Agricultural product safety early warning method based on artificial intelligence

Abstract

The invention relates to the technical field of agricultural analysis, in particular to an agricultural product safety early warning method based on artificial intelligence, and the method comprises the steps: obtaining a free-ranging range and a historical death place of a target organism, and determining a corresponding target reference range and a target monitoring range according to a target position, determining a position characterization state and a soil characterization state of each target position so as to determine a monitoring type of the target position, determining the number of monitoring points corresponding to a target monitoring range according to the monitoring type, and determining a plurality of common monitoring ranges, and according to the soil acidity and alkalinity and the soil heavy metal content of each monitoring range, determining whether to send out a safety early warning signal, and according to the number of times of sending out the safety early warning signal of each monitoring range, determining a monitoring adjustment strategy of the corresponding monitoring range. According to the invention, the efficiency and accuracy of agricultural product safety management are improved, potential safety risks can be timely found and dealt with, and the quality of agricultural products is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of agricultural analysis technology, and in particular to an agricultural product safety early warning method based on artificial intelligence. Background Art

[0002] The rapid development of artificial intelligence technology has been widely used in the monitoring of agricultural product quality and safety. By collecting data on the breeding, production, processing, storage and transportation of agricultural products in real time, and using algorithms and models to conduct intelligent analysis of the data, rapid and accurate early warning of agricultural product quality and safety can be achieved, thereby improving the efficiency of agricultural product quality and safety supervision, reducing quality and safety risks, promoting agricultural industry upgrading and enhancing the market competitiveness of agricultural products.

[0003] Chinese patent announcement number CN112883137B discloses a method for early warning of agricultural product production safety, which organically combines historical monitoring information with encrypted point monitoring information, considers the model algorithm results and the spatial characteristics of the area to be warned, obtains the minimum number of encrypted points in each area while ensuring the representativeness of the points, and achieves rapid, dynamic, and multi-index soil-agricultural product early warning, which provides data support and effective ideas for the later regional production environment early warning work. Based on the monitoring information, the area to be warned is divided into the standard-reaching area and the over-standard area, and targeted prevention and control technical measures are selected according to regional characteristics. The soil ecological threshold and heavy metal accumulation rate are comprehensively analyzed, and preventive measures are proposed based on the analysis results. Compared with traditional early warning methods, the prevention time is advanced, the warning period is extended, the prevention measures are more effective, and the monitoring work is more targeted. It can be seen that the invention has the following problems:

[0004] The invention does not consider the problem of being unable to balance monitoring efficiency and monitoring cost by dividing the monitoring range into key monitoring range, secondary key monitoring range and general range according to the historical death information of the target organisms and setting different numbers of monitoring points accordingly. Summary of the invention

[0005] To this end, the present invention provides an agricultural product safety early warning method based on artificial intelligence to overcome the problem in the prior art that the key monitoring range, secondary key monitoring range and general range are divided according to the historical death information of the target organism, and different numbers of monitoring points are set accordingly, resulting in an inability to balance the monitoring efficiency and monitoring cost.

[0006] To achieve the above purpose, the present invention provides an agricultural product safety early warning method based on artificial intelligence, comprising:

[0007] Obtaining the free-range range and historical death sites of the target organisms, and identifying each of the historical death sites as a target location;

[0008] Determine the corresponding target reference range and target monitoring range according to the target position;

[0009] Determining a position representation state thereof according to the number of target positions within the target reference range;

[0010] Determine the soil characterization status of the target reference range according to whether it meets the soil monitoring conditions;

[0011] Determining a monitoring type of the target location according to the location characterization state and the soil characterization state, wherein the monitoring type includes a key monitoring type and a secondary key monitoring type;

[0012] Determine the number of monitoring points corresponding to the target monitoring range according to the monitoring type of the target location;

[0013] Determine a common monitoring area according to the free-range range and each of the target monitoring ranges to determine a number of common monitoring ranges;

[0014] Determine whether to issue a safety warning signal based on the soil pH and soil heavy metal content in each monitoring range;

[0015] The monitoring adjustment strategy for the corresponding monitoring range is determined based on the number of safety warning signals issued by each monitoring range in the previous monitoring cycle, including:

[0016] Increasing monitoring frequency, decreasing monitoring frequency, and not developing a monitoring adjustment strategy.

[0017] Furthermore, the method for determining the target reference range and the target monitoring range corresponding to the target position includes:

[0018] Determine the base length and reference length;

[0019] Determine the target reference range according to the target position and the reference length;

[0020] Determine the target monitoring range according to the target position and the reference length;

[0021] The target reference range is a circle with the target position as the center and the reference length as the radius, and the target monitoring range is a circle with the target position as the center and the reference length as the radius.

