Heap leaching field spray intelligent regulation system and method based on internet of things

Abstract

The present application relates to program control system technical field, specifically be a kind of based on Internet of Things's heap leaching field intelligent control system and method of spraying, system includes: Internet of Things partition state perception module, obtain the current humidity, permeation rate, ore grade, leaching time and leaching state parameters of M×N heap leaching field partitions from Internet of Things terminal, arrange combination according to the spatial position in heap leaching field, obtain real-time heap leaching state matrix.In the present application, the fine data such as humidity, permeation rate, ore grade, leaching time and leaching state of each partition of heap leaching field are obtained in real time through Internet of Things, to form real-time state matrix based on spatial position, realize heap leaching state perception, ensure that spraying task and actual demand dynamic matching;And when generating spraying task, through the double screening of leaching time and humidity of each partition, target area and its execution order are reasonably determined, and unnecessary work load is reduced.

Description

Technical Field

[0001] This invention relates to the field of program control system technology, and in particular to an intelligent control system and method for heap leaching spraying based on the Internet of Things. Background Technology

[0002] A programmable control system is based on a preset control program. It uses computers, programmable logic controllers (PLCs) or industrial computing devices, along with various sensors, actuators and communication modules, to acquire real-time data on the field environment and equipment operating status. After analysis, judgment and optimization calculation, it generates precise control commands to achieve automated monitoring and intelligent control of industrial production processes or equipment operating status.

[0003] Existing technologies rely on preset program control modes during actual operation, generating control commands solely through the collection and analysis of equipment or environmental parameters. They lack differentiation between different spatial zones within the production area, making it difficult to precisely match control actions with actual operating conditions and leading to discrepancies between production resource allocation and actual needs. Furthermore, existing technologies neglect the identification and response to systemic risks in heap leaching fields, such as heap saturation, insufficient storage capacity, or slope instability. This makes the control process overly mechanized and lagging, unable to adjust control strategies promptly in the face of unexpected environmental conditions, thus increasing operational risks. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent control system and method for heap leaching spraying based on the Internet of Things.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent control system for heap leaching spraying based on the Internet of Things includes: The IoT partition status sensing module obtains the current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field partitions from IoT terminals, and arranges them according to their spatial location in the heap leaching field to obtain a real-time heap leaching status matrix. The spraying decision program generation module, based on the real-time heap leaching state matrix, scans the leaching time and current humidity of all partitions in the matrix, filters to obtain a list of partitions to be processed, configures rest leaching program instructions for each target partition according to the list of partitions to be processed, sorts them according to execution priority, and generates a basic spraying task sequence. The heap leaching site risk assessment module obtains the heap saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall for the next 24 hours. It then normalizes these parameters to obtain the heap saturation factor, pool capacity vulnerability factor, slope instability probability factor, and expected rainfall impact factor. A multidimensional risk factor set is established, and the environmental risk exposure index is calculated based on this multidimensional risk factor set. The preventive safety control module compares the environmental risk exposure index with the warning line and danger line thresholds to determine the range to which it belongs, obtains the system risk response level, and generates a control instruction program for PLC to execute based on the system risk response level.

[0006] Preferably, the steps for obtaining the real-time heap immersion state matrix are as follows: The current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field zones are collected from IoT terminals. Based on the spatial location coordinates of the IoT terminals in each zone of the heap leaching field, the collected parameter values ​​are spatially marked to generate a set of zone parameters with spatial location information. Based on the set of partition parameters with spatial location information, the horizontal and vertical coordinates of the IoT terminals in each partition of the heap leaching field are sorted in two dimensions row by row and column by column to determine the relative position index of each partition in the heap leaching field. The position index is then bound to the corresponding partition parameter value to generate a spatial sorting sequence of partition parameters. Based on the spatial sorting sequence of the partition parameters, the current humidity, permeation rate, ore grade, leaching time and rest leaching state parameters corresponding to each partition are sequentially filled into the array cells corresponding to the position index using a two-dimensional array structure. The filling is completed in an M×N spatial layout to obtain the real-time heap leaching state matrix.

[0007] Preferably, the step of obtaining the list of partitions to be processed is as follows: Based on the real-time heap leaching state matrix, the leaching time values ​​of all heap leaching field partitions in the matrix are scanned sequentially. The leaching time values ​​of all heap leaching field partitions are compared, and the heap leaching field partition with the longest leaching time value in the matrix is ​​determined, generating a set of heap leaching field partitions with the longest leaching time value. Based on the set of heap leaching field partitions with the longest leaching time, the current humidity value of the corresponding heap leaching field partition in the real-time heap leaching state matrix is ​​called and compared with the preset dry area threshold value one by one. Heap leaching field partitions with current humidity values ​​less than the dry area threshold are selected to form a set of heap leaching field partitions whose humidity meets the dry area condition. Based on the real-time heap leaching state matrix, the current humidity value and rest leaching duration value of all heap leaching field partitions are called one by one. The current humidity value is compared with the preset saturation threshold, and the rest leaching duration value is compared with the preset minimum rest leaching duration. Heap leaching field partitions whose current humidity value reaches the saturation threshold and whose rest leaching duration value exceeds the minimum rest leaching duration are selected to form a set of heap leaching field partitions with saturated humidity and rest leaching duration that meet the conditions. Based on the set of heap leaching zone partitions that meet the dry zone conditions and the set of heap leaching zone partitions that meet the conditions of humidity saturation and rest leaching duration, a list of partitions to be processed is formed.

[0008] Preferably, the step of obtaining the basic spraying task sequence is as follows: Based on the list of partitions to be processed, the ore grade, leaching time, permeation rate and rest leaching duration values ​​corresponding to each heap leaching field partition are extracted one by one. By setting a spray type judgment process, it is identified whether the spray duration configuration conditions, spray flow configuration conditions or rest leaching program configuration conditions are met, and a set of spray configuration instructions to be evaluated is generated based on the judgment results. Based on the set of spray configuration instructions to be evaluated, calculate the spray configuration priority value for each heap leaching zone; Based on the spray configuration priority value, all heap leaching zone partitions are prioritized in descending order. The ranking result is combined with the spray duration instruction, spray flow instruction and rest leaching procedure instruction of each partition to form an execution task unit, thus forming a basic spray task sequence.

[0009] Preferably, the steps for obtaining the multidimensional risk factor set are as follows: The system obtains values ​​for pile saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall over the next 24 hours. It then constructs theoretical lower and lower bounds and performs linear normalization on all raw parameters, with slope stability parameters being normalized by inversion. This results in pile saturation factor, precious liquid pool capacity vulnerability factor, emergency pool capacity vulnerability factor, slope instability risk factor, and expected rainfall impact factor. Based on the saturation factor of the heap, the vulnerability factor of the precious liquid pool, the vulnerability factor of the accident pool, and the slope instability risk factor, the values ​​are verified in turn to see if they are within the closed interval. Normalization failures or abnormal missing terms are eliminated, and together with the expected rainfall impact factor, they form a multidimensional risk factor set.

[0010] Preferably, the steps for obtaining the environmental risk exposure index are as follows: Based on the aforementioned multidimensional risk factor set, the environmental risk exposure index is calculated.

[0011] Preferably, the steps for obtaining the system risk response level are as follows: Based on the environmental risk exposure index, preset warning threshold and danger threshold are invoked, and the environmental risk exposure index is compared with the warning threshold and danger threshold one by one to determine the risk level range to which the environmental risk exposure index belongs, and the system risk response level is generated.

[0012] Preferably, the step of obtaining the control instruction program for PLC execution is as follows: Based on the system risk response level, the basic spraying task sequence is invoked and it is determined whether the system risk response level has reached the warning threshold. If the environmental risk exposure index value has not reached the warning threshold, the basic spraying task sequence remains unchanged. If it has reached or exceeded the warning threshold, the risk avoidance subroutine is invoked to reduce the overall spraying intensity by adjusting the values ​​of spraying duration and spraying flow rate in the task sequence item by item, thereby generating a risk-avoidance adjusted spraying task sequence. Based on the system risk response level and the spray task sequence adjusted for risk avoidance, it is determined whether the environmental risk exposure index value has reached the danger threshold. If it has reached or exceeded the danger threshold, the emergency limiting subroutine is called to delete and reconstruct the task items in the spray task sequence adjusted for risk avoidance, and generate a control instruction program for PLC to execute.

[0013] This invention also provides a method for intelligent control of spraying in heap leaching fields, comprising the following steps: The current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field zones are obtained from the Internet of Things terminal and arranged and combined according to their spatial location in the heap leaching field to obtain a real-time heap leaching status matrix. Based on the real-time heap leaching state matrix, the leaching time and current humidity of all partitions in the matrix are scanned to obtain a list of partitions to be processed. Based on the list of partitions to be processed, rest leaching program instructions are configured for each target partition and sorted according to execution priority to generate a basic spraying task sequence. The saturation of the heap, the capacity of the precious liquid pool, the capacity of the emergency pool, the slope stability parameters, and the predicted rainfall for the next 24 hours are obtained and normalized to obtain the heap saturation factor, the capacity vulnerability factor, the slope instability probability factor, and the expected rainfall impact factor. A multidimensional risk factor set is established, and the environmental risk exposure index is calculated based on the multidimensional risk factor set. The environmental risk exposure index is compared with the warning line and danger line thresholds to determine the range to which it belongs, and the system risk response level is obtained. Based on the system risk response level, a control instruction program for PLC execution is generated.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, refined data such as humidity, permeation rate, ore grade, leaching time, and resting leaching status of each zone in the heap leaching field are acquired in real time through the Internet of Things (IoT). A real-time status matrix is ​​formed based on spatial location, enabling heap leaching status awareness and ensuring dynamic matching between spraying tasks and actual needs. When generating spraying tasks, the target area and its execution order are rationally determined through dual screening of leaching time and humidity in each zone, reducing resource waste and unnecessary workload. Furthermore, by introducing normalized analysis of multi-dimensional risk factors such as heap saturation, reservoir vulnerability, slope instability probability, and rainfall prediction, an environmental risk exposure index is used to describe the system's risk status, improving the accuracy and sensitivity of risk identification. Based on the interval judgment between the risk exposure index and preset thresholds, the execution strategy of the spraying task is automatically adjusted, enabling rapid response in the event of sudden environmental incidents or high-risk situations, avoiding production accidents, and improving the safety and stability of system operation. Attached Figure Description

[0015] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Please see Figure 1 This invention provides a technical solution: an IoT-based intelligent control system for heap leaching spraying, comprising: The IoT partition status sensing module obtains the current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field partitions from IoT terminals, and arranges them according to their spatial location in the heap leaching field to obtain a real-time heap leaching status matrix. The spraying decision program generation module, based on the real-time heap leaching state matrix, scans the leaching time and current humidity of all partitions in the matrix, filters to obtain a list of partitions to be processed, configures leaching program instructions for each target partition according to the list of partitions to be processed, sorts them according to execution priority, and generates a basic spraying task sequence. The heap leaching risk assessment module obtains the heap saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and the predicted rainfall for the next 24 hours. After normalization, it obtains the heap saturation factor, pool capacity vulnerability factor, slope instability probability factor, and expected rainfall impact factor. A multidimensional risk factor set is established, and the environmental risk exposure index is calculated from the multidimensional risk factor set. The preventive safety control module compares the environmental risk exposure index with warning and danger thresholds to determine the relevant range, obtains the system risk response level, and generates control instructions for the PLC to execute based on the system risk response level.

[0018] The steps for obtaining the real-time heap immersion state matrix are as follows: The current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field zones are collected from IoT terminals. Based on the spatial location coordinates of the IoT terminals in each zone of the heap leaching field, the collected parameter values ​​are spatially marked to generate a set of zone parameters with spatial location information. Based on the set of partition parameters with spatial location information, the horizontal and vertical coordinates of the IoT terminals in each partition of the heap leaching field are sorted in two dimensions row by row and column by column to determine the relative position index of each partition in the heap leaching field. The position index is then bound to the corresponding partition parameter value to generate a spatial sorting sequence of partition parameters. Based on the spatial sorting sequence of partition parameters, the current humidity, permeation rate, ore grade, leaching time and rest leaching state parameters corresponding to each partition are sequentially filled into the array cell corresponding to the position index using a two-dimensional array structure. The filling is completed in an M×N spatial layout to obtain the real-time heap leaching state matrix.

[0019] Specifically, after collecting current humidity, permeation rate, ore grade, leaching time, and resting leaching status parameters from M×N heap leaching zone partitions from IoT terminals, the system first initiates a data-spatial location association processing flow. This is achieved by retrieving a pre-configured heap leaching zone IoT terminal deployment map. This map records the unique identification code of each IoT terminal and its physical coordinates within the heap leaching zone. For example, the southwest corner of the heap leaching zone is defined as the coordinate origin (0,0), and the coordinates of each terminal are precisely calibrated in meters. For instance, the location of terminal T001 is (10.5, 25.2), and the location of terminal T002 is (10.6, 25.2). 35.8) This information is solidified into a terminal location mapping table. When the system receives data packets from each terminal, each data packet carries a unique terminal identification code. The system queries the terminal location mapping table to match the terminal identification code in the data packet with the corresponding physical coordinates, and appends this coordinate information to the data record, forming an extended data record containing terminal identification code, physical coordinates, current humidity, permeation rate, ore grade, leaching time, and resting leaching state parameters. During this process, the system performs preliminary data verification, checking the collected values ​​according to preset parameter validity rules. For example, humidity values... The effective range is set to 0% to 100%. Any data exceeding this range will be marked as abnormal but will not be deleted temporarily. The effective range of the permeability rate is empirically set to 0.1 liters / m²·h to 20 liters / m²·h based on the ore type and pile structure. The ore grade is set to a reasonable range based on the geological exploration report, such as 0.5 g / t to 8.0 g / t. If the terminal identification code in the data packet cannot be found in the mapping table, the data record is marked as "unknown source" and a system log is generated for subsequent investigation. After processing all data packets, the final set of partition parameters with spatial location information is obtained.

[0020] Based on the partition parameter set with spatial location information, the system performs spatial sorting and index allocation operations. First, the system performs a secondary sort on all extended data records with physical coordinates in the set. The sorting rule adopts a dual-key value sorting, with the primary key being the vertical coordinate value, arranged in ascending order, and the secondary key being the horizontal coordinate value, also arranged in ascending order. This sorting operation ensures that all partition data are linearly arranged row by row and column by column, starting from one side of the heap leaching field, laying the foundation for subsequent matrix processing. After sorting, the system assigns a two-dimensional relative position index (i, j) to each sorted data record according to the preset heap leaching field partition grid layout, i.e., M rows and N columns, where i represents the row number and j represents the column number. The index allocation is implemented through a loop counting process. For example, for a 100×50 (M=100, N=50) heap leaching field, the first sorted data record is assigned the index (0, 0), the second record is (0, 1), and so on until the 50th record is (0, 49). The next 51st record is assigned the index (1, 0, 1). 0), and so on, the index (i, j) of the kth data record (k starts counting from 1) can be calculated. The row index i is the result of (k-1) divided by N, and the column index j is the result of (k-1) modulo N. During the index allocation process, the system will also handle the problem of missing partition data. If the total number of actual valid partition data records is less than M×N, after sorting and index allocation, the system will identify which (i, j) index positions do not have corresponding data records according to the definitions of M and N. For these empty positions, the system will create a placeholder record with the position index of the empty position (i, j), and all parameter values ​​will be assigned a specific null value identifier, such as -999.9, to clearly distinguish between real collected data and missing data. Subsequently, the system will strongly type-bind the relative position index (i, j) of each partition with its corresponding set of parameter values ​​to generate a partition parameter space sorting sequence.

[0021] Based on the spatial sorting sequence of partitioned parameters, the system performs a matrix construction operation. This process does not generate a single two-dimensional array, but rather creates a composite data structure containing multiple parallel two-dimensional arrays. Specifically, the system pre-initializes five independent M×N two-dimensional arrays according to the M×N grid layout of the heap leaching field, named the humidity matrix, permeation rate matrix, ore grade matrix, leaching time matrix, and resting leaching state matrix, respectively. Subsequently, the system traverses each record in the spatial sorting sequence of partitioned parameters. Each record contains a relative position index (i, j) and a series of parameter values ​​such as the current humidity, permeation rate, ore grade, leaching time, and resting leaching state. For the k-th record in the sequence, the system extracts its position index (i, j). j) and each parameter value, and then precisely fill these parameter values ​​into the corresponding cells of the corresponding matrices. For example, fill the current humidity value of the record into the i-th row and j-th column of the humidity matrix, i.e., humidity matrix [i][j], fill its permeation rate value into the permeation rate matrix [i][j], and so on, until all five parameters are filled into their respective matrices. For placeholder records marked as missing data in the previous step, when the system processes these records, it will fill the preset null value flag -999.9 into the corresponding (i, j) cells of the five matrices. For cells at position j), after all M×N positions have been filled with data, the system performs an integrity check, calculates the percentage of null values ​​in each parameter matrix, and compares it with a preset "data integrity alarm threshold." This threshold is set based on historical operating data and expert experience. For example, if the missing data in any matrix exceeds 15%, the current data is considered insufficient to support reliable decision-making, and the system will generate a high-priority alarm to notify maintenance personnel to check for communication or sensor failures in IoT terminals. The basis for setting this threshold is that data missing of less than 15% can be effectively compensated by methods such as nearest neighbor interpolation, while data missing of more than 15% will not be compensated effectively and may lead to incorrect spraying decisions. After all filling and verification are completed, these five spatially aligned M×N matrices are encapsulated together to form the real-time heap immersion state matrix.

[0022] The steps to obtain the list of partitions to be processed are as follows: Based on the real-time heap leaching state matrix, the leaching time values ​​of all heap leaching field partitions in the matrix are scanned sequentially. The leaching time values ​​of all heap leaching field partitions are compared to determine the heap leaching field partition with the longest leaching time value in the matrix, and a set of heap leaching field partitions with the longest leaching time value is generated. Based on the set of heap leaching field partitions with the longest leaching time, the current humidity value of the corresponding heap leaching field partition in the real-time heap leaching state matrix is ​​called and compared with the preset dry zone threshold one by one. Heap leaching field partitions with current humidity values ​​less than the dry zone threshold are selected to form a set of heap leaching field partitions whose humidity meets the dry zone condition. Based on the real-time heap leaching state matrix, the current humidity value and rest leaching duration value of all heap leaching field partitions are called one by one. The current humidity value is compared with the preset saturation threshold, and the rest leaching duration value is compared with the preset minimum rest leaching duration. Heap leaching field partitions whose current humidity value reaches the saturation threshold and whose rest leaching duration value exceeds the minimum rest leaching duration are selected to form a set of heap leaching field partitions with saturated humidity and rest leaching duration that meet the conditions. The set of heap leaching zone partitions that meet the humidity requirements for dry zones and the set of heap leaching zone partitions that meet the humidity requirements and rest leaching duration requirements are integrated to form a list of partitions to be processed.

[0023] Specifically, based on the real-time heap leaching state matrix, the system first extracts the leaching time matrix from the composite data structure. This matrix is ​​an M×N two-dimensional array, where each cell of the leaching time matrix [i][j] stores the cumulative leaching time value of the corresponding partition in hours. The system starts an iterative scanning program to read the leaching time value of all non-empty cells in the matrix one by one and store them in a temporary one-dimensional list. During the traversal, the program tracks and updates the currently found maximum leaching time value in real time, denoted as T_max. After traversing all M×N partitions, T_max is the longest leaching time of all partitions in the entire heap leaching field. Subsequently, the system does not simply select partitions with a leaching time equal to... Instead of using a partition of T_max, a dynamic "longest leaching cycle identification window" is introduced. The lower limit of this window is obtained by multiplying T_max by a preset "longest cycle tolerance coefficient". This tolerance coefficient is set according to the production process requirements and aims to include partitions that are close to completing their leaching cycle. For example, if the tolerance coefficient is set to 0.98, then the lower limit of the window is T_max multiplied by 0.98. For example, if the current longest leaching time T_max in the field is 1200 hours, then the identification window range is the closed interval [1176, 1200] hours. The system then traverses the leaching time matrix again, comparing the leaching time value of each partition with this window range. For any heap leaching field partition whose leaching time value falls within the interval [1176, 1200] hours, its corresponding position index (i, j) is recorded and added to an initially empty set. This set does not allow duplicate elements. After the traversal is completed, this set is the set of heap leaching field partitions with the longest leaching time value.

[0024] Based on the set of heap leaching field partitions with the longest leaching time, the system continues the screening process, judging the dry zone condition compliance of each partition in the set. Specifically, the system traverses the position index (i, j) of each partition in the set and uses this index to directly retrieve the current humidity value of the corresponding position from the real-time heap leaching state matrix, i.e., the humidity matrix [i][j]. Then, the obtained current humidity value is compared with a preset "dry zone humidity threshold". This threshold is determined based on the physical characteristics of the ore and the experimental data of leaching kinetics. The setting process is as follows: First, by conducting a series of column leaching experiments on representative ore samples under different initial humidity, the relationship between humidity and the concentration of target metal ions in the leachate is monitored and recorded, and a humidity-leaching rate curve is plotted. Analysis of the curve reveals that when the volumetric water content of the ore is lower than a certain value, the leaching rate will decrease nonlinearly and sharply, indicating the appearance of a dry zone in the heap and a reduction in the effective wetting area. For example, for For specific types of gold ore, experiments have shown that when the humidity is below 18%, the gold recovery rate drops by more than 20%. Therefore, to ensure leaching efficiency, the humidity threshold for dry areas is set to 18%. When the system performs a judgment, if the current humidity value read from the humidity matrix [i][j] is less than 18%, for example, 15.5%, then the partition is determined to meet the dry area condition, and its position index (i,j) is retained and added to a new set. If the humidity value read is 22%, then because it is not less than 18%, the partition is not considered a dry area that needs to be processed immediately, and its index will be discarded. After completing the humidity judgment of all partitions in the heap leaching field partition set with the longest leaching time value, the final set of heap leaching field partitions that meet the dry area condition is formed.

[0025] Based on the real-time heap leaching state matrix, the system initiates a parallel global scan task to identify zones that have undergone sufficient resting and are ready for re-spraying. This task traverses all M×N zones in the entire heap leaching field. For each zone's location index (i, j), the system simultaneously retrieves its current humidity value (humidity matrix [i][j]) and resting state duration value (resting state matrix [i][j]) from the real-time heap leaching state matrix. The system compares the current humidity value with the "preset saturation threshold" and the resting state duration value with the "preset minimum resting duration." Both thresholds are set based on the hydraulic characteristics and geochemical reaction kinetics of the ore pile. The saturation threshold is set based on the field water holding capacity of the ore pile. By saturating the ore pillar through on-site sampling or laboratory reconstruction and then freely draining it, the average humidity of the ore pile measured when the drainage rate tends to a very low stable value is the field water holding capacity. For example, if the measured value is 35%, considering sensor errors and local non-uniformity, the preset saturation threshold is usually set to the field water holding capacity. The water volume is 1.05 to 1.1 times the normal volume, set to 37% here. This means that when the current humidity value is greater than or equal to 37%, it is considered to be saturated. The minimum resting time needs to take into account the time required for oxygen diffusion and secondary reactions in the heap during the resting period. By analyzing mineral samples with different resting times, a minimum time that can significantly restore the subsequent spray leaching rate is determined. For example, for sulfide minerals, experiments show that after 48 hours of resting, the oxygen concentration in the heap recovers to a level that can support bacterial oxidation. Therefore, the minimum resting time is preset to 48 hours. The system's judgment logic for each partition is: only when both conditions are met simultaneously, such as the current humidity value of a partition being greater than or equal to 37% and its resting state duration being strictly greater than 48 hours, will the location index (i, j) of that partition be selected and added to an independent set. After this process covers all partitions, a set of heap leaching field partitions with saturated humidity and resting state durations is formed.

[0026] Based on the set of heap leaching field partitions that meet the dry zone condition and the set of heap leaching field partitions that meet the condition of saturated humidity and rest leaching duration, the system performs a final list integration operation. This process is accomplished through a set union operation. The system creates an initially empty final list. First, all partition position indices (i, j) in the set of heap leaching field partitions that meet the dry zone condition are added to this final list one by one. Then, all partition position indices (i, j) in the set of heap leaching field partitions that meet the condition of saturated humidity and rest leaching duration are also added to the same final list one by one. During the addition process, the system automatically handles duplicate items. Since the physical state of a partition at any given time cannot be both below the dry zone threshold and above the saturated threshold, these two source sets are logically mutually exclusive, and in reality, there will be no duplicate position indices. The integration operation essentially brings together two types of partitions with different processing reasons but both need to be configured for spraying or rest leaching in the next stage, forming a unified pool of objects to be processed. For example, if the set of heap leaching field partitions that meet the dry zone condition contains index {(10,5)}, (22,18)}, while the set of heap leaching field partitions that are saturated with humidity and meet the resting leaching duration conditions contains the index {(15,30), (40,12)}, then the integrated list will contain {(10,5),(22,18), (15,30), (40,12)}. This final generated list containing the location indexes of all partitions to be processed is the list of partitions to be processed.

[0027] The steps to obtain the basic spraying task sequence are as follows: Based on the list of partitions to be processed, the ore grade, leaching time, permeation rate and rest leaching duration values ​​corresponding to each heap leaching field partition are extracted one by one. By setting the spray type judgment process, it is identified whether the spray duration configuration conditions, spray flow configuration conditions or rest leaching procedure configuration conditions are met, and a set of spray configuration instructions to be evaluated is generated based on the judgment results. Based on the set of spray configuration instructions to be evaluated, the priority value of the spray configuration for each heap leaching zone is calculated using the following formula: ; in, For the first Priority values ​​for spray configuration in each heap leaching zone. For the first Ore grade values ​​for each heap leaching zone. For the first The leaching time or cumulative resting time for each heap leaching zone. This is the time effect coefficient. For the first Permeation rate values ​​for each heap leaching zone It is the half-saturated permeation rate constant; Based on the priority value of the spray configuration, all heap leaching zone partitions are prioritized in descending order. The ranking result is combined with the spray duration instruction, spray flow instruction and rest leaching procedure instruction of each partition to form an execution task unit, thus forming a basic spray task sequence.

[0028] Specifically, based on the list of partitions to be processed, the system initiates an instruction type identification process. It iterates through the location index of each heap leaching field partition in the list and extracts its complete parameter set from the real-time heap leaching state matrix based on that index. This parameter set includes ore grade, leaching time, permeation rate, current humidity, and resting leaching duration. Next, the system executes a rule-based spray type judgment process. The core of this process is a predefined logical judgment tree used to determine what specific control operation is needed for each partition to be processed. The first step of this judgment process is to distinguish the source of the partition, i.e., whether the partition was included in the list of partitions to be processed because "humidity meets dry zone conditions" or because "humidity is saturated and the resting leaching duration meets the conditions." For partitions whose source is "humidity meets dry zone conditions," their current humidity must be lower than the preset dry zone humidity threshold, for example, lower than 18%. The system determines that such partitions urgently need water replenishment to restore an effective leaching environment. Therefore, the system identifies that they have the appropriate spray duration. The system sets the conditions for setting the spray flow rate and marks the configuration type as "dry area water replenishment". For partitions whose source is "humidity saturation and rest soaking time meets the conditions", their current humidity has reached saturation and the necessary rest soaking process has been completed. For example, if the humidity is higher than 37% and the rest soaking time exceeds 48 hours, the system determines that such partitions are ready to enter a new spray leaching cycle. Therefore, the system also identifies that they have the conditions for setting the spray duration and spray flow rate and marks the configuration type as "periodic spraying". As for the rest soaking procedure configuration conditions, in this process, since the composition logic of the partition list determines that all partitions in it are to be sprayed, the system determines that all partitions in the list do not have the conditions for setting the rest soaking procedure. The result of this judgment is "no". After the judgment is completed, the system generates a structured data object for each partition, which includes its location index, all relevant parameters and the identified configuration type mark. All these objects are gathered together to form a set of spray configuration instructions to be evaluated.

[0029] formula: The advantage of the formula lies in its ability to incorporate ore grade. This ensures that high-value areas have priority access to leachate, which is directly related to economic output and the cumulative effect over time. A logarithmic function was used to simulate the natural law of diminishing marginal benefits during leaching or resting, avoiding giving excessive weight to the zones at the end of leaching or the beginning of resting, and the hydraulic conduction term... Using the Michaelis equation, the advantages of high permeability are effectively balanced with the risk of solution short-circuiting caused by excessive seepage, making the priority allocation more in line with the actual fluid dynamics. The three factors are multiplied to construct a comprehensive evaluation index for multi-objective optimization. parameter For the first The ore grade values ​​for each heap leaching zone represent the average content of the target metal in the ore of that zone, expressed in grams per ton (g / t). These values ​​originate from geological exploration and ore blending schemes prior to the construction of the heap leaching site. During the construction of the heap leaching site, the system has already entered the grade data for each zone and generated an initial ore grade matrix. During calculations, the ore grade matrix is ​​directly retrieved from the real-time heap leaching status matrix based on the zone index. Retrieve the corresponding value; this is a static parameter that remains unchanged within a leaching cycle. For example, for a partition with index (10, 5), its grade in the geological block model is 3.2 g / t. .

[0030] parameter For the first The leaching time or cumulative resting time value for each heap leaching zone, in hours (h), is a dynamic parameter. Its specific meaning depends on the current state of the zone, which is marked when the set of spray configuration instructions to be evaluated is generated. If the zone's configuration type is marked as "dry zone makeup," then... The value is the cumulative leaching time of the partition, obtained from the leaching time matrix of the real-time heap leaching state matrix. If the partition's configuration type is marked as "periodic spraying", then... The value is the cumulative rest immersion time for that partition, obtained from the rest immersion state matrix of the real-time heap immersion state matrix. For example, for partition (10, 5) with a configuration type of "dry area replenishment", it has accumulated 1350 hours of immersion. .

[0031] parameter The time effect coefficient is a dimensionless adjustment coefficient used to adjust the weight of the time factor in priority calculation. Its value is determined based on statistical analysis of historical production data. Specifically, it involves collecting daily leaching rate data and corresponding leaching and resting time data for at least five complete production cycles, establishing a multiple nonlinear regression model with total recovery rate as the dependent variable and total leaching and resting time as independent variables, and fitting the model using the least squares method to obtain the optimal coefficient before the time term. This coefficient is the time effect coefficient. The baseline value, for example, is obtained by performing regression analysis on historical data, resulting in the model: recovery rate. Therefore, it is determined The baseline value is 0.004.

[0032] parameter For the first The permeability rate value for each heap leaching zone represents the volume of liquid flowing through a unit area of ​​ore per unit time, expressed in liters per square meter per hour (L / (m²·h)). This value is monitored and uploaded in real time by IoT terminals deployed within the zone. The system directly retrieves this value from the permeability rate matrix of the real-time heap leaching state matrix. This is a dynamically changing parameter that reflects the current hydraulic conductivity characteristics of the zone. For example, for the zone with index (10, 5), the latest permeability rate sensor reading is 6.5 L / (m²·h). .

[0033] parameter The half-saturated permeation rate constant is expressed in units of 1 / 2π. The same applies, expressed as liters per square meter per hour (L / (m²·h)). This parameter is a global physical constant for the entire heap leaching field, characterizing the permeation rate when the hydraulic conductivity reaches half of its maximum value. Its value is determined through laboratory pillar leaching experiments. The specific steps are as follows: select representative ore samples to prepare multiple pillars, conduct leaching experiments with increasing spray intensities, continuously record the actual permeation rate at each intensity, and plot the spray intensity-permeation rate relationship curve. This curve is usually S-shaped. Find the permeation rate value at the point where the curve rises the fastest, or the permeation rate value corresponding to the spray intensity reaching half of the maximum permeation rate. This value is the permeation rate. For example, experimental measurements show that for the currently processed ore, when the permeation rate reaches 8.0 L / (m²·h), the gain in hydraulic conductivity begins to slow down significantly. Therefore, a setting is made... .

[0034] Calculation process: Based on the set of spray configuration instructions to be evaluated, calculate the spray configuration priority value for the heap leaching zone with index (10, 5). This zone belongs to the "dry area water replenishment" type. The values ​​for each parameter are as follows: ; ; ; ; ; Calculation process: ; ; ; ; ; The results indicate that the priority value for the sprinkler configuration of the partition with index (10, 5) is 2.662. This is a comprehensive, dimensionless evaluation value. The value itself does not have an absolute physical meaning, but is used for relative comparison among all partitions to be processed. The higher the value, the greater the overall urgency and potential benefit of performing the sprinkler operation on that partition. In subsequent steps, the system will sort all partitions to be processed in descending order based on this value to determine the final sprinkler execution order.

[0035] Based on the sprinkler configuration priority values ​​calculated for all unprocessed partitions, the system executes a priority sorting and task unit construction process. First, the location indices of all unprocessed partitions and their corresponding sprinkler configuration priority values ​​are treated as key-value pairs, forming a list to be sorted. The system uses an efficient sorting algorithm, such as heap sort or quick sort, to sort the list in descending order of the sprinkler configuration priority values. After sorting, a partition sequence is obtained in descending order, representing the suggested execution order of the sprinkler operation. Next, the system traverses this sorted partition sequence and generates specific execution instructions for each partition in the sequence, including sprinkler duration instructions and sprinkler flow instructions. The sprinkler flow instructions are generated based on the real-time infiltration rate of the partition, calculated by multiplying the infiltration rate value of the partition by a preset "flow safety factor." This factor is set based on the stability of the slope and historical operating experience, aiming to prevent local liquid accumulation or erosion due to excessively rapid spraying. For example, if the safety factor is set to 0.85, and the infiltration rate of a certain partition is 6.0 L / (m²·h), then its sprinkler flow instruction is set to 5.1 L / (m²·h). The spray duration command, calculated in L / (m²·h), is based on the total water volume required to raise the humidity of a zone to the target value. The target humidity is determined according to the zone's configuration type. For example, the target humidity for "dry zone water replenishment" is 90% of field capacity (e.g., 32%), while the target humidity for "periodic spraying" is 105% of field capacity (e.g., 37%). The system calculates the required total water volume based on the current humidity, target humidity, and zone area. Then, it divides the total water volume by the determined spray flow rate to calculate the spray duration. For example, if 200 liters of water are needed, the flow rate is 5.1 liters. L / (m²·h), with a zone area of ​​100 square meters, the duration is 200 / (5.1*100) hours, approximately 0.39 hours, or 23.4 minutes. For the resting and soaking program instruction, since all currently processed zones are to be sprayed, this instruction is uniformly set to "not execute". Finally, the system combines the sorting order, zone index, spraying configuration priority value, spraying duration instruction, spraying flow instruction, and resting and soaking program instruction into a structured execution task unit. All execution task units corresponding to the zones are arranged in sorting order to form the basic spraying task sequence.

[0036] The steps for obtaining a multidimensional risk factor set are as follows: The system obtains values ​​for pile saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall over the next 24 hours. It then constructs theoretical lower and lower bounds and performs linear normalization on all raw parameters, with slope stability parameters being normalized by inversion. This results in pile saturation factor, precious liquid pool capacity vulnerability factor, emergency pool capacity vulnerability factor, slope instability risk factor, and expected rainfall impact factor. Based on the saturation factor of the heap, the vulnerability factor of the precious liquid pool, the vulnerability factor of the accident pool, and the slope instability risk factor, the values ​​are verified in turn to see if they are within the closed interval. Normalization failures or abnormal missing terms are eliminated, and together with the expected rainfall impact factor, they form a multidimensional risk factor set.

[0037] Specifically, firstly, through the integrated environmental and facility monitoring interface, the system acquires real-time values ​​for pile saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall for the next 24 hours. Pile saturation is obtained by averaging data from a multi-point time-domain reflectometer (TDR) sensor array buried at different depths within the pile, expressed as a percentage. The capacities of the precious liquid pool and emergency pool are obtained using ultrasonic or pressure level gauges installed within the pools, expressed in cubic meters. Slope stability parameters are represented by a safety factor (FOS) calculated from data from multiple fixed inclinometers and surface displacement monitoring points deployed on key slope sections; this is a dimensionless value. The predicted rainfall for the next 24 hours is obtained by calling the API interface of a third-party commercial meteorological service provider, expressed in millimeters. After acquiring the raw parameters, and combining them with the specific design parameters and historical operating limits of the site, theoretical upper and lower bounds are constructed for each parameter. For example, the theoretical range for pile saturation is set to [15%] based on the field water holding capacity and fully saturated water content of the ore. [45%], the designed effective storage capacity of the Guiye Pond is 10,000 cubic meters, so its upper and lower limits are [0, 10,000]. The design minimum allowable value of the slope stability safety factor is 1.2, and the theoretical optimal value is 2.5, so its range is [1.2, 2.5]. The historical extreme value of the rainfall in the next 24 hours is 80 mm, so its range is [0, 80]. Subsequently, the system uses the maximum-minimum normalization method to process all the original parameters. The calculation method is: normalized value = (current value - lower limit of the range) / (upper limit of the range - lower limit of the range). For all parameters except slope stability parameters, the larger the value, the higher the risk. This formula is directly used for positive normalization. For slope stability parameters, the larger the value, the safer and the lower the risk. Therefore, negative normalization is used. The calculation method is: normalized value = 1 - [(current value - lower limit of the range) / (upper limit of the range - lower limit of the range)] [Lower limit of the interval] After this step, all original parameters are converted into dimensionless risk factors between 0 and 1, which are respectively formed as the pile saturation factor, the precious liquid pool capacity vulnerability factor, the accident pool capacity vulnerability factor, the slope instability risk factor and the expected rainfall impact factor.

[0038] Based on the saturation factor, precious liquid pool capacity vulnerability factor, accident pool capacity vulnerability factor, and slope instability risk factor generated in the previous step, the system performs a data validity verification process. The core of this process is to check whether the value of each factor strictly falls within the closed interval [0, 1]. This is a critical quality control step because during the normalization process, if the original sensor data exceeds the preset theoretical lower and lower bounds due to faults or interference—for example, a level gauge malfunctions and returns a negative value, or an inclinometer data anomaly causes the calculated safety factor to be lower than the design lower limit—then the factor value calculated by the normalization formula may be less than 0 or greater than 1. The system will check the values ​​of these four internal risk factors one by one. If the value of a factor, such as the precious liquid pool capacity vulnerability factