A mulberry planting evaluation method and system based on multi-source data

Abstract

The present invention provides a mulberry planting evaluation method and system based on multi-source data. The method comprises the following steps: acquiring arrangement information of sensors and field density information by collecting multi-source data in an area to be evaluated, determining candidate identification sensors, and outputting an identification information set based on the candidate identification sensors; processing the propagation path of mulberry brown spot disease symptoms in the identification information set to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and obtaining a safe planting request set based on the multi-directional propagation extension interval; combining the safe planting request set and environmental regulation elements to confirm the pest and disease propagation trend, and obtaining a soil environment identification record based on the pest and disease propagation trend; the technical solution provided by the present invention effectively realizes the automation, refinement and intelligence of soil environment identification and evaluation for mulberry planting, and improves the ability of soil management and protection.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of environmental identification technology, and in particular to a mulberry planting evaluation method and system based on multi-source data. Background Art

[0002] As the global soil resource pest and disease problem becomes increasingly serious, the need for efficient and accurate identification of soil environment has become particularly urgent. Especially in the context of rapid urbanization and industrialization, changes in key indicators such as nutrition, fertility, and mulberry brown spot symptoms in the field directly affect the health of the ecosystem and the quality of human life.

[0003] At present, soil identification mainly relies on fixed monitoring stations and regular manual sampling and testing. The existing methods have limitations. For example, the arrangement density of existing fixed monitoring stations is limited, and identification activities cannot cover the entire field; manual sampling cycles are long and costly, and it is difficult to achieve a rapid response to sudden pest and disease incidents. Summary of the invention

[0004] The embodiment of the present invention provides a mulberry planting evaluation method and system based on multi-source data, which are used to solve the problems in the prior art that the identification activity cannot cover the entire field; the manual sampling cycle is long and the cost is high, and it is difficult to achieve a rapid response to sudden pest and disease events.

[0005] In a first aspect, an embodiment of the present invention provides a mulberry tree planting evaluation method based on multi-source data, comprising:

[0006] Collect multi-source data in the area to be evaluated to obtain sensor arrangement information and field density information;

[0007] Determining candidate identification sensors based on the arrangement information and field density information, and outputting an identification information set based on the candidate identification sensors;

[0008] The symptom propagation path of mulberry brown spot disease in the identification information set is processed to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and a safe planting request set is obtained according to the multi-directional propagation extension interval;

[0009] The safe planting request set and environmental regulation factors are combined to confirm the pest and disease transmission trend, and the soil environment identification record is obtained based on the pest and disease transmission trend.

[0010] Optionally, the symptom propagation path of mulberry brown spot disease in the identification information set is processed to obtain a pest and disease propagation degree evaluation map to simulate a multi-directional propagation extension interval, and a safe planting request set is obtained based on the multi-directional propagation extension interval, including:

[0011] Based on the identification information set, a soil abnormality propagation model and a nutrient characteristic map are constructed, and the nutrient characteristic map and the soil abnormality propagation model are used to perform synergistic coupling processing on the propagation effect of the fertility stratification field to obtain a pest and disease propagation probability characteristic matrix;

[0012] Based on the pest transmission probability characteristic matrix, a three-dimensional soil model is constructed to reversely process the pest source location and emission characteristics of the mulberry brown spot disease symptom transmission path to obtain a pest transmission degree evaluation map;

[0013] According to the pest and disease transmission degree evaluation atlas, a directional detection method is used to simulate the preset pest and disease degree stratification, and a multi-directional transmission extension interval is output. According to the multi-directional transmission extension interval, a safe planting request set is obtained.

[0014] Optionally, according to the multi-directional propagation extension interval, a safe planting request set is obtained, including:

[0015] Conduct regional detection on the multi-directional propagation extension interval and high-risk mulberry tree area, and output the maximum coverage of pests and diseases and regional infection rate of sensitive fields in all directions downward;

[0016] The geological period update coefficient is calculated by the similarity between the current flow monitoring information and the past average information, and the maximum coverage of the pests and diseases is matched with the standard maximum coverage of the corresponding pests and diseases in the surface soil quality standard according to the geological period update coefficient, and the pests and diseases matching record is output;

[0017] When the pest and disease matching record meets the preset multi-source environment, the pest and disease source positioning model is triggered to fuse the pest and disease transmission probability feature matrix with the current monitoring information flow to obtain the pest and disease source safety interval electronic fence, and a safe planting request set is obtained based on the pest and disease source safety interval electronic fence. The preset multi-source environment includes a first environment in which the overlap ratio between the high-risk area and the planting soil channel source safety zone is greater than a threshold value and a second environment in which the pest and disease coverage fluctuation index in the current monitoring information flow is greater than a fluctuation threshold value, and the threshold value is set according to the severity of the pest and disease and the soil coverage status of the soil channel source.