[0022] Further, determining the position representation state thereof according to the number of target positions within the target reference range includes:

[0023] If the number of target positions within the target reference range is greater than or equal to a preset number, then determining that the position representation state is a densely monitored position;

[0024] If the number of target positions within the target reference range is less than a preset number, then the position representation state is determined to be an independent monitoring position.

[0025] Further, the method for determining the soil characterization state according to whether the target reference range meets the soil monitoring condition includes:

[0026] Obtain soil type and soil pH for each target benchmark range;

[0027] The soil characterization state is determined according to whether the soil type and the soil pH in each target reference range meet the soil monitoring conditions, wherein:

[0028] If the soil type and soil pH within a single target reference range meet the soil monitoring conditions, then the soil characterization state is determined to be a poor characterization state;

[0029] If the soil type and the soil pH within the single target reference range do not meet the soil monitoring conditions, then the soil characterization state is determined to be a benign characterization state;

[0030] The soil monitoring conditions are that the soil texture is clay and / or the soil pH exceeds a preset range of pH.

[0031] Further, determining the monitoring type of the target location according to the location characterization state and the soil characterization state includes:

[0032] If the location characterization state is an intensive monitoring location and the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a key monitoring type;

[0033] If the location characterization state is an intensive monitoring location or the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a secondary key monitoring type.

[0034] Further, the number of monitoring points corresponding to the target monitoring range is determined according to the monitoring type of the target location, including:

[0035] If the monitoring type is a key monitoring type, the number of monitoring points corresponding to the target monitoring range is the first reference number;

[0036] If the monitoring type is a secondary key monitoring type, the number of monitoring points corresponding to the target monitoring range is the second reference number;

[0037] The first reference quantity is greater than the second reference quantity.

[0038] Further, the method of determining a plurality of common monitoring ranges by determining a common monitoring area according to the free-range range and each of the target monitoring ranges includes:

[0039] Determine the range outside each target monitoring range in the free-range range as a common monitoring area;

[0040] Determining the base number of the ordinary monitoring range according to the ratio of the area of ​​the ordinary monitoring area to the preset area;

[0041] Dividing the common monitoring area into a benchmark number of common monitoring ranges;

[0042] Among them, the areas of each of the ordinary monitoring ranges are equal.

[0043] Furthermore, whether to issue a safety warning signal is determined based on the soil pH and soil heavy metal content in each monitoring range, including:

[0044] If the soil pH exceeds the preset pH range and / or the soil heavy metal content exceeds the preset pollution range, a safety warning signal is issued;

[0045] If the soil pH does not exceed the preset pH range and the soil heavy metal content does not exceed the preset pollution range, it is determined that no safety warning signal will be issued.

[0046] Furthermore, the method of determining the monitoring adjustment strategy of the corresponding monitoring range according to the number of times each monitoring range sends out a safety warning signal in the previous monitoring cycle includes:

[0047] If no safety warning signal is issued in the previous monitoring cycle, the monitoring adjustment strategy for the corresponding monitoring range is to reduce the monitoring frequency to the minimum monitoring frequency;

[0048] If the number of safety warning signals issued in the previous monitoring cycle is less than the preset number, it is determined that no monitoring adjustment strategy for the corresponding monitoring range will be formulated;

[0049] If the number of safety warning signals issued in the previous monitoring cycle is greater than or equal to the preset number, the monitoring adjustment strategy for the corresponding monitoring range is determined to be to increase the monitoring frequency.

[0050] Furthermore, when determining to increase the monitoring frequency, the monitoring frequency is positively correlated with the number of times the safety warning signal was issued in the previous monitoring cycle.

[0051] Compared with the prior art, the beneficial effect of the present invention is that the agricultural product safety early warning method based on artificial intelligence provided by the present invention not only improves the efficiency and accuracy of agricultural product safety management, but also helps to timely discover and respond to potential safety risks, thereby ensuring the quality of agricultural products;

[0052] Furthermore, by counting the number of other target locations within the target benchmark range and dividing the target locations into intensive monitoring locations and independent monitoring locations based on this, this method can guide the allocation of monitoring resources more scientifically and accurately to improve monitoring efficiency; when there are other target locations greater than or equal to a preset number (such as 3) within the target benchmark range, the location is determined to be an intensive monitoring location, which means that the area may have a high biological activity intensity or soil environmental risk, so it is necessary to increase monitoring efforts to ensure the accuracy and reliability of the monitoring results; and when there are other target locations less than the preset number within the target benchmark range, the location is determined to be an independent monitoring location, at which time the monitoring frequency and resource input can be appropriately reduced to avoid unnecessary waste; this differentiated monitoring strategy not only helps to optimize resource allocation, but also improves the pertinence and efficiency of monitoring work, and provides strong support for the scientific formulation of agricultural production plans;

