, a data processing operation matched with the abnormal processing mode of the abnormal data comprises:

[0067] For each of the abnormal data, determining a to-be-amplified data time period of the abnormal data according to the collection interval time period information of the abnormal data, and calculating a similarity between the abnormal data and a data analysis result of each of the target data according to the data analysis result of each of the target data and the environment information to obtain a data similarity set of the abnormal data, determining a highest similarity from the data similarity set of the abnormal data, and determining a target similar data corresponding to the highest similarity as a target similar data of the abnormal data;

[0068] For each of the abnormal data, target associated data matching a data time period to be amplified of the abnormal data is determined from target similar data of the abnormal data, and a data amplification operation is performed on the abnormal data according to the data time period to be amplified of the abnormal data and the target associated data corresponding to the abnormal data, so as to complete a data processing operation on the abnormal data.

[0069] As an optional implementation, in the second aspect, the specific manner in which the determining module determines the target data analysis result of the target region according to the data analysis results of all the target data comprises:

[0070] For each of the target data, biological parameter information of the target data is determined according to the data analysis result of the target data, the biological parameter information comprising one or more of a survival time length of a biological corresponding to the target data and a survival region of the biological corresponding to the target data;

[0071] For each of the target data, a category analysis result of a data category of the target data is determined according to the biological parameter information of the target data and the data classification result of the target data;

[0072] A target data analysis result of the target region is determined based on the category analysis results of the data categories of all the target data.

[0073] As an optional implementation, in the second aspect, the determining module is further configured to determine, after determining the target data analysis result of the target region according to the data analysis results of all the target data, plant and animal growth information of the target region according to the target data analysis result of the target region, wherein the plant and animal growth information of the target region comprises growth information of each kind of biological in the target region;

[0074] The judging module is further configured to judge whether there is a target biological that does not meet a preset growth condition in the plant and animal growth information of the target region;

[0075] The determining module is further configured to, when the judging module judges that there is a target biological that does not meet the preset growth condition in the plant and animal growth information of the target region, determine, for each of the target biological, a target growth reason why the target biological does not meet the preset growth condition;

[0076] The determining module is further configured to determine, based on the target growth reason of each of the target biological, an environment improvement parameter of the target region;

[0077] The executing module is further configured to perform an operation matching the environment improvement parameter on the target region, so as to improve the living quality of plants and animals in the target region.

[0078] The third aspect of the present application discloses another intelligent plant and animal data processing device based on environmental information, which comprises:

[0079] a memory storing executable program codes;

[0080] a processor coupled with the memory;

[0081] The processor invokes the executable program codes stored in the memory to execute the intelligent plant and animal data processing method based on environmental information disclosed in the first aspect of the present application.

[0082] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, which are invoked to execute the intelligent plant and animal data processing method based on environmental information disclosed in the first aspect of the present application.

[0083] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0084] In the embodiments of the present application, the environmental information of a target area is acquired; target data reported by each data collection point in the target area is received; for each target data, the target data is input into a preset data classification model to obtain a data classification result of the target data; according to the data classification result of each target data, all target data belonging to the same data category are determined as a target data category set; based on the environmental information of the target area, a data analysis result of each target data is determined; according to the data analysis result of all target data, a target data analysis result of the target area is determined; wherein the target data analysis result comprises plant and animal survival information of the target area. It can be seen that the present application can improve the efficiency of processing plant and animal data and improve the accuracy of processing plant and animal data. BRIEF DESCRIPTION OF DRAWINGS

[0085] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0086] Figure 1 is a flowchart of an intelligent plant and animal data processing method based on environmental information disclosed by the embodiments of the present application;

[0087] Figure 2 is a flowchart of another intelligent plant and animal data processing method based on environmental information disclosed by the embodiments of the present application;

[0088] Figure 3 is a structural schematic view of another plant and animal data intelligent processing device based on environmental information according to an embodiment of the present application;

[0089] Figure 4 is a structural schematic view of another plant and animal data intelligent processing device based on environmental information according to an embodiment of the present application;

[0090] Figure 5 is a structural schematic view of another plant and animal data intelligent processing device based on environmental information according to an embodiment of the present application. DETAILED DESCRIPTION

[0091] In order to make the personnel in the art better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0092] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or end.

[0093] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor does it necessarily refer to a separate or alternative embodiment. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined.

[0094] The present application discloses a plant and animal data intelligent processing method and device based on environmental information, which can improve the efficiency of processing plant and animal data and improve the accuracy of processing plant and animal data. The following are described in detail.

[0095] Embodiment one

[0096] Please refer to Figure 1 , Figure 1It is a flowchart of an intelligent processing method for animal and plant data based on environmental information disclosed by the embodiment of the present application. Among them, Figure 1 The intelligent processing method for animal and plant data based on environmental information described can be applied to intelligent processing of animal and plant data based on environmental information, and can also be applied to intelligent processing of animal and plant data based on environmental information. The embodiment of the present application is not limited. For example Figure 1 As shown in the figure, the intelligent processing method for animal and plant data based on environmental information can include the following operations:

[0097] 101, obtaining environmental information of a target area.

[0098] In the embodiment of the present application, the environmental information includes one or more of the area location information of the target area, the climate information of the target area, the animal species information of the target area, the plant species information of the target area, the data collection point information of the target area, and the terrain information of the target area.

[0099] In the embodiment of the present application, the environmental information of the target area can be obtained in real time, can be obtained at a predetermined time interval, or can be obtained when the animal and plant data in the target area need to be processed. The embodiment of the present application is not limited.

[0100] 102, receiving target data reported by each data collection point in the target area.