[0018] Optionally, when the pest matching record meets the preset multi-source environment, the pest source positioning model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain the pest source safety zone electronic fence, and a safe planting request set is obtained based on the pest source safety zone electronic fence. The preset multi-source environment includes a first environment in which the proportion of the overlap between the high-risk area and the planting soil channel source safety zone is greater than a threshold value and a second environment in which the pest coverage fluctuation index in the current monitoring information flow is greater than the fluctuation threshold value. The threshold value is set according to the severity of the pest and the soil coverage status of the soil channel source, including:

[0019] Using the pest severity classification strategy and the soil channel source soil coverage status classification standard, a threshold value association table is constructed, and according to the threshold value association table, the range proportion threshold value corresponding to the combination is searched to obtain a threshold value set, and according to the threshold value set, the area overlap ratio of the high-risk area and the planting soil channel source safety area is calculated, and the combination includes the pest severity and the soil channel source soil coverage status;

[0020] Extracting pest and disease coverage time series based on the current monitoring information flow, and calculating pest and disease coverage fluctuation index based on the pest and disease coverage time series;

[0021] If the proportion of the overlapping range of the areas is greater than the threshold value of the corresponding severity of pests and diseases and the soil coverage status of the soil channel source, it is determined to meet the first environment; if the pest and disease coverage fluctuation index is greater than the fluctuation threshold value corresponding to the preset pest and disease type, it is determined to meet the second environment;

[0022] When the area overlap ratio meets both the first environment and the second environment, the pest source location model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain an electronic fence of the pest source safety zone;

[0023] A safe planting request set is obtained based on the electronic fence of the safe zone of the pest source.

[0024] Optionally, according to the pest source safety zone electronic fence, a safe planting request set is obtained, including:

[0025] Obtaining a log of potential pest sources within the fence according to the degree coordinates of the pest source safety zone electronic fence, the threshold value set, and past information of the pest source;

[0026] According to the pest coverage fluctuation index and the soil channel source service population state parameter, the existing request classification strategy is adjusted, and the adjusted classification strategy is output. The graded planting request set is obtained by using the adjusted classification strategy, and the graded planting request set includes a shutdown request, a production limit request, and an inspection request;

[0027] Based on the log of potential pest and disease sources within the wall and the graded planting request set, combined with the current monitoring information flow and past risk information, a multi-candidate optimization algorithm is used to match the pest and disease source feature vector with the request degree to obtain a safe planting request set. The pest and disease source feature vector includes a disease index vector and a resistance evaluation vector.

[0028] Optionally, the safe planting request set and the environmental regulation elements are combined to confirm the pest and disease transmission trend, and a soil environment identification record is obtained based on the pest and disease transmission trend, including:

[0029] Matching the pest and disease transmission safety degree and the transmission rate threshold value in the safe planting request set, outputting a matching record, adjusting the sampling frequency of the candidate identification sensor according to the matching record, and outputting the adjusted identification sensor;

[0030] Using the adjusted identification sensor to perform time-space alignment processing on the current monitoring information flow, and output multi-source monitoring data;

[0031] Performing planting quality verification processing on the multi-source monitoring data to obtain pest and disease transmission evaluation indicators;

[0032] The pest and disease transmission trend is confirmed based on the pest and disease transmission evaluation index and the environmental regulation factors to obtain soil identification records. The environmental regulation factors include soil hardness regulation factors and temperature stratification regulation factors.

[0033] Optionally, determining a candidate identification sensor according to the arrangement information and the field density information, and outputting an identification information set according to the candidate identification sensor, including:

[0034] According to the arrangement information and the field density information, a preliminary identification path is obtained, and adaptive topological adjustment is performed on the multi-source data acquisition sensors in the preliminary identification path to output candidate identification sensors;

[0035] According to the candidate identification sensor, collecting nutrition information, fertility information and mulberry brown spot disease coverage monitoring information;

[0036] Compensation processing is performed on the non-uniform sampling points in the nutrition information to obtain a nutrition characteristic map, regional stratification in the fertility information is enhanced to obtain a fertility stratification field, and particle tracking modeling processing is performed on the propagation path in the mulberry brown spot disease coverage monitoring information to obtain a mulberry brown spot disease symptom propagation path;

[0037] The nutritional characteristic map, the fertility stratification field and the mulberry brown spot disease symptom propagation path are fused to obtain an identification information set.

[0038] In a second aspect, an embodiment of the present invention provides a mulberry planting evaluation system based on multi-source data, comprising:

[0039] A collection unit, used for collecting arrangement information of sensors and field density information of multi-source data acquisition in the area to be evaluated;

[0040] a determination unit, configured to determine a candidate identification sensor according to the arrangement information and the field density information, and output an identification information set according to the candidate identification sensor;

[0041] A construction unit is used to process the symptom propagation path of mulberry brown spot disease in the identification information set to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and obtain a safe planting request set based on the multi-directional propagation extension interval;

[0042] The output unit is used to combine the safe planting request set and the environmental regulation factors to confirm the pest and disease transmission trend, and obtain the soil environment identification record based on the pest and disease transmission trend.

[0043] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a mulberry planting evaluation method based on multi-source data as described in any one of the first aspects.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having a computer program request stored thereon, wherein the computer program request, when executed by a processor, implements a mulberry planting evaluation method based on multi-source data as described in any one of the first aspects.

[0045] In an embodiment of the present invention, multi-source data in the area to be evaluated are collected to obtain arrangement information and field density information of sensors; candidate identification sensors are determined based on the arrangement information and field density information, and an identification information set is output based on the candidate identification sensors; the symptom propagation path of mulberry brown spot disease in the identification information set is processed to obtain a pest and disease propagation degree evaluation map to simulate and obtain a multi-directional propagation extension interval, and a safe planting request set is obtained based on the multi-directional propagation extension interval; the pest and disease propagation trend is confirmed in combination with the safe planting request set and environmental regulation factors, and a soil environment identification record is obtained based on the pest and disease propagation trend. The technical solution provided by the present invention collects the arrangement information and field density information of the mobile monitoring sensors in the area to be evaluated, and obtains the candidate identification sensors based on this information, ensuring that the entire monitoring area can be covered, especially the blind spots under complex terrain; this improves the comprehensiveness and accuracy of soil environmental monitoring; at the same time, the layout of the candidate identification sensors is more reasonable and efficient, so as to more accurately reflect the changes in soil conditions; based on the identification information set, the soil abnormal propagation model and the nutritional characteristic map are constructed, and the soil changes can be detected and the pest propagation trend can be evaluated; the pest propagation trend is confirmed by the obtained safe planting request set, and the soil environment identification record is obtained accordingly, which is helpful to take effective measures to control the pest propagation in time and protect the safety of soil resources. The method takes advantage of the communication technology to ensure the high hardness and low latency of information transmission, further enhances the hardness and effectiveness of the safety response; at the same time, it helps to quickly confirm the specific trend of pest propagation, provide a scientific basis for formulating effective response strategies, and minimize the damage caused by pests; through the detection of the pest propagation trend, a detailed soil environment identification record is obtained, which provides strong decision support for managers and helps them formulate reasonable governance and prevention measures to protect the safety of soil resources. Among them, the geological period update coefficient is calculated by combining the similarity between the current flow monitoring information and the past average information to match the maximum coverage of pests and diseases with the standard maximum coverage, ensuring the accuracy and timeliness of soil identification; the sliding time window mechanism is combined to extract the pest and disease coverage time series and calculate the pest and disease coverage fluctuation index, ensuring the accuracy and timeliness of soil monitoring; when the pest and disease matching records meet the preset multi-source environment, the system can quickly trigger the pest and disease source positioning model, obtain the electronic fence of the pest and disease source safety zone, and formulate an effective safe planting request set.

[0046] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained through these drawings without paying creative work.

[0048] Figure 1 A workflow diagram of a mulberry planting evaluation method based on multi-source data provided by an embodiment of the present invention;

[0049] Figure 2 A second workflow diagram of a mulberry tree planting evaluation method based on multi-source data provided by an embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the structure of a mulberry planting evaluation system based on multi-source data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0052] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, units, etc., and do not represent the order of precedence, nor do they limit the "first" and "second" to be different types.

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. According to the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0054] Figure 1 A flowchart of a mulberry tree planting evaluation method based on multi-source data is provided for an embodiment of the present invention. Figure 1 As shown, the method includes:

[0055] The present invention provides a mulberry planting evaluation method based on multi-source data, such as Figure 1 ,include:

[0056] Step 101: Collect multi-source data in the area to be evaluated to obtain sensor arrangement information and field density information;

[0057] In this step, the multi-source data acquisition sensor refers to the sensor equipment that moves freely in the area to be evaluated, which is used to collect soil parameters (such as nutrition, fertility, mulberry brown spot disease symptom coverage, etc.). Arrangement information refers to the information describing the positional information of the multi-source data acquisition sensor in the soil, including three-dimensional coordinates (x, y, z) and their relative position relationship. Field density information refers to information reflecting the terrain at the bottom of the field and the surrounding environment characteristics, including soil thickness, slope, heavy metal characteristics, etc.

[0058] In actual operation, multiple multi-source data acquisition sensors are first deployed in the area to be evaluated. These sensors record their arrangement information through GPS or other positioning technologies, and use equipment such as sonar or lidar to collect field density information.

[0059] Step 102: determining candidate identification sensors according to the arrangement information and field density information, and outputting an identification information set according to the candidate identification sensors;

[0060] In this step, the candidate identification sensor refers to the optimized and adjusted multi-source data acquisition sensor layout to ensure coverage of the entire monitoring area and improve information collection efficiency. The identification information set refers to the multi-dimensional soil parameter information set collected by the multi-source data acquisition sensor, including nutritional characteristic maps, fertility stratification fields, and mulberry brown spot disease symptom transmission paths.

[0061] In this step, the system automatically obtains the optimal candidate identification sensor layout plan through the collected arrangement information and density information. For example, through algorithm detection, it is found that some areas need more dense monitoring points to cover complex terrain changes. Finally, five candidate identification sensors are determined, each of which is responsible for collecting soil parameters in a specific area, such as nutrition, fertility, and mulberry brown spot disease symptom coverage, to form a complete identification information set.

[0062] Step 103: Processing the symptom propagation path of mulberry brown spot disease in the identification information set to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and obtaining a safe planting request set based on the multi-directional propagation extension interval;

[0063] In this step, the multi-directional propagation extension range refers to the propagation range in different directions output by simulating the propagation of pests and diseases in environments with different pest and disease severity levels.

[0064] This step uses the transmission path information of mulberry brown spot disease symptoms in the identification information set and combines it with the soil abnormal transmission model to obtain a pest and disease transmission degree evaluation map.

[0065] Step 104: Combining the safe planting request set and the environmental regulation factor, confirming the pest and disease transmission trend, and obtaining the soil environment identification record according to the pest and disease transmission trend;

[0066] In this step, environmental regulating factors include soil hardness regulating factors and temperature stratification regulating factors, which affect the transmission path and rate of pests and diseases.

[0067] This step combines the obtained safe planting request set with environmental conditioning factors (such as soil hardness, temperature stratification, etc.) to further confirm the actual spread trend of pests and diseases.

[0068] The embodiment of the present invention achieves efficient and comprehensive soil monitoring by accurately collecting and processing multi-source data to obtain sensor arrangement information and field density information. Based on the obtained candidate identification sensor layout and identification information set, it is possible to accurately simulate the transmission path and interval of pests and diseases, and predict high-risk potential pest and disease events in advance. Combined with environmental regulation factors, the accuracy of pest and disease transmission trend evaluation is further improved, ensuring that effective safe planting request sets are obtained in a timely manner. This not only improves the accuracy and response hardness of soil monitoring, but also significantly enhances the ability to protect soil resources, providing strong technical support for the realization of all-round and multi-level soil management.

[0069] In modern soil management, it is crucial to detect and respond to abnormal soil transmission events in a timely manner. However, traditional monitoring methods often lack the ability to comprehensively detect multi-dimensional information, making it difficult to accurately evaluate the transmission trend and impact range of pests and diseases. In order to improve the predictability and response hardness of soil monitoring, the present invention provides a specific embodiment, in which the symptom transmission path of mulberry brown spot disease in the identification information set is processed in step 103 to obtain a pest and disease transmission degree evaluation map, so as to simulate and obtain a multi-directional transmission extension interval, and obtain a safe planting request set based on the multi-directional transmission extension interval, which specifically includes the following steps:

[0070] Step 301: constructing a soil abnormality propagation model and a nutrient characteristic map based on the identification information set, and using the nutrient characteristic map and the soil abnormality propagation model to perform synergistic coupling processing on the propagation effect of the fertility stratification field to obtain a pest and disease propagation probability characteristic matrix;

[0071] In this step, the nutritional characteristic map refers to the arrangement of nutrient coverage in the soil, which is one of the important indicators for assessing soil health. The propagation effect refers to the spread of pests and diseases in the soil caused by the soil, which usually affects the propagation path and hardness of the pests and diseases. Cooperative coupling processing refers to the comprehensive processing of different types of soil parameter information (such as nutrition and fertility) to improve the evaluation accuracy of the pest and disease propagation path. The pest and disease propagation probability characteristic matrix represents the propagation probability characteristics of pests and diseases at different times and regional locations, which is used to further detect the propagation trend of pests and diseases.

[0072] In actual operation, the soil anomaly propagation model is first constructed using the identification information set, and the nutritional characteristic map is obtained.

[0073] Step 302: constructing a three-dimensional soil model based on the pest transmission probability characteristic matrix to reversely process the pest source location and emission characteristics of the mulberry brown spot disease symptom transmission path to obtain a pest transmission degree evaluation map;

[0074] In this step, the reverse processing of the location and emission characteristics of the pest source refers to the reverse calculation of the location and emission characteristics of the pest source through the pest transmission path information. The pest transmission degree evaluation map refers to the evaluation of the degree range of pest transmission in the soil, which helps to identify potential pest areas.

[0075] This step uses the obtained pest and disease transmission probability characteristic matrix to systematically construct a three-dimensional soil model to simulate the spread of pests and diseases in a three-dimensional area.

[0076] Step 303: Based on the pest and disease transmission degree evaluation map, a direction detection method is used to simulate the preset pest and disease degree stratification, and a multi-directional transmission extension interval is output. Based on the multi-directional transmission extension interval, a safe planting request set is obtained;

[0077] In this step, the system uses the direction detection method to simulate the pest spread situation under different pest spread environments through the obtained pest spread evaluation map.

[0078] The embodiments of the present invention accurately simulate the propagation paths and intervals of pests and diseases in the soil by constructing a soil abnormal propagation model and a nutritional characteristic map; utilize a collaborative coupling processing method to improve the evaluation accuracy of the pest and disease propagation paths; through a three-dimensional soil model, the location of the pest and disease source and its emission characteristics can be accurately inferred to obtain a detailed pest and disease propagation degree evaluation map; a direction detection method is used to simulate the propagation conditions under different pest and disease degree environments, obtain multi-directional propagation extension intervals, and ensure that an effective safe planting request set is obtained in a timely manner.

[0079] When faced with complex soil pest and disease events, accurately assessing the spread range and impact of pests and diseases is the key to developing effective safety response measures. Traditional monitoring methods can usually only provide limited information support and it is difficult to fully cover the spread paths and extents of pests and diseases in multiple directions. Based on this, the present invention provides a specific embodiment, in step 303, based on the multi-directional propagation extension range, a safe planting request set is obtained, which specifically includes the following steps:

[0080] Step 311: Perform regional detection on the multi-directional propagation extension interval and the high-risk mulberry tree area, and output the maximum coverage of pests and diseases and regional infection rate of sensitive fields in all directions downward;

[0081] In this step, the maximum coverage of pests and diseases and the regional infection rate indicate the area with the highest pest and disease coverage and its coverage ratio in a specific direction.

[0082] In actual operation, the ecological exposure risk weighted superposition algorithm is first used to conduct regional detection of multi-directional propagation extension intervals and high-risk mulberry tree areas.

[0083] Step 312: Calculate the geological period update coefficient by the similarity between the current flow monitoring information and the past average information, match the maximum coverage of the pests and diseases with the standard maximum coverage of the corresponding pests and diseases in the surface soil quality standard according to the geological period update coefficient, and output the pests and diseases matching record;

[0084] In this step, the current flow monitoring information refers to the soil hardness, flow and other information currently collected by the sensor, which is used to monitor geological changes. The past average information refers to the average flow information in the same time period in the past, which is used as a benchmark reference. The pest matching record refers to the record obtained by matching the maximum coverage of pests and diseases with the standard maximum coverage, which is used to determine whether safety measures need to be taken.