[0053] Furthermore, by comprehensively considering the two factors of soil type and soil pH and comparing them with the preset soil monitoring conditions, the impact of soil environment on the growth of target organisms and the safety of agricultural products can be scientifically and accurately evaluated; when the soil type in the target baseline range is clay or the soil pH exceeds the appropriate range, the soil characterization state is judged to be a poor characterization state, which suggests that the soil environment in this area may have factors that are not conducive to the growth of target organisms and the safety of agricultural products; and when the soil type and soil pH both meet the soil monitoring conditions, the soil characterization state is judged to be a benign characterization state, which indicates that the soil environment in this area is relatively good, which is conducive to the growth of target organisms and the safety of agricultural products; this assessment method based on soil characterization state helps to timely discover and solve soil environmental problems;

[0054] Furthermore, by comprehensively considering the location characterization status and soil characterization status, the monitoring priorities of different target locations can be accurately divided to achieve optimal allocation of monitoring resources; for target locations whose location characterization status is an intensive monitoring location and whose soil characterization status is a poor characterization status, they are determined to be key monitoring types, which can ensure that these key areas receive sufficient attention and resource investment, and timely discover and solve potential problems that may have a significant impact on the life and health of target organisms; and for target locations that only meet one of the conditions (intensive monitoring location or poor characterization status), they are determined to be secondary key monitoring types, which can not only maintain attention on these areas, but also reasonably control monitoring costs; at the same time, for target locations whose location characterization status is an independent monitoring location and whose soil characterization status is a benign characterization status, they are regarded as ordinary monitoring areas to avoid unnecessary waste of monitoring resources; this monitoring type classification method based on comprehensive evaluation improves the pertinence and efficiency of monitoring work;

[0055] Furthermore, dividing the general monitoring area into multiple general monitoring ranges of equal area ensures the comprehensiveness and balance of the monitoring work; setting up a backup sensor group in each general monitoring range not only improves the reliability and continuity of the monitoring data, but also enhances the stability and fault response capability of the system; this method not only optimizes the utilization of monitoring resources, but also improves the monitoring efficiency and quality, providing strong support for the comprehensive and accurate monitoring of the free-range environment;

[0056] Furthermore, by reasonably setting the number of monitoring points corresponding to key monitoring types and secondary key monitoring types, the comprehensiveness and accuracy of monitoring data are guaranteed, and the monitoring cost is effectively controlled; this differentiated monitoring point setting strategy not only improves the pertinence and efficiency of monitoring work, but also achieves the optimal allocation of monitoring resources. At the same time, the uniform distribution of monitoring points within the target monitoring range ensures the representativeness and reliability of monitoring data;

[0057] Furthermore, through the combined application of the fully automatic soil pH meter and the online soil heavy metal monitor, long-term monitoring of soil acidity and heavy metal content can be achieved; at the same time, based on the risk screening value in the "Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard (Trial)", and setting a more stringent warning threshold (i.e. 0.9 times the risk screening value), the potential risks of soil environmental quality can be more keenly captured and safety warning signals can be issued in a timely manner;

[0058] Furthermore, the rational allocation and efficient utilization of monitoring resources are achieved through the dynamic adjustment of the monitoring frequency; for the monitoring range that did not issue a safety warning signal in the previous monitoring cycle, the monitoring frequency is appropriately reduced to the minimum monitoring frequency, which not only reduces the burden of monitoring work, but also avoids unnecessary waste of resources; and for the monitoring range that issues a safety warning signal less frequently, the original monitoring frequency is maintained to ensure the continuity and stability of the monitoring work; for the monitoring range that issues a safety warning signal more frequently (indicating a higher risk), the monitoring frequency is increased in a timely manner, so as to more accurately grasp the changes in soil environmental quality, take timely response measures, and effectively ensure the safety of target organisms and the stability of the ecological environment; in addition, this method further improves the pertinence and flexibility of monitoring work by setting reasonable preset times and monitoring frequency adjustment rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a step diagram of an agricultural product safety early warning method based on artificial intelligence according to an embodiment of the present invention;

[0060] Figure 2 A method step diagram for determining a target reference range and a target monitoring range according to an embodiment of the present invention;

[0061] Figure 3A flow chart of a method for determining a monitoring type of a target location according to an embodiment of the present invention;

[0062] Figure 4 A diagram showing the steps of a method for determining a normal monitoring range according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0064] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0065] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0066] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0067] See also Figure 1 As shown, it is a step diagram of an agricultural product safety early warning method based on artificial intelligence according to an embodiment of the present invention. An agricultural product safety early warning method based on artificial intelligence is provided in an embodiment of the present invention, comprising:

[0068] Step S1, obtaining the free-range range and historical death locations of the target organism, and determining each of the historical death locations as the target location;

[0069] Step S2, determining the target reference range and target monitoring range corresponding to the target position according to the target position;

[0070] Step S3, determining the position representation state thereof according to the number of target positions within the target reference range;

[0071] Step S4, determining the soil characterization state according to whether the target reference range meets the soil monitoring conditions; in implementation, step S3 and step S4 can be switched in order or determined simultaneously;

[0072] Step S5, determining the monitoring type of the target location according to the location characterization state and the soil characterization state, wherein the monitoring type includes a key monitoring type and a secondary key monitoring type;

[0073] Step S6, determining the number of monitoring points corresponding to the target monitoring range according to the monitoring type of the target location;

[0074] Step S7, determining a common monitoring area according to the free-range range and each of the target monitoring ranges to determine a plurality of common monitoring ranges; it can be understood that a single common monitoring range has only one monitoring point, and the monitoring point is located at the geometric center of the common monitoring range;

[0075] In implementation, step S6 and step S7 can be switched in order or can be determined simultaneously;

[0076] Step S8, determining whether to issue a safety warning signal according to the soil pH and soil heavy metal content in each monitoring range;

[0077] Step S9, determining the monitoring adjustment strategy of the corresponding monitoring range according to the number of times each monitoring range sends out a safety warning signal in the previous monitoring cycle, including:

[0078] Increasing monitoring frequency, decreasing monitoring frequency, and not developing a monitoring adjustment strategy.

[0079] It can be understood that the artificial intelligence-based agricultural product safety early warning method provided by the present invention not only improves the efficiency and accuracy of agricultural product safety management, but also helps to timely discover and respond to potential safety risks, thereby ensuring the quality of agricultural products: (1) By obtaining the free-range range and historical death locations of the target organisms, it is possible to accurately identify potential risk areas in agricultural product production, providing a scientific basis for subsequent key monitoring; (2) By combining the location characterization status and soil characterization status to determine the monitoring type of the target location, it not only considers the geographical location distribution characteristics of the potential risk area but also incorporates the key factor of soil quality, making risk assessment more comprehensive and accurate; (3) According to the monitoring type, the monitoring is reasonably allocated. The number of measuring points ensures the efficient use of monitoring resources. At the same time, by setting up general monitoring areas, comprehensive coverage of the entire free-range range is achieved, further improving the efficiency and effectiveness of monitoring; (4) Regular monitoring based on soil pH and soil heavy metal content can timely discover and warn of safety hazards in agricultural product production. At the same time, the monitoring frequency can be flexibly adjusted according to the number of warning signals issued in the previous monitoring cycle, which not only ensures the timeliness of monitoring but also avoids unnecessary waste of resources; (4) The safety warning method provided by the present invention is based on artificial intelligence technology, which can automatically process and analyze large amounts of data, quickly generate warning signals and monitoring adjustment strategies, and provide intelligent decision-making support for agricultural product safety management.

[0080] See also Figure 2 As shown, it is a step diagram of a method for determining a target reference range and a target monitoring range according to an embodiment of the present invention. Specifically, in step S2, the method for determining the target reference range and the target monitoring range corresponding to the target position includes:

[0081] Step S21, determining the benchmark length and the reference length;

[0082] Step S22, determining the target reference range according to the target position and the reference length;

[0083] Step S23, determining the target monitoring range according to the target position and the reference length;

[0084] The target reference range is a circle with the target position as the center and the reference length as the radius, and the target monitoring range is a circle with the target position as the center and the reference length as the radius.

[0085] It can be understood that the target reference range is used to measure the density of a certain target location, while the target monitoring range is used to monitor and analyze the soil data of each target location within the range of free-range livestock; usually, the target organisms (cattle and sheep) will cause changes in the pH value and pollutant content of the soil due to their frequent trampling and accumulation of excrement during the free-range process, thereby inhibiting the growth of vegetation at that location and causing the vegetation to contain excessive heavy metals. At the same time, the target monitoring location is used to monitor the safety of the target organism's food source (grass). Taking a smaller range can also determine the size of the target organism's activity range based on changes in the pH value and heavy metal content of the soil (if the target organism is confined to a certain range, this range will change the pH value and heavy metal content of the soil at that location due to frequent trampling and accumulation of excrement). Therefore, the target monitoring range of each target location is < the target reference range of the target location, that is, the reference length is < the reference length.

[0086] In practice, if the benchmark length is set too large, a large number of irrelevant areas will be included, which will lead to data dilution and mask the actual local density. If the benchmark length is set too small, it will be easily affected by local abnormal conditions, making the density assessment unstable. Therefore, the benchmark length ∈ [5m, 15m] is preferably set to 10m, that is, the corresponding target benchmark range is approximately 314 square meters.

[0087] In implementation, the reference length ∈ [5m, 10m] is preferably set to 5m; setting the target monitoring range to a smaller range can better focus on monitoring the range to fully understand the safety of the target organisms and their food within this range.

[0088] It is understandable that within the target baseline range, the density of target locations can be preliminarily assessed to provide a basis for subsequent detailed monitoring; while the target monitoring range conducts in-depth soil data monitoring and analysis; the relatively small target monitoring range can concentrate resources to focus on monitoring specific areas, which not only improves monitoring efficiency, but also more accurately reflects the impact of target biological activities on the soil environment and the safety of food sources.

[0089] See also Figure 3 As shown, it is a flow chart of a method for determining the monitoring type of a target position according to an embodiment of the present invention. Specifically, in step S3, the position characterization state is determined according to the number of target positions within the target reference range, including:

[0090] If the number of other target positions within the target reference range is greater than or equal to a preset number, then the position representation state is determined to be a densely monitored position;

[0091] If the number of other target positions within the target reference range is less than a preset number, then the position representation state is determined to be an independent monitoring position.

[0092] It can be understood that the total number of target positions, 2 ≤ preset number ≤ 0.5 times, is preferably set to 3; that is, if there are greater than or equal to 3 other target positions within the target reference range corresponding to each target position, that is, one target organism dies every 100 square meters, it means that the number of target organisms that die within this target reference range is large, and this position is a intensive monitoring position; that is, if there are less than 3 other target positions within the target reference range corresponding to each target position, it means that the number of target organisms that die within this target reference range is not large, and this position is an independent monitoring position.

[0093] Specifically, in step S4, the method for determining the soil characterization state according to whether the target reference range meets the soil monitoring condition includes:

[0094] Step S41, obtaining the soil type and soil pH of each target reference range;

[0095] Step S42, determining the soil characterization state according to whether the soil type and the soil pH in each target reference range meet the soil monitoring conditions, wherein:

[0096] If the soil type and soil pH within a single target reference range meet the soil monitoring conditions, then the soil characterization state is determined to be a poor characterization state;

[0097] If the soil type and the soil pH within the single target reference range do not meet the soil monitoring conditions, then the soil characterization state is determined to be a benign characterization state;

[0098] The soil monitoring conditions are that the soil texture is clay and / or the soil pH exceeds a preset range of pH.

[0099] It can be understood that step S4 is to determine the soil characterization state of each target reference position to determine whether the death of the target organisms in this range is related to the soil environment.

[0100] It can be understood that soil types include loam, clay and sandy soil, among which: (1) Loam is an ideal soil texture for growing grass. It has a moderate particle composition and an appropriate ratio of sand, silt and clay. This texture makes loam have good air permeability and water permeability, which can ensure that there is enough oxygen in the soil for plant roots to breathe; at the same time, because it has sand, silt and clay and only the clay part can absorb heavy metals, and although loam has a small amount of small pores that can absorb heavy metals, there are also some larger pores to ensure a certain air permeability and water permeability, so its ability to absorb heavy metals is not very strong; (2) Although sandy soil has certain limitations in growing grass, it can also be used for growing grass through reasonable management. Sandy soil has very good air permeability and water permeability, making it difficult for plant roots to suffer from hypoxia. Sandy soil can be used to grow some drought-resistant and barren-resistant plants. Thin forage varieties (such as Astragalus membranaceus); at the same time, due to the coarse particles, small specific surface area and few adsorption sites of sandy soil, the adsorption of heavy metals is mainly physical adsorption, so the adsorption capacity of heavy metals is small, but its air permeability is good and the pores are large. Heavy metals are more likely to leach and migrate in sandy soil, so that they can penetrate into deep soil or groundwater more quickly. Therefore, the adsorption capacity of sandy soil for heavy metals is relatively weak; (3) Clay has a large specific surface area, fine particles and more negative charges, which makes clay have a strong adsorption capacity for cationic heavy metals. For example, when heavy metal ions such as lead, cadmium and mercury exist in the soil solution in the form of cations, clay particles can adsorb these ions on their surface through electrostatic adsorption and other methods; at the same time, clay has small pores, and heavy metals are difficult to leach and migrate after entering, and are easy to accumulate in the soil. Therefore, soil texture (i.e. soil type) is used as a factor to measure the soil characterization state.