[0101] In the embodiment of the present application, the target data includes at least one animal and plant data.

[0102] In the embodiment of the present application, the target area includes at least one data collection point. Further, the target data in each data collection point can be collected by a data collection device and a data collection device, wherein the data collection device and the data collection device can be a sensor, a data collector, and the like for collecting data.

[0103] 103, for each target data, inputting the target data into a preset data classification model to obtain a data classification result of the target data.

[0104] In the embodiment of the present application, the data classification result includes the data category of the target data.

[0105] In the embodiment of the present application, the data category of the target data includes animal category and plant category. Further, the animal category includes one or more of bird category, amphibian category, insect category, and the like; and the plant category includes one or more of tree category, shrub category, vine category, grass category, angiosperm category, gymnosperm category, and bryophyte category.

[0106] 104. According to the data classification result of each target data, all target data belonging to the same data category are determined as a target data category set.

[0107] In the embodiment of the present application, the data category includes an animal data category and a plant data category, and each target data category set includes at least one target data.

[0108] In the embodiment of the present application, each target data corresponds to a data classification result.

[0109] 105. Based on the environmental information of the target area, for each target data included in each target data category set, a data analysis result of the target data is determined.

[0110] In the embodiment of the present application, the data analysis result of each target data is used to represent the growth of the target data in the target area.

[0111] 106. According to the data analysis result of all target data, a target data analysis result of the target area is determined.

[0112] In the embodiment of the present application, the target data analysis result includes the animal and plant survival information of the target area.

[0113] In the embodiment of the present application, the target data analysis result includes the data analysis result of all target data.

[0114] It can be seen that the implementation Figure 1 The described intelligent processing method of animal and plant data based on environmental information can obtain the environmental information in the target area, receive the target data reported by each data collection point in the target area, input each target data into the preset data classification model to obtain the data classification result of each target data, determine all target data belonging to the same data category as a target data category set according to the data classification result of each target data, determine the data analysis result of each target data included in each target data category set based on the environmental information of the target area, determine the target data analysis result of the target area according to the data analysis result of all target data, and determine the animal and plant survival information in the target area based on the environmental information and the target data reported by each data collection point in the target area. The user can intuitively understand the survival of animals and plants in the target area and the ecological environment in the target area, and the target data analysis result of the target area can be determined comprehensively in combination with various data, which is beneficial to improve the accuracy, intelligence and efficiency of determining the target data analysis result of the target area.

[0115] Embodiment two

[0116] Please refer to Figure 2 , Figure 2 is a flowchart of an intelligent processing method for animal and plant data based on environmental information according to an embodiment of the present application. In the figure, Figure 2 The intelligent processing method for animal and plant data based on environmental information described above can be applied to intelligent processing of animal and plant data based on environmental information, and can also be applied to intelligent processing of animal and plant data based on environmental information. The present application does not limit the embodiments. As Figure 2 shown, the intelligent processing method for animal and plant data based on environmental information can include the following operations:

[0117] 201, obtaining environmental information of a target area.

[0118] 202, receiving target data reported by each data collection point in the target area.

[0119] 203, determining whether there is an overlapping data group in all target data according to the target data reported by each data collection point.

[0120] In the present application, optionally, each overlapping data group includes at least two target data, and all target data included in each overlapping data group are the same.

[0121] In the present application, optionally, the number of overlapping data groups can be one or more, and the present application does not limit the embodiments. Further optionally, when the number of overlapping data groups is more than one, the target data included in each overlapping data group is different from the target data included in other overlapping data groups.

[0122] 204, when it is determined that there is an overlapping data group in all target data, determining the data monitoring range of each data collection point.

[0123] In the present application, optionally, when it is determined that there is no overlapping data group in all target data, step 207 is triggered directly.

[0124] 205, for each overlapping data group, determining the target data collection point of the overlapping data group according to the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group.

[0125] In the present application, optionally, the target data collection point of each overlapping data group has only one.

[0126] In the present application, optionally, the target data collection point of each overlapping data group is determined according to the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group, including:

[0127] For each data monitoring range corresponding to a data collection point of each target data, a collection center position of the data collection point is determined;

[0128] According to the environmental information and the collection center position of each data collection point, a target collection center position with the highest matching degree with the environmental information is determined, and a data collection point corresponding to the target collection center position is determined as a target data collection point of the overlapping data group.

[0129] 206. According to the target data collection point of each overlapping data group, a data deduplication operation is performed on each overlapping data group to update all target data.

[0130] In the embodiment of the application, optionally, according to the target data collection point of each overlapping data group, a data deduplication operation is performed on each overlapping data group to update all target data, comprising:

[0131] For each overlapping data group, target data reported by the target data collection point of the overlapping data group is determined as target collection data of the overlapping data group, and other target data except the target collection data is deleted from the overlapping data group to update all target data.

[0132] 207. For each target data, the target data is input into a preset data classification model to obtain a data classification result of the target data.

[0133] 208. According to the data classification result of each target data, all target data belonging to the same data category are determined as a target data category set.

[0134] 209. Based on the environmental information of the target area, for each target data included in each target data category set, a data analysis result of the target data is determined.

[0135] 210. According to the data analysis result of all target data, a target data analysis result of the target area is determined.

[0136] In the embodiment of the application, for the detailed description of steps 201-202, 207-210, please refer to the other description of steps 101-106 in embodiment one, and the embodiment of the application will not be described again.