[0085] In this step, the system calculates the geological period update coefficient by the similarity between the current flow monitoring information and the past mean information. For example, assuming that the current flow monitoring information shows that the current soil hardness is 20% faster than in the past, the geological period update coefficient is calculated to be 1.2. Then, based on the update coefficient, the system matches the maximum coverage of pests and diseases with the standard maximum coverage of the corresponding pests and diseases in the surface soil quality standard to obtain the pest and disease matching record. Assuming that the corrected maximum coverage of pests and diseases is higher than the standard maximum coverage, it indicates that the current soil hazard has exceeded the standard and safety measures need to be taken.

[0086] Step 313: When the pest matching record meets the preset multi-source environment, the pest source positioning model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain the pest source safety zone electronic fence, and a safe planting request set is obtained based on the pest source safety zone electronic fence. The preset multi-source environment includes a first environment in which the proportion of the overlap between the high-risk area and the planting soil channel source safety zone is greater than a threshold value and a second environment in which the pest coverage fluctuation index in the current monitoring information flow is greater than a fluctuation threshold value, and the threshold value is set according to the pest severity and the soil channel source soil coverage status;

[0087] In this step, the electronic fence of the pest source safety zone refers to the area where the possible pest source is located determined by model inversion, which is used to locate the potential pest source. The high-risk area refers to the area where the maximum coverage of pests exceeds the threshold value.

[0088] The embodiments of the present invention improve the ability to identify ecological risks by accurately evaluating the impact of pest and disease transmission in different directions on sensitive fields; calculate the geological period update coefficient by combining the similarity between current flow monitoring information and past mean information, match the maximum coverage of pests and diseases with the standard maximum coverage, and ensure the accuracy and timeliness of soil identification; when the pest and disease matching records meet the preset multi-source environment, the system can quickly trigger the pest and disease source positioning model, obtain the electronic fence of the pest and disease source safety zone, and formulate an effective safe planting request set.

[0089] In the process of safety response to soil pest incidents, quickly and accurately locating the pest source and obtaining an effective safe planting request set is the key to ensuring the safety of soil resources. The present invention provides a specific embodiment, step 313, when the pest matching record meets the preset multi-source environment, the pest source positioning model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain the pest source safety interval electronic fence, and obtain the pest source safety interval electronic fence according to the pest source safety interval electronic fence, and obtain the safe planting request set, the preset multi-source environment includes the first environment where the proportion of the overlap between the high-risk area and the planting soil channel source safety zone is greater than the threshold value and the second environment where the pest coverage fluctuation index in the current monitoring information flow is greater than the fluctuation threshold value, the threshold value is set according to the severity of the pest and the soil channel source soil coverage state, and specifically includes the following steps:

[0090] Step 321: construct a threshold value association table using the pest severity classification strategy and the soil channel source soil coverage status classification standard, and search for the range proportion threshold value corresponding to the combination according to the threshold value association table to obtain a threshold value set, and calculate the area overlap ratio of the high-risk area and the planting soil channel source safety zone according to the threshold value set, wherein the combination includes the pest severity and the soil channel source soil coverage status;

[0091] In this step, the threshold value association table refers to a table constructed based on the severity of pests and diseases and the soil channel source soil coverage status, which is used to find the corresponding range proportion threshold value to obtain a threshold value set. The range proportion threshold value refers to the maximum allowable proportion of the overlap range between the high-risk area set by the severity of pests and diseases and the soil channel source soil coverage status and the planting soil channel source safety zone. The threshold value set refers to a set of range proportion threshold values ​​under different combinations of pest and disease severity and soil channel source soil coverage status.

[0092] The system uses the threshold value association table to find that the threshold value for the range corresponding to this combination is 20%. Next, the system calculates the overlap ratio between the high-risk area and the planting soil channel source safety zone. Assuming the calculated record is 25%, it is greater than the set threshold value of 20%.

[0093] Step 322: extracting the pest coverage time series according to the current monitoring information flow, and calculating the pest coverage fluctuation index according to the pest coverage time series;

[0094] Step 323: If the area overlap ratio is greater than the threshold value corresponding to the severity of the pests and diseases and the soil coverage state of the soil channel source, it is determined to meet the first environment; if the pest and disease coverage fluctuation index is greater than the fluctuation threshold value corresponding to the preset pest and disease type, it is determined to meet the second environment;

[0095] Based on the calculation record of step 322, the system determines whether it meets the preset multi-source environment. Assume that the area overlap ratio is 25%, which is greater than the threshold value of 20%, which meets the first environment; the pest coverage fluctuation index is 1.5, which is greater than the fluctuation threshold value of 1.2, which meets the second environment.

[0096] Step 324: When the area overlap ratio meets both the first environment and the second environment, the pest source location model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain an electronic fence of the pest source safety zone;

[0097] When both the first and second environments are met, the system triggers the pest source location model. For example, in the above example, the system integrates the pest transmission probability feature matrix with the current monitoring information flow to obtain the pest source safety zone electronic fence.

[0098] Step 325: obtaining a safe planting request set according to the pest source safety zone electronic fence;

[0099] Finally, the system formulates a specific set of safe planting requests based on the electronic fence of the safe zone of the pest and disease source.