[0101] It is understandable that when the pH value of the soil exceeds the range suitable for crop growth (the suitable pH value for different crops varies, but is generally between 5.5 and 7.5), it means that the soil here is not suitable for vegetation growth and will affect the foraging of target organisms; usually, when the pH value of the soil exceeds 5.5-7.5, it is not conducive to plant growth, and when the pH value is lower than 4.5 or higher than 8.5, it means that the pH value of the soil deviates seriously from the normal range, which will greatly affect the effectiveness of nutrients and microbial activity in the soil, thereby affecting the quality of vegetation and further affecting the health of agricultural products (target organisms); usually the preset range of pH is set to [5.5,7.5].

[0102] Specifically, in step S5, the monitoring type of the target location is determined according to the location characterization state and the soil characterization state of the same target location, including:

[0103] If the location characterization state is an intensive monitoring location and the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a key monitoring type;

[0104] If the location characterization state is an intensive monitoring location or the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a secondary key monitoring type.

[0105] It can be understood that the location characterization status indicates how many deaths of target organisms have occurred near the location before and whether they are accidental, and the soil characterization status indicates the influence of the soil quality here on the preparation and indirectly affects the health of the target organisms; therefore, for a target location that meets both the intensive monitoring location and the poor characterization status, it means that the location will have a great impact on the life and health of the target organism, so it needs to be monitored intensively; and for a target location that meets one of the conditions (intensive monitoring location / poor characterization status), it is necessary to strengthen monitoring but not focus on monitoring, so the location is set as a secondary focus monitoring type;

[0106] In implementation, if the location characterization status is an independent monitoring location and the soil characterization status is a benign characterization status, it means that the death of the target organism at this location may have been accidental and fortuitous. Therefore, it is determined that the monitoring type of the target location is neither a key monitoring type nor a secondary key monitoring type, and it belongs to the ordinary monitoring area.

[0107] See also Figure 4 As shown, it is a step diagram of the method for determining the general monitoring range according to an embodiment of the present invention. Specifically, in step S7, the method for determining the general monitoring area according to the free-range range and each of the target monitoring ranges to determine a plurality of general monitoring ranges includes:

[0108] Step S71, determining the range outside each target monitoring range in the free-range range as a common monitoring area; it can be understood that the common monitoring area and each target monitoring range constitute a complete free-range range;

[0109] Step S72, determining the reference number of the common monitoring range according to the ratio of the area of ​​the common monitoring area to the preset area; in implementation, the preset area is usually ≥ the target monitoring area, preferably equal to the target monitoring area; in implementation, the reference number = the area of ​​the common monitoring area ÷ the preset area, and the reference number is calculated by rounding up;

[0110] Step S73, dividing the common monitoring area into a reference number of common monitoring ranges; it is understandable that a monitoring point is set in each common monitoring range, but two sensor groups need to be installed at one monitoring point; in implementation, only one of the sensor groups is turned on and used, and when a problem occurs in one of the sensor groups, the other sensor group is automatically turned on;

[0111] Among them, the areas of each of the ordinary monitoring ranges are equal.

[0112] Specifically, in step S6, the number of monitoring points corresponding to the target monitoring range is determined according to the monitoring type of the target location, including:

[0113] If the monitoring type is a key monitoring type, the number of monitoring points corresponding to the target monitoring range is the first reference number;

[0114] If the monitoring type is a secondary key monitoring type, the number of monitoring points corresponding to the target monitoring range is the second reference number;

[0115] The first reference quantity is greater than the second reference quantity.

[0116] It can be understood that 2≤second reference number<first reference number≤5. For key monitoring types, a more precise understanding of the situation within the target monitoring range is required. Due to the uneven soil properties and heavy metal distribution of the contaminated site, 3 to 5 monitoring points need to be set up. Secondary key monitoring types usually mean that the pollution risk or degree of change is relatively low. In the case of limited resources, the secondary key monitoring area does not need to have a high monitoring density as the key monitoring area, so 2 to 3 monitoring points are set.

[0117] It is understandable that if more than 5 monitoring points are set in the target monitoring range corresponding to a single key monitoring type, too many sensors will cause the cost to rise sharply, but if too few sensors are set, the meaning of key monitoring will be lost; therefore, in implementation, preferably, the first reference number = 3, and the second reference number = 2.

[0118] It can be understood that the monitoring positions within a single target monitoring range are evenly distributed within the monitoring range.

[0119] Specifically, in step S8, whether to issue a safety warning signal is determined according to the soil pH and soil heavy metal content in each monitoring range, including:

[0120] If the soil pH exceeds the preset pH range and / or the soil heavy metal content exceeds the preset pollution range, a safety warning signal is issued;

[0121] If the soil pH does not exceed the preset pH range and the soil heavy metal content does not exceed the preset pollution range, it is determined that no safety warning signal will be issued.

[0122] It can be understood that a single monitoring point is equipped with a sensor group, including a fully automatic soil pH meter and an online soil heavy metal monitor; among them, the fully automatic soil pH meter can automatically complete the steps of calibration, temperature compensation, liquid addition, stirring, pH monitoring and reporting; in reality, the soil heavy metal monitor collects the heavy metal information in the soil in real time by installing fixed sensors or probes in the soil, and can realize long-term, continuous and real-time monitoring, and can transmit data to the remote monitoring platform, so that users can check the changes in the heavy metal content in the soil at any time.

[0123] In implementation, the pollution preset range is determined according to the risk screening value in the Soil Environmental Quality Agricultural Land Soil Pollution Risk Control Standard (Trial) (GB 15618-2018). The risk screening value is the maximum allowable heavy metal content value. The purpose of the present invention is to provide safety warnings for agricultural products. Therefore, in the present invention, the maximum value of the preset range is set to 0.9 times the corresponding risk screening value, that is, a safety warning is required when the risk screening value exceeds 0.9 times.

[0124] Specifically, in step S9, the method of determining the monitoring adjustment strategy of the corresponding monitoring range according to the number of times each monitoring range sends out a safety warning signal in the previous monitoring cycle includes:

[0125] If no safety warning signal was issued in the previous monitoring cycle, the monitoring adjustment strategy for the corresponding monitoring range in this monitoring cycle is to reduce the monitoring frequency to the minimum monitoring frequency (once per quarter);

[0126] If the number of safety warning signals issued in the previous monitoring cycle is less than the preset number, it is determined that no monitoring adjustment strategy for the corresponding monitoring range will be formulated in the current monitoring cycle, that is, the monitoring frequency is the same as the previous monitoring cycle;

[0127] If the number of safety warning signals issued in the previous monitoring cycle is greater than or equal to the preset number, it is determined that the monitoring adjustment strategy for the corresponding monitoring range in the current monitoring cycle is to increase the monitoring frequency.

[0128] It can be understood that the monitoring frequency ranges from 2 times / month to 1 time / quarter; the initial monitoring frequency is once a month, that is, the monitoring frequency of each monitoring point in the initial stage of free-range target organisms is set to once a month; thereafter, the monitoring frequency and monitoring period are determined based on the number of safety warnings in the previous monitoring period.

[0129] It can be understood that the monitoring period varies with the monitoring frequency. In one implementation, if the monitoring frequency is once a month, the corresponding monitoring period is 1 month; if the monitoring frequency is once every two months, the corresponding monitoring period is two months; if the monitoring frequency is three times a month, the corresponding monitoring period is half a month.

[0130] In practice, the preset number of times is usually 3 to 5 times. In order to allow the target organisms to better adapt to the free-range process while ensuring their safety, the preset number of times is preferably set to 3 times.

[0131] Specifically, when deciding to increase the monitoring frequency, the monitoring frequency of the current monitoring cycle is positively correlated with the number of times safety warning signals were issued in the previous monitoring cycle.

[0132] It can be understood that the monitoring frequency of this monitoring cycle = the number of times the safety warning signal was issued in the previous monitoring cycle ÷ the preset number of times × the monitoring frequency of the previous monitoring cycle. The principle of rounding up is adopted when calculating the monitoring frequency, but the maximum monitoring frequency can only be 3 times / month; after determining the monitoring frequency, the monitoring cycle of this time is determined.

[0133] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An agricultural product safety early warning method based on artificial intelligence, characterized in that: include: Obtaining the free-range range and historical death sites of the target organisms, and identifying each of the historical death sites as a target location; Determine the corresponding target reference range and target monitoring range according to the target position; Determining a position representation state thereof according to the number of target positions within the target reference range; Determine the soil characterization status of the target reference range according to whether it meets the soil monitoring conditions; Determining a monitoring type of the target location according to the location characterization state and the soil characterization state, wherein the monitoring type includes a key monitoring type and a secondary key monitoring type; Determine the number of monitoring points corresponding to the target monitoring range according to the monitoring type of the target location; Determine a common monitoring area according to the free-range range and each of the target monitoring ranges to determine a number of common monitoring ranges; Determine whether to issue a safety warning signal based on the soil pH and soil heavy metal content in each monitoring range; The monitoring adjustment strategy for the corresponding monitoring range is determined based on the number of safety warning signals issued by each monitoring range in the previous monitoring cycle. include, Increasing monitoring frequency, decreasing monitoring frequency, and not developing a monitoring adjustment strategy.

2. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: The method for determining the corresponding target reference range and target monitoring range according to the target position includes: Determine the base length and reference length; Determine the target reference range according to the target position and the reference length; Determine the target monitoring range according to the target position and the reference length; The target reference range is a circle with the target position as the center and the reference length as the radius, and the target monitoring range is a circle with the target position as the center and the reference length as the radius.

3. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: Determining the position representation state of the target according to the number of target positions within the target reference range includes: If the number of target positions within the target reference range is greater than or equal to a preset number, then determining that the position representation state is a densely monitored position; If the number of target positions within the target reference range is less than a preset number, then the position representation state is determined to be an independent monitoring position.

4. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: The method for determining the soil characterization state according to whether the target reference range meets the soil monitoring conditions includes: Obtain soil type and soil pH for each target benchmark range; The soil characterization state is determined according to whether the soil type and the soil pH in each target reference range meet the soil monitoring conditions, wherein: If the soil type and soil pH within a single target reference range meet the soil monitoring conditions, then the soil characterization state is determined to be a poor characterization state; If the soil type and the soil pH within the single target reference range do not meet the soil monitoring conditions, then the soil characterization state is determined to be a benign characterization state; The soil monitoring conditions are that the soil texture is clay and / or the soil pH exceeds a preset range of pH.

5. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: Determining the monitoring type of the target location according to the location characterization state and the soil characterization state includes: If the location characterization state is an intensive monitoring location and the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a key monitoring type; If the location characterization state is an intensive monitoring location or the soil characterization state is a poor characterization state, the monitoring type of the target location is determined to be a secondary key monitoring type.

6. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: Determine the number of monitoring points corresponding to the target monitoring range according to the monitoring type of the target location, include, If the monitoring type is a key monitoring type, the number of monitoring points corresponding to the target monitoring range is the first reference number; If the monitoring type is a secondary key monitoring type, the number of monitoring points corresponding to the target monitoring range is the second reference number; The first reference quantity is greater than the second reference quantity.

7. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: The method of determining a plurality of common monitoring ranges according to the free-range range and each of the target monitoring ranges comprises: Determine the range outside each target monitoring range in the free-range range as a common monitoring area; Determining the base number of the ordinary monitoring range according to the ratio of the area of ​​the ordinary monitoring area to the preset area; Dividing the common monitoring area into a benchmark number of common monitoring ranges; Among them, the areas of each of the ordinary monitoring ranges are equal.

8. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: Determine whether to issue a safety warning signal based on the soil pH and heavy metal content in each monitoring range. include, If the soil pH exceeds the preset pH range and / or the soil heavy metal content exceeds the preset pollution range, a safety warning signal is issued; If the soil pH does not exceed the preset pH range and the soil heavy metal content does not exceed the preset pollution range, it is determined that no safety warning signal will be issued.

9. The agricultural product safety early warning method based on artificial intelligence according to claim 1 is characterized in that: The method of determining the monitoring adjustment strategy of the corresponding monitoring range according to the number of times each monitoring range sends out a safety warning signal in the previous monitoring cycle includes: If no safety warning signal is issued in the previous monitoring cycle, the monitoring adjustment strategy for the corresponding monitoring range is to reduce the monitoring frequency to the minimum monitoring frequency; If the number of safety warning signals issued in the previous monitoring cycle is less than the preset number, it is determined that no monitoring adjustment strategy for the corresponding monitoring range will be formulated; If the number of safety warning signals issued in the previous monitoring cycle is greater than or equal to the preset number, the monitoring adjustment strategy for the corresponding monitoring range is determined to be to increase the monitoring frequency.

10. The agricultural product safety early warning method based on artificial intelligence according to claim 9 is characterized in that: When deciding to increase the monitoring frequency, the monitoring frequency is positively correlated with the number of times safety warning signals were issued in the previous monitoring cycle.

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