[0137] It can be seen that the implementation Figure 2The described environment information-based plant and animal data intelligent processing method can determine whether there is an overlapping data group in all target data according to the target data reported by each data collection point. If there is, the data monitoring range of each data collection point is determined, and the target data collection point of each overlapping data group is determined according to the environment information and the data monitoring range corresponding to the data collection point of each target data included in each overlapping data. The overlapping data in the overlapping data group is removed by performing a data deduplication operation on the overlapping data group according to the target data collection point of each overlapping data group to update all target data, which can improve the simplicity of the data and the efficiency and workload of subsequent analysis and processing operations on the data, improve the intelligence and efficiency of processing plant and animal data, and improve the accuracy and reliability of the deduplication operation by determining the target data collection point of each overlapping data group, which improves the accuracy and reliability of updating the target data, and further improves the accuracy and efficiency of generating the target data analysis result of the target region.

[0138] In an optional embodiment, after determining the target data analysis result of the target region according to the data analysis result of all target data, the method further comprises:

[0139] According to the target data analysis result of the target region, it is determined whether there is abnormal data that does not meet the preset data condition in the target data analysis result;

[0140] When it is determined that there is abnormal data that does not meet the preset data condition in the target data analysis result, the abnormal category of each abnormal data is determined;

[0141] For each abnormal data, the abnormal processing mode of the abnormal data is determined according to the abnormal category of the abnormal data, and the abnormal processing mode includes one or more of a data expansion processing mode and a data merging processing mode;

[0142] For each abnormal data, the data processing operation matched with the abnormal processing mode of the abnormal data is performed on the abnormal data.

[0143] In this optional embodiment, optionally, the number of abnormal data can be one or more.

[0144] In this optional embodiment, optionally, when it is determined that there is no abnormal data that does not meet the preset data condition in the target data analysis result, the process can be ended.

[0145] In this optional embodiment, optionally, the abnormal category includes one or more of a data missing category, a data duplication category, and a data error category.

[0146] It can be seen that the optional embodiment can determine whether there is abnormal data in the target data according to the target data analysis result of the target region, and if so, determine the abnormal category of each abnormal data, determine the abnormal processing mode of each abnormal data, and perform a data processing operation matching the abnormal processing mode of each abnormal data. The abnormal category of each abnormal data can be determined when it is determined that there is abnormal data, and the corresponding abnormal processing mode can be further determined, which can improve the intelligence and reliability of processing animal and plant data, improve the accuracy of processing animal and plant data, and realize processing and analysis of animal and plant data based on environmental information, thereby improving the rationality and accuracy of analyzing animal and plant data.

[0147] In another optional embodiment, determining the abnormal category of each abnormal data comprises:

[0148] Determining the data collection information of each abnormal data, the data collection information comprising collection time information of each abnormal data and collection frequency information of each abnormal data;

[0149] For each abnormal data, determining collection interval time period information of the abnormal data according to the collection time information of the abnormal data and the collection frequency information of the abnormal data;

[0150] According to the collection interval time period information of each abnormal data, determining whether there is first data abnormal information in which the collection interval time period is greater than or equal to a preset first interval time period threshold in all collection interval time period information;

[0151] When it is determined that there is first data abnormal information in all collection interval time period information, determining the abnormal category of the abnormal data corresponding to each first data abnormal information as a data missing abnormal category;

[0152] Determining whether there is second data abnormal information in which the collection interval time period is less than a preset second interval time period threshold in all collection interval time period information;

[0153] When it is determined that there is second data abnormal information in all data collection information, determining the abnormal category of the abnormal data corresponding to each second data abnormal information as a data repetition abnormal category;

[0154] Wherein, the first interval time period threshold is greater than the second interval time period threshold.

[0155] In this optional embodiment, optionally, the collection frequency information of each abnormal data is used to represent the frequency of collecting the abnormal data in a time period.

[0156] In the optional embodiment, optionally, when it is judged that there is no first data abnormal information with a collection interval time period greater than or equal to the preset first interval time period threshold and no second data abnormal information with a collection interval time period less than the preset second interval time period threshold in all collection interval time period information, the current process can be ended.

[0157] It can be seen that, by implementing the optional embodiment, the collection interval time period information can be determined based on the collection time information and the collection frequency information of each abnormal data, it can be judged whether there is first data abnormal information with a collection interval time period greater than or equal to the preset first interval time period threshold, if there is, the abnormal category of the abnormal data corresponding to each first data abnormal information is determined as the data missing abnormal category, and it can be judged whether there is second data abnormal information with a collection interval time period less than the preset second interval time period threshold in the collection interval time period information, if there is, the abnormal category of the abnormal data corresponding to each second data abnormal information is determined as the data repetition abnormal category, the collection interval time period information can be determined based on the collection time information and the collection frequency information of each abnormal data, the accuracy and reliability of determining the collection interval time period information of each abnormal data can be improved, the efficiency and intelligence of determining the collection interval time period information of each abnormal data can be improved, the accuracy and reliability of determining the abnormal category of each abnormal data can be improved by determining whether there is first data abnormal information or second data abnormal information based on the collection interval time period information of each abnormal data, and the intelligence of determining the abnormal category of each abnormal data can be improved, thereby facilitating to improve the accuracy and reliability of performing data processing operation matching the abnormal processing mode of each abnormal data, and further facilitating to improve the rationality and accuracy of analyzing the animal and plant data.

[0158] In yet another optional embodiment, when the abnormal processing mode of the abnormal data includes a data expansion processing mode, for each abnormal data, performing data processing operation matching the abnormal processing mode of the abnormal data on the abnormal data includes:

[0159] For each abnormal data, the to-be-expanded data time period of the abnormal data is determined according to the collection interval time period information of the abnormal data, the similarity between the abnormal data and the data analysis result of each target data is calculated according to the data analysis result of each target data and the environmental information, to obtain a data similarity set of the abnormal data, the highest similarity is determined from the data similarity set of the abnormal data, and the target data corresponding to the highest similarity is determined as the target similar data of the abnormal data.