[0100] The present invention provides a specific embodiment, step 325, obtaining a safe planting request set according to the pest source safety zone electronic fence, specifically comprising the following steps:

[0101] Step 331: obtaining a log of potential pest sources within the fence according to the degree coordinates of the pest source safety zone electronic fence, the threshold value set, and the pest source past information;

[0102] In this step, the degree coordinates of the electronic fence of the pest source safety zone refer to the geographical degree coordinates of the area where the possible pest source is located, which is determined by the pest source location model, and is used to locate the potential pest source. The past information of the pest source refers to the information recording the location, emission conditions and pest events of each pest source in the past, which is used to assist in detecting the possibility of the current pest source.

[0103] In actual operation, the degree coordinates of the electronic fence in the safe zone of the pest source, the threshold value set and the past information of the pest source are first used to obtain the log of potential pest sources within the fence.

[0104] Step 332: adjusting the existing request classification strategy according to the pest coverage fluctuation index and the soil channel source service population state parameter, outputting the adjusted classification strategy, and obtaining a graded planting request set using the adjusted classification strategy, wherein the graded planting request set includes a shutdown request, a production limit request, and an inspection request;

[0105] Step 333: Based on the potential pest source log within the fence and the graded planting request set, combined with the current monitoring information flow and past risk information, a multi-candidate optimization algorithm is used to match the pest source feature vector with the request degree to obtain a safe planting request set, wherein the pest source feature vector includes a disease index vector and a resistance evaluation vector;

[0106] In this step, the current monitoring information flow refers to the soil parameter information currently collected by the sensor, which is used to monitor soil quality changes. Past risk information refers to information that records past pest and disease events and their treatment records, which is used to assess the risk level of current pest and disease events. The pest and disease source feature vector refers to a vector that describes the characteristics of the pest and disease source, usually including a disease index vector and a resistance evaluation vector, which is used to distinguish different types of pest and disease sources.

[0107] In this step, the system combines the logs of potential pest and disease sources within the fence and the graded planting request set, uses the current monitoring information flow and past risk information, and adopts a multi-candidate optimization algorithm to match the pest and disease source feature vector with the request degree to obtain the final safe planting request set.

[0108] In the process of soil environment monitoring, accurately confirming the pest transmission trend and obtaining detailed identification records are the key to formulating effective response measures. Based on this, the present invention provides a specific embodiment, step 104, combining the safe planting request set and the environmental adjustment element, confirming the pest transmission trend, and obtaining the soil environment identification record based on the pest transmission trend, specifically including the following steps:

[0109] Step 401: matching the pest transmission safety degree and the transmission rate threshold value in the safe planting request set, outputting a matching record, adjusting the sampling frequency of the candidate identification sensor according to the matching record, and outputting the adjusted identification sensor;

[0110] In this step, the pest transmission safety level indicates the accuracy of the soil abnormal transmission model's evaluation of the pest transmission level, usually expressed as a probability or safety interval. The transmission rate threshold refers to the pre-set upper limit of the pest transmission rate, which is used to determine whether the monitoring strategy needs to be adjusted. The matching record refers to the record obtained by comparing the pest transmission safety level with the transmission rate threshold, which is used to determine whether to adjust the sampling frequency of the candidate identification sensor.

[0111] In actual operation, the system first collects the degree of safety of pests and diseases from the safe planting request set and compares it with the preset transmission rate threshold. For example, the sampling frequency originally once an hour is adjusted to once every half an hour to ensure the timely collection of the latest soil information.

[0112] Step 402: using the adjusted identification sensor to perform time-space alignment processing on the current monitoring information stream, and output multi-source monitoring data;

[0113] In this step, multi-source monitoring data refers to a multi-dimensional information structure formed by aligning information at different time points and regional locations to facilitate subsequent detection.

[0114] In this step, the candidate identification sensor starts to collect soil information through the adjusted sampling frequency and performs spatiotemporal alignment processing on it.

[0115] Step 403: performing planting quality verification processing on the multi-source monitoring data to obtain pest and disease transmission evaluation indicators;

[0116] In this step, the pest and disease transmission evaluation index refers to the information processed by the spatiotemporal convolutional neural network, which represents the propagation probability characteristics of pests and diseases at different time and regional locations.

[0117] Step 404: confirming the pest transmission trend according to the pest transmission evaluation index and the environmental regulation factor to obtain a soil identification record, wherein the environmental regulation factor includes a soil hardness regulation factor and a temperature stratification regulation factor;

[0118] The present invention provides a specific embodiment, step 102, according to the arrangement information and field density information, determine the candidate identification sensor, according to the candidate identification sensor, output the identification information set, refer to Figure 2 , specifically including the following steps:

[0119] Step 201: obtaining a preliminary identification path according to the arrangement information and field density information, performing adaptive topological adjustment on the multi-source data acquisition sensors in the preliminary identification path, and outputting candidate identification sensors;

[0120] In this step, adaptive topology adjustment refers to adjusting the location and layout of multi-source data acquisition sensors according to actual monitoring needs and field characteristics to optimize monitoring efficiency and coverage range.

[0121] In actual operation, a preliminary identification path is first obtained using the collected arrangement information and field density information.

[0122] Step 202: collecting nutrition information, fertility information and mulberry brown spot disease coverage monitoring information according to the candidate identification sensor;

[0123] In this step, the candidate identified sensors refer to the optimal multi-source data acquisition sensor layout after adaptive topology adjustment to ensure coverage of the entire monitoring area and improve information collection efficiency.

[0124] In this step, the sensor layout is identified through the obtained candidates, and each sensor starts to collect soil parameter information.

[0125] Step 203: compensating the non-uniform sampling points in the nutrition information to obtain a nutrition characteristic map, strengthening the regional stratification in the fertility information to obtain a fertility stratification field, and performing particle tracking modeling on the propagation path in the mulberry brown spot disease coverage monitoring information to obtain a mulberry brown spot disease symptom propagation path;

[0126] In this step, the nutritional information refers to the nutrient coverage information in the soil collected by the sensor, which is an important indicator of soil health. The fertility information refers to the soil turbidity information collected by the sensor, which reflects the content of mixed particles in the soil. The mulberry spot coverage monitoring information refers to the mulberry spot symptom coverage information in the soil collected by the sensor, which is used to evaluate the soil pest and disease situation.

[0127] For non-uniform sampling points in nutrition information, the system uses interpolation algorithms to compensate and obtain continuous nutrition characteristic maps. For example, by interpolating the unsampled area between sensor A and sensor B, a complete nutrition characteristic map is obtained. For fertility information, the system enhances its regional variation characteristics through regional stratification and obtains a detailed fertility stratification field. For example, by calculating the fertility difference between sensor B and surrounding sensors, a clear fertility stratification field is obtained. For mulberry brown spot coverage monitoring information, the system uses particle tracking modeling methods to simulate the propagation path of mulberry brown spot symptoms in the soil.

[0128] Step 204: fusing the nutrition characteristic map, the fertility stratification field and the mulberry brown spot disease symptom propagation path to obtain an identification information set;

[0129] In this step, the identification information set refers to a multi-dimensional soil parameter information set including a nutritional characteristic map, a fertility stratification field, and a mulberry brown spot disease symptom propagation path.

[0130] The system integrates the obtained nutritional characteristic maps, fertility stratification fields and mulberry brown spot disease symptom propagation paths to form a comprehensive identification information set. For example, the above-mentioned nutritional characteristic maps, fertility stratification fields and mulberry brown spot disease symptom propagation paths are integrated to obtain a comprehensive information set containing all soil parameters. This information set can be used to further detect soil conditions and provide basic information support for the subsequent construction of soil abnormality propagation models.

[0131] The embodiment of the present invention achieves efficient and comprehensive soil monitoring layout by accurately obtaining preliminary identification paths and adaptively adjusting the topology of multi-source data acquisition sensors; based on the obtained candidate identification sensors, the system can accurately collect multi-dimensional soil parameter information such as nutrition, fertility and mulberry brown spot disease coverage, and obtain high-quality nutrition characteristic maps, fertility stratification fields and mulberry brown spot disease symptom propagation paths through compensation processing, regional stratification and enhancement processing, and particle tracking modeling. The identification information set obtained through fusion processing not only improves the accuracy and coverage of soil monitoring, but also provides a solid information foundation for subsequent soil abnormal propagation evaluation and safety response. This significantly enhances the ability to protect soil resources and provides strong technical support for achieving all-round and multi-level soil management.

[0132] Figure 3 A schematic diagram of a mulberry planting evaluation system based on multi-source data is provided for an embodiment of the present invention. Figure 3 As shown, the system includes:

[0133] A collection unit 21 is used to collect arrangement information and field density information of multi-source data acquisition sensors in the area to be evaluated;

[0134] A determination unit 22, configured to determine a candidate identification sensor according to the arrangement information and the field density information, and output an identification information set according to the candidate identification sensor;

[0135] A construction unit 23 is used to process the symptom propagation path of mulberry brown spot disease in the identification information set to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and obtain a safe planting request set according to the multi-directional propagation extension interval;

[0136] The output unit 24 is used to combine the safe planting request set and the environmental regulation factor to confirm the pest and disease transmission trend, and obtain the soil environment identification record based on the pest and disease transmission trend.

Claims

1. A mulberry planting evaluation method based on multi-source data, characterized in that: include: Collect multi-source data in the area to be evaluated to obtain sensor arrangement information and field density information; According to the arrangement information and the field density information, a preliminary identification path is obtained, and adaptive topological adjustment is performed on the multi-source data acquisition sensors in the preliminary identification path to output candidate identification sensors; According to the candidate identification sensor, collecting nutrition information, fertility information and mulberry brown spot disease coverage monitoring information; Compensation processing is performed on the non-uniform sampling points in the nutrition information to obtain a nutrition characteristic map, regional stratification in the fertility information is enhanced to obtain a fertility stratification field, and particle tracking modeling processing is performed on the propagation path in the mulberry brown spot disease coverage monitoring information to obtain a mulberry brown spot disease symptom propagation path; The nutritional characteristic map, the fertility stratification field and the mulberry brown spot disease symptom propagation path are fused to obtain an identification information set; Based on the identification information set, a soil abnormality propagation model and a nutrient characteristic map are constructed, and the nutrient characteristic map and the soil abnormality propagation model are used to perform synergistic coupling processing on the propagation effect of the fertility stratification field to obtain a pest and disease propagation probability characteristic matrix; Based on the pest transmission probability characteristic matrix, a three-dimensional soil model is constructed to reversely process the pest source location and emission characteristics of the mulberry brown spot disease symptom transmission path to obtain a pest transmission degree evaluation map; According to the pest and disease transmission degree evaluation map, a directional detection method is used to simulate the preset pest and disease degree stratification, and a multi-directional transmission extension interval is output. According to the multi-directional transmission extension interval, a safe planting request set is obtained; The safe planting request set and environmental regulation factors are combined to confirm the pest and disease transmission trend, and the soil environment identification record is obtained based on the pest and disease transmission trend.

2. The method according to claim 1, characterized in that According to the multi-directional propagation extension interval, a safe planting request set is obtained, including: Conduct regional detection on the multi-directional propagation extension interval and high-risk mulberry tree area, and output the maximum coverage of pests and diseases and regional infection rate of sensitive fields in all directions downward; The geological period update coefficient is calculated by the similarity between the current flow monitoring information and the past average information, and the maximum coverage of the pests and diseases is matched with the standard maximum coverage of the corresponding pests and diseases in the surface soil quality standard according to the geological period update coefficient, and the pests and diseases matching record is output; When the pest and disease matching record meets the preset multi-source environment, the pest and disease source positioning model is triggered to fuse the pest and disease transmission probability feature matrix with the current monitoring information flow to obtain the pest and disease source safety interval electronic fence, and a safe planting request set is obtained based on the pest and disease source safety interval electronic fence. The preset multi-source environment includes a first environment in which the overlap ratio between the high-risk area and the planting soil channel source safety zone is greater than a threshold value and a second environment in which the pest and disease coverage fluctuation index in the current monitoring information flow is greater than a fluctuation threshold value, and the threshold value is set according to the severity of the pest and disease and the soil coverage status of the soil channel source.

3. The method according to claim 2, characterized in that When the pest matching record meets the preset multi-source environment, the pest source positioning model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain the pest source safety zone electronic fence, and a safe planting request set is obtained based on the pest source safety zone electronic fence. The preset multi-source environment includes a first environment in which the proportion of the overlap between the high-risk area and the planting soil channel source safety zone is greater than a threshold value and a second environment in which the pest coverage fluctuation index in the current monitoring information flow is greater than a fluctuation threshold value. The threshold value is set according to the severity of the pest and the soil coverage status of the soil channel source, including: Using the pest severity classification strategy and the soil channel source soil coverage status classification standard, a threshold value association table is constructed, and according to the threshold value association table, the range proportion threshold value corresponding to the combination is searched to obtain a threshold value set, and according to the threshold value set, the area overlap ratio of the high-risk area and the planting soil channel source safety area is calculated, and the combination includes the pest severity and the soil channel source soil coverage status; Extracting pest and disease coverage time series based on the current monitoring information flow, and calculating pest and disease coverage fluctuation index based on the pest and disease coverage time series; If the proportion of the overlapping range of the regions is greater than the threshold value of the corresponding severity of the pests and diseases and the soil coverage status of the soil channel source, it is determined to meet the first environment; if the pest and disease coverage fluctuation index is greater than the fluctuation threshold value corresponding to the preset pest and disease type, it is determined to meet the second environment; When the area overlap ratio meets both the first environment and the second environment, the pest source location model is triggered to fuse the pest transmission probability feature matrix with the current monitoring information flow to obtain an electronic fence of the pest source safety zone; A safe planting request set is obtained based on the electronic fence of the safe zone of the pest source.

4. The method according to claim 3, characterized in that According to the electronic fence of the pest source safety zone, a safe planting request set is obtained, including: Obtaining a log of potential pest sources within the fence according to the degree coordinates of the pest source safety zone electronic fence, the threshold value set, and past information of the pest source; According to the pest coverage fluctuation index and the soil channel source service population state parameter, the existing request classification strategy is adjusted, and the adjusted classification strategy is output. The graded planting request set is obtained by using the adjusted classification strategy, and the graded planting request set includes a shutdown request, a production limit request, and an inspection request; Based on the log of potential pest and disease sources within the wall and the graded planting request set, combined with the current monitoring information flow and past risk information, a multi-candidate optimization algorithm is used to match the pest and disease source feature vector with the request degree to obtain a safe planting request set. The pest and disease source feature vector includes a disease index vector and a resistance evaluation vector.

5. The method according to claim 1, characterized in that Combining the safe planting request set and the environmental regulation elements, confirm the pest and disease transmission trend, and obtain the soil environment identification record based on the pest and disease transmission trend, including: Matching the pest and disease transmission safety degree and the transmission rate threshold value in the safe planting request set, outputting a matching record, adjusting the sampling frequency of the candidate identification sensor according to the matching record, and outputting the adjusted identification sensor; Using the adjusted identification sensor to perform time-space alignment processing on the current monitoring information flow, and output multi-source monitoring data; Performing planting quality verification processing on the multi-source monitoring data to obtain pest and disease transmission evaluation indicators; The pest and disease transmission trend is confirmed based on the pest and disease transmission evaluation index and the environmental regulation factors to obtain soil identification records. The environmental regulation factors include soil hardness regulation factors and temperature stratification regulation factors.

6. A mulberry planting evaluation system based on multi-source data, characterized in that: include: A collection unit, used for collecting arrangement information of sensors and field density information of multi-source data acquisition in the area to be evaluated; a determination unit, configured to determine a candidate identification sensor according to the arrangement information and the field density information, and output an identification information set according to the candidate identification sensor; A construction unit is used to process the symptom propagation path of mulberry brown spot disease in the identification information set to obtain a pest and disease propagation degree evaluation map, so as to simulate and obtain a multi-directional propagation extension interval, and obtain a safe planting request set based on the multi-directional propagation extension interval; An output unit, used to combine the safe planting request set and the environmental regulation factor to confirm the pest and disease transmission trend, and obtain a soil environment identification record based on the pest and disease transmission trend; The mulberry planting evaluation system based on multi-source data is used to execute the mulberry planting evaluation method based on multi-source data as described in any one of claims 1-5.

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