[0160] For each abnormal data, target associated data matching the to-be-enlarged data time period of the abnormal data is determined from target similar data of the abnormal data, and a data enlargement operation is performed on the abnormal data according to the to-be-enlarged data time period of the abnormal data and the target associated data corresponding to the abnormal data, so as to complete a data processing operation on the abnormal data.

[0161] In the optional embodiment, the number of the to-be-enlarged data time period of each abnormal data can be one or multiple, which is not limited in the embodiment of the application.

[0162] In the optional embodiment, the data similarity set of each abnormal data includes a similarity between the abnormal data and a data analysis result of each target data.

[0163] In the optional embodiment, for each abnormal data, the target associated data matching the to-be-enlarged data time period of the abnormal data is determined from target similar data of the abnormal data, including:

[0164] For each abnormal data, target time period identical to the to-be-enlarged data time period of the abnormal data is determined from target similar data of the abnormal data, and data corresponding to the target time period is determined as target associated data of the abnormal data.

[0165] In the optional embodiment, the data enlargement operation is performed on the abnormal data according to the to-be-enlarged data time period of the abnormal data and the target associated data corresponding to the abnormal data, so as to complete a data processing operation on the abnormal data, including:

[0166] According to the to-be-enlarged data time period of the abnormal data and the target associated data corresponding to the abnormal data, the target associated data corresponding to the abnormal data is supplemented into the abnormal data, so as to complete a data enlargement operation and further complete a data processing operation on the abnormal data; or,

[0167] The to-be-enlarged data time period of the abnormal data and the target associated data corresponding to the abnormal data are input into a preset data enlargement model, to obtain a data enlargement result of the abnormal data, and the data enlargement result of the abnormal data is supplemented into the abnormal data, so as to complete a data enlargement operation and further complete a data processing operation on the abnormal data.

[0168] It can be seen that, by implementing the optional embodiment, the to-be-expanded data time period can be determined according to the collection interval time period information of each abnormal data, the data similarity set can be obtained by calculating the similarity between each abnormal data and the data analysis result of each target data according to the data analysis result of each target data and the environmental information, the highest similarity can be determined from the data similarity set of each abnormal data, and the target similar data can be determined, the target associated data matching the to-be-expanded data time period can be determined from the target similar data of each abnormal data, and the data expansion operation is further performed to complete the data processing operation on the abnormal data, which can improve the accuracy and reliability of performing the data processing operation matching the abnormal processing mode of each abnormal data, and can determine the target associated data based on the collection interval time period information and the similarity to determine the target similar data, which can improve the accuracy and reliability of performing the data expansion operation on the abnormal data, is beneficial to improve the accuracy and reliability of processing the abnormal data, and further realizes processing and analyzing the animal and plant data based on the environmental information, which is further beneficial to improve the rationality and accuracy of analyzing the animal and plant data.

[0169] In yet another optional embodiment, according to the data analysis result of all target data, the target data analysis result of the target region is determined, including:

[0170] For each target data, according to the data analysis result of the target data, the biological parameter information of the target data is determined, and the biological parameter information includes one or more of the survival time of the biological corresponding to the target data and the survival region of the biological corresponding to the target data;

[0171] For each target data, according to the biological parameter information of the target data and the data classification result of the target data, the category analysis result of the data category of the target data is determined.

[0172] Based on the category analysis result of the data category of all target data, the target data analysis result of the target region is determined.

[0173] In this optional embodiment, optionally, the category analysis result of the data category of the target data includes the analysis result of the data category of the target data in the target region.

[0174] In this optional embodiment, optionally, based on the category analysis result of the data category of all target data, the target data analysis result of the target region is determined, including: determining the category analysis result of the data category of all target data as the target data analysis result of the target region.

[0175] It can be seen that, by implementing the optional embodiment, the biological parameter information of each target data can be determined according to the data analysis result of the target data, the category analysis result of the data category of the target data can be determined according to the biological parameter information of the target data and the data category result of the target data, the target data analysis result of the target region can be determined based on the category analysis results of the data categories of all target data, the category analysis result of the target data can be determined based on the biological parameter information of the target data and the data category result of the target data, the target data analysis result can be determined based on the comprehensive determination of the biological parameter information of the target data and the data category result of the target data, the target data analysis result can be determined based on the comprehensive determination of multiple parameters, the intelligence of determining the target data analysis result can be improved, the accuracy and reliability of determining the target data analysis result can be improved, and the processing and analysis of the plant and animal data based on the environmental information can be realized, thereby improving the rationality and accuracy of analyzing the plant and animal data.

[0176] In yet another optional embodiment, after determining the target data analysis result of the target region according to the data analysis results of all target data, the method further comprises:

[0177] determining the plant and animal growth information of the target region according to the target data analysis result of the target region, wherein the plant and animal growth information of the target region comprises the growth information of each type of organism in the target region;

[0178] determining whether there is a target organism that does not meet the preset growth condition in the plant and animal growth information of the target region;

[0179] when it is determined that there is a target organism that does not meet the preset growth condition in the plant and animal growth information of the target region, for each target organism, determining the target growth reason why the target organism does not meet the preset growth condition;

[0180] based on the target growth reason of each target organism, determining the environmental improvement parameter of the target region, and performing an operation matching the environmental improvement parameter on the target region to improve the survival quality of the plants and animals in the target region.

[0181] In this optional embodiment, optionally, when it is determined that there is no target organism that does not meet the preset growth condition in the plant and animal growth information of the target region, the process can be ended.

[0182] In this optional embodiment, optionally, the plant and animal growth information of the target region further comprises one or more of the growth duration information of each type of organism in the target region, the growth region information of each type of organism in the target region, the growth nutrition parameter information of each type of organism in the target region, and the growth quantity information of each type of organism in the target region.

[0183] In the optional embodiment, optionally, the environment improvement parameter of the target region is determined based on the target growth reason of each target organism, including:

[0184] The reason keyword is determined based on the target growth reason of each target organism, and the target improvement parameter matched with the reason keyword is determined from the preset environment improvement parameter library, and the target improvement parameter is determined as the environment improvement parameter of the target region; or,

[0185] The reason keyword is determined based on the target growth reason of each target organism, and the target improvement parameter is generated according to the reason keyword, and the target improvement parameter is determined as the environment improvement parameter of the target region.

[0186] It can be seen that the optional embodiment can determine the plant and animal growth information of the target region according to the target data analysis result of the target region, and determine whether there is a target organism that does not meet the preset growth condition in the plant and animal growth information of the target region. If there is, the target growth reason of each target organism that does not meet the preset growth condition is determined, the environment improvement parameter of the target region is determined based on the target growth reason, and the operation matched with the environment improvement parameter is performed to improve the survival quality of the plants and animals in the target region. The environment improvement parameter of the target region can be intelligently generated to improve the survival quality of the plants and animals in the target region, and the ecological environment of the target region can be better protected, and then the plant and animal data can be processed and analyzed based on the environment information, and then the rationality and accuracy of analyzing and processing the plant and animal data can be improved.

[0187] Embodiment three

[0188] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of a plant and animal data intelligent processing device based on environment information disclosed by the embodiment of the application. As Figure 3 shown, the plant and animal data intelligent processing device based on environment information can include:

[0189] The acquisition module 301 is configured to acquire environment information of a target region, and the environment information includes one or more of region location information of the target region, climate information of the target region, animal species information of the target region, plant species information of the target region, data collection point information of the target region, and terrain information of the target region.

[0190] The receiving module 302 is configured to receive target data reported by each data collection point in the target region, and the target data includes at least one plant and animal data.

[0191] The input module 303 is used to input each target data into a preset data classification model to obtain the data classification result of the target data, which includes the data category of the target data.

[0192] The determination module 304 is used to determine all target data belonging to the same data category as a target data category set based on the data classification results of each target data; the data categories include animal data category and plant data category, and each target data category set includes at least one target data;

[0193] The determination module 304 is also used to determine the data analysis result of each target data included in each target data category set based on the environmental information of the target area;

[0194] The determination module 304 is also used to determine the target data analysis results of the target area based on the data analysis results of all target data; wherein, the target data analysis results include the survival information of plants and animals in the target area.

[0195] It is evident that implementation Figure 3 The described device can acquire environmental information within a target area, receive target data reported by each data collection point within the target area, obtain data classification results for each target data input value from a preset data classification model, and, based on the data classification results of each target data, determine all target data belonging to the same data category as a target data category set. Based on the environmental information of the target area, it determines the data analysis results for each target data included in each target data category set. Based on the data analysis results of all target data, it determines the target data analysis results for the target area. It can determine the survival information of plants and animals within the target area based on environmental information and target data reported by each data collection point within the target area, allowing users to intuitively understand the survival status of plants and animals and the ecological environment within the target area. It can comprehensively determine the target data analysis results for the target area by combining data from multiple aspects, which is beneficial to improving the accuracy, intelligence, and efficiency of determining the target data analysis results for the target area.

[0196] In an optional embodiment, such as Figure 4 As shown, the device also includes:

[0197] The judgment module 305 is used to determine whether there is an overlapping data group among all the target data after the receiving module 302 receives the target data reported by each data collection point in the target area, based on the target data reported by each data collection point. The overlapping data group includes each target data with the same data monitoring range and each target data included in the overlapping data group comes from different data collection points.

[0198] The determination module 304 is also used to determine the data monitoring range of each data collection point when the judgment module 305 determines that there are overlapping data groups among all target data.

[0199] The determination module 304 is also used to determine the target data collection point of each overlapping data group based on the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group.

[0200] The update module 306 is used to perform a data deduplication operation on each overlapping data group based on the target data collection points of each overlapping data group, so as to update all target data.

[0201] It is evident that implementation Figure 4 The described device can determine whether there are overlapping data groups among all target data based on the target data reported by each data acquisition point. If so, it determines the data monitoring range of each data acquisition point and, based on environmental information and the data monitoring range corresponding to the data acquisition point of each target data included in each overlapping data group, determines the target data acquisition point for each overlapping data group. It then performs a data deduplication operation on the overlapping data groups based on the target data acquisition points of each overlapping data group to update all target data. This process removes overlapping data from overlapping data groups, improving data simplicity and increasing the efficiency and workload of subsequent data analysis and processing. It is beneficial for improving the intelligence and efficiency of processing plant and animal data. Furthermore, by determining the target data acquisition points for each overlapping data group and then performing deduplication on each overlapping data group, it improves the accuracy and reliability of the deduplication operation, the accuracy and reliability of updating target data, and ultimately, the accuracy and efficiency of generating target data analysis results for the target area.

[0202] In another alternative embodiment, such as Figure 4 As shown, the judgment module 305 is also used to determine whether there is any abnormal data in the target data analysis results that do not meet the preset data conditions after the determination module 304 determines the target data analysis results of the target area based on the data analysis results of all target data.

[0203] The determination module 304 is also used to determine the abnormality category of each abnormal data when the judgment module 305 determines that there is abnormal data in the target data analysis result that does not meet the preset data conditions;

[0204] The determination module 304 is also used to determine the abnormal handling method for each abnormal data according to the abnormal category of the abnormal data. The abnormal handling method includes one or more of the following: data amplification processing method and data merging processing method.

[0205] The device also includes:

[0206] The execution module 307 is used to perform data processing operations on each abnormal data that match the abnormal data's exception handling method.

[0207] It is evident that implementation Figure 4 The described device can determine whether there is abnormal data in the target data based on the target data analysis results of the target area. If there is abnormal data, it determines the abnormality category of each abnormal data, determines the abnormality handling method for each abnormal data, and performs data processing operations matching the abnormality handling method for each abnormal data. It can determine the abnormality category for each abnormal data and further determine the corresponding abnormality handling method when abnormal data is determined to exist. This can improve the intelligence and reliability of processing animal and plant data, improve the accuracy of processing animal and plant data, and realize the processing and analysis of animal and plant data based on environmental information, thereby improving the rationality and accuracy of animal and plant data analysis.

[0208] In yet another alternative embodiment, such as Figure 4 As shown, the specific methods by which module 304 determines the anomaly category for each piece of abnormal data include:

[0209] Determine the data collection information for each abnormal data point, including the collection time and collection frequency of each abnormal data point.

[0210] For each abnormal data point, the collection interval time period is determined based on the collection time information and collection frequency information of the abnormal data.

[0211] Based on the collection interval information of each abnormal data, determine whether there is a first data abnormal information in all collection interval information that has a collection interval greater than or equal to the preset first interval threshold.

[0212] When it is determined that there is first data anomaly information in all the data collection interval time period information, the anomaly category of the abnormal data corresponding to each first data anomaly information is determined as the data missing anomaly category;

[0213] determining whether there is second data abnormal information in which the collection interval time period is less than the preset second interval time period threshold in all collection interval time period information;

[0214] When it is determined that there is second data abnormal information in all data collection information, determining the abnormal category of the abnormal data corresponding to each second data abnormal information as a data duplication abnormal category;

[0215] The first interval time period threshold is greater than the second interval time period threshold.

[0216] It can be seen that the implementation Figure 4 The described device can determine the collection interval time period information based on the collection time information and the collection frequency information of each abnormal data, determine whether there is first data abnormal information in which the collection interval time period is greater than or equal to the preset first interval time period threshold, and if so, determine the abnormal category of the abnormal data corresponding to each first data abnormal information as a data missing abnormal category; and determine whether there is second data abnormal information in which the collection interval time period is less than the preset second interval time period threshold in the collection interval time period information, and if so, determine the abnormal category of the abnormal data corresponding to each second data abnormal information as a data duplication abnormal category. The device can determine the collection interval time period information based on the collection time information and the collection frequency information of each abnormal data, which can improve the accuracy and reliability of determining the collection interval time period information of each abnormal data, and can improve the efficiency and intelligence of determining the collection interval time period information of each abnormal data. In addition, by determining whether there is first data abnormal information or second data abnormal information based on the collection interval time period information of each abnormal data, and then determining the abnormal category of each abnormal data, the accuracy and reliability of determining the abnormal category of each abnormal data can be improved, and the intelligence of determining the abnormal category of each abnormal data can be improved, thereby facilitating the improvement of the accuracy and reliability of performing data processing operations on each abnormal data that match the abnormal processing mode of the abnormal data, and thereby facilitating the improvement of the rationality and accuracy of analyzing the data of animals and plants.

[0217] In yet another optional embodiment, as Figure 4 shown, when the abnormal processing mode of the abnormal data includes a data expansion processing mode, the specific way in which the execution module 307 performs, for each abnormal data, a data processing operation that matches the abnormal processing mode of the abnormal data includes:

[0218] For each abnormal data, the time period of data to be amplified is determined based on the collection interval information of the abnormal data. Based on the data analysis results of each target data and environmental information, the similarity between the abnormal data and the data analysis results of each target data is calculated to obtain the data similarity set of the abnormal data. The highest similarity is determined from the data similarity set of the abnormal data, and the target data corresponding to the highest similarity is determined as the target similar data of the abnormal data.

[0219] For each anomalous data, target related data that matches the time period of the data to be augmented is determined from the target similar data of the anomalous data. Then, data augmentation operation is performed on the anomalous data according to the time period of the data to be augmented and the target related data corresponding to the anomalous data, so as to complete the data processing operation on the anomalous data.

[0220] It is evident that implementation Figure 4 The described device can determine the time period of data to be amplified based on the collection interval information of each abnormal data, and calculate the similarity between the data analysis results of each abnormal data and each target data based on the data analysis results of each target data and environmental information to obtain a data similarity set. It then determines the highest similarity from the data similarity set of each abnormal data to identify the target similar data. Furthermore, it identifies target related data matching the time period of the data to be amplified from the target similar data of each abnormal data, and performs data amplification to complete the data processing operation for the abnormal data. This improves the accuracy and reliability of performing data processing operations matching the abnormal data's anomaly processing method. Moreover, it can determine target related data based on the collection interval information and similarity, improving the accuracy and reliability of data amplification operations on abnormal data. This enhances the accuracy and reliability of abnormal data processing, enabling the processing and analysis of plant and animal data based on environmental information, thereby improving the rationality and accuracy of plant and animal data analysis.

[0221] In yet another alternative embodiment, such as Figure 4 As shown, the specific methods by which the determining module 304 determines the target data analysis results for the target area based on the data analysis results of all target data include:

[0222] For each target data, based on the data analysis results of the target data, the biological parameter information of the target data is determined. The biological parameter information includes one or more of the following: the survival time of the organism corresponding to the target data and the survival region of the organism corresponding to the target data.

[0223] For each target data point, the category analysis results are determined based on the biological parameter information and data classification results of the target data to determine the data category of the target data.

[0224] Based on the category analysis results of all target data, the target data analysis results for the target region are determined.

[0225] It is evident that implementation Figure 4 The described device can determine biological parameter information based on the data analysis results of each target data, and determine the category analysis results of the target data based on the biological parameter information and data classification results of the target data. Based on the category analysis results of the data categories of all target data, it can determine the target data analysis results of the target area. It can comprehensively determine the category analysis results of the target data based on the biological parameter information and data classification results of the target data, and thus determine the target data analysis results. It can comprehensively determine the target data analysis results by combining multiple parameters, which can improve the intelligence, accuracy and reliability of the target data analysis results. In this way, it can realize the processing and analysis of animal and plant data based on environmental information, which is conducive to improving the rationality and accuracy of animal and plant data analysis.

[0226] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 304 is further configured to determine the plant and animal growth information of the target area based on the target data analysis results of the target area after determining the target data analysis results of the target area based on the data analysis results of all target data, wherein the plant and animal growth information of the target area includes the growth information of each type of organism in the target area.

[0227] The judgment module 305 is also used to determine whether there are target organisms in the plant and animal growth information of the target area that do not meet the preset growth conditions;

[0228] The determining module 304 is also used to determine the target growth reason for each target organism that does not meet the preset growth conditions when the judging module 305 determines that there are target organisms in the plant and animal growth information of the target area that do not meet the preset growth conditions.

[0229] The determination module 304 is also used to determine environmental improvement parameters for the target area based on the target growth causes of each target organism;

[0230] The execution module 307 is also used to perform operations on the target area that match the environmental improvement parameters in order to improve the survival quality of plants and animals in the target area.

[0231] It is evident that implementation Figure 5The described device can determine the plant and animal growth information of the target region according to the target data analysis result of the target region, and determine whether there is a target organism that does not meet the preset growth condition in the plant and animal growth information of the target region, if there is, determine the target growth reason why each target organism does not meet the preset growth condition, determine the environment improvement parameter of the target region based on the target growth reason, and perform an operation matched with the environment improvement parameter to improve the survival quality of the plants and animals in the target region, can intelligently generate the environment improvement parameter of the target region to improve the survival quality of the plants and animals in the target region, can better realize the protection of the ecological environment of the target region, and then realize the processing and analysis of the plant and animal data based on the environment information, and then help to improve the rationality and accuracy of the analysis and processing of the plant and animal data.

[0232] Embodiment four

[0233] Please refer to Figure 5 , Figure 5 is another structure diagram of the plant and animal data intelligent processing device based on environment information disclosed by the embodiment of the application. As ​ shown, the plant and animal data intelligent processing device based on environment information can include:

[0234] The memory 401 stores executable program codes;

[0235] The processor 402 is coupled with the memory 401;

[0236] The processor 402 calls the executable program codes stored in the memory 401, and executes the steps in the plant and animal data intelligent processing method based on environment information described in the embodiment one or the embodiment two of the application.

[0237] Embodiment five

[0238] The embodiment of the application discloses a computer storage medium, which stores computer instructions, and the computer instructions are used to execute the steps in the plant and animal data intelligent processing method based on environment information described in the embodiment one or the embodiment two of the application when called.

[0239] Embodiment six

[0240] The embodiment of the application discloses a computer program product, which includes a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to make a computer execute the steps in the plant and animal data intelligent processing method based on environment information described in the embodiment one or the embodiment two.

[0241] The apparatus embodiments described above are only illustrative, wherein the modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected to achieve the purposes of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0242] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and necessary general hardware platform, and of course can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of software products, and the computer software products can be stored in a computer readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0243] Finally, it should be noted that: the plant and animal data intelligent processing method and device based on environmental information disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for intelligent processing of plant and animal data based on environmental information, characterized in that, The method includes: Obtain environmental information of the target area, including one or more of the following: the target area's location information, climate information, animal species information, plant species information, data collection point information, and terrain information. Receive target data reported by each data collection point within the target area, wherein the target data includes at least one plant and animal data; For each target data, the target data is input into a preset data classification model to obtain the data classification result of the target data, the data classification result including the data category of the target data; Based on the data classification results of each target data, all target data belonging to the same data category are identified as a target data category set; the data categories include animal data category and plant data category, and each target data category set includes at least one target data; Based on the environmental information of the target area, for each target data included in each target data category set, the data analysis result of the target data is determined; the data analysis result of each target data is used to represent the growth status of the target data in the target area; Based on the data analysis results of all the target data, the target data analysis results for the target area are determined; wherein, the target data analysis results include the plant and animal survival information of the target area.

2. The intelligent processing method for plant and animal data based on environmental information according to claim 1, characterized in that, After receiving the target data reported by each data collection point within the target area, the method further includes: Based on the target data reported by each data collection point, determine whether there is an overlapping data group among all the target data. The data monitoring range corresponding to each target data included in the overlapping data group is the same, and each target data included in the overlapping data group comes from different data collection points. When it is determined that there are overlapping data groups among all the target data, the data monitoring range of each data collection point is determined; For each of the overlapping data groups, the target data collection point of the overlapping data group is determined based on the environmental information and the data monitoring range corresponding to the data collection point of each target data included in the overlapping data group. Based on the target data collection points of each overlapping data group, a data deduplication operation is performed on each overlapping data group to update all the target data.

3. The intelligent processing method for plant and animal data based on environmental information according to claim 2, characterized in that, After determining the target data analysis results for the target region based on the data analysis results of all the target data, the method further includes: Based on the target data analysis results of the target area, determine whether there is any abnormal data in the target data analysis results that does not meet the preset data conditions; When it is determined that there is abnormal data in the target data analysis results that does not meet the preset data conditions, the abnormality category of each abnormal data is determined; For each of the abnormal data, the abnormal handling method is determined according to the abnormal category of the abnormal data. The abnormal handling method includes one or more of the following: data amplification processing method and data merging processing method. For each of the abnormal data, perform a data processing operation that matches the exception handling method for that abnormal data.

4. The intelligent processing method for plant and animal data based on environmental information according to claim 3, characterized in that, Determining the anomaly category for each of the anomalous data includes: Determine the data acquisition information for each of the abnormal data, wherein the data acquisition information includes the acquisition time information and the acquisition frequency information for each of the abnormal data; For each of the abnormal data, the collection interval time period information of the abnormal data is determined based on the collection time information and the collection frequency information of the abnormal data. Based on the collection interval information of each abnormal data, determine whether there is a first data abnormal information in all the collection interval information that has a collection interval greater than or equal to a preset first interval threshold. When it is determined that the first data anomaly information exists in all the collection interval time period information, the anomaly category of the abnormal data corresponding to each first data anomaly information is determined as the data missing anomaly category. Determine whether there is any second data anomaly information among all the collected interval time information that is less than a preset second interval time threshold; When it is determined that the second data anomaly information exists in all the data collection information, the anomaly category of the abnormal data corresponding to each second data anomaly information is determined as the data duplication anomaly category; Wherein, the first interval time threshold is greater than the second interval time threshold.

5. The intelligent processing method for plant and animal data based on environmental information according to claim 4, characterized in that, When the anomaly handling method for the abnormal data includes the data augmentation processing method, the step of performing a data processing operation matching the anomaly handling method for each abnormal data includes: For each of the abnormal data, the time period for data to be amplified is determined based on the collection interval information of the abnormal data. Based on the data analysis results of each of the target data and the environmental information, the similarity between the abnormal data and the data analysis results of each of the target data is calculated to obtain the data similarity set of the abnormal data. The highest similarity is determined from the data similarity set of the abnormal data, and the target data corresponding to the highest similarity is determined as the target similar data of the abnormal data. For each anomalous data, target related data matching the time period of the data to be augmented is determined from the target similar data of the anomalous data, and data augmentation operation is performed on the anomalous data according to the time period of the data to be augmented and the target related data corresponding to the anomalous data, so as to complete the data processing operation on the anomalous data.

6. The intelligent processing method for plant and animal data based on environmental information according to claim 5, characterized in that, The step of determining the target data analysis results for the target region based on the data analysis results of all the target data includes: For each target data, based on the data analysis results of the target data, the biological parameter information of the target data is determined. The biological parameter information includes one or more of the following: the survival time of the organism corresponding to the target data and the survival region of the organism corresponding to the target data. For each target data, the category analysis result of the data category of the target data is determined based on the biological parameter information of the target data and the data classification result of the target data; Based on the category analysis results of all the target data categories, the target data analysis results for the target region are determined.

7. The intelligent processing method for plant and animal data based on environmental information according to claim 6, characterized in that, After determining the target data analysis results for the target region based on the data analysis results of all the target data, the method further includes: Based on the target data analysis results of the target area, the plant and animal growth information of the target area is determined, wherein the plant and animal growth information of the target area includes the growth information of each species of organism in the target area; Determine whether there are any target organisms in the plant and animal growth information of the target area that do not meet the preset growth conditions; When it is determined that there are target organisms in the plant and animal growth information of the target area that do not meet the preset growth conditions, for each target organism, the target growth reason for the target organism not meeting the preset growth conditions is determined. Based on the target growth causes of each target organism, environmental improvement parameters for the target area are determined, and operations matching the environmental improvement parameters are performed on the target area to improve the survival quality of plants and animals in the target area.

8. A smart data processing device for plants and animals based on environmental information, characterized in that, The device includes: The acquisition module is used to acquire environmental information of the target area, the environmental information including one or more of the following: the regional location information of the target area, the climate information of the target area, the animal species information of the target area, the plant species information of the target area, the data collection point information of the target area, and the terrain information of the target area; The receiving module is used to receive target data reported by each data collection point within the target area, wherein the target data includes at least one plant and animal data. The input module is used to input each target data into a preset data classification model to obtain a data classification result for the target data, wherein the data classification result includes the data category of the target data; The determination module is used to determine all the target data belonging to the same data category as a target data category set based on the data classification result of each target data; the data categories include animal data category and plant data category, and each target data category set includes at least one target data; The determining module is further configured to, based on the environmental information of the target area, determine the data analysis result of each target data included in each target data category set; the data analysis result of each target data is used to represent the growth status of the target data in the target area; The determining module is further configured to determine the target data analysis results of the target area based on the data analysis results of all the target data; wherein the target data analysis results include the plant and animal survival information of the target area.

9. A smart data processing device for plants and animals based on environmental information, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent processing method for plant and animal data based on environmental information as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the intelligent processing method for plant and animal data based on environmental information as described in any one of claims 1-7.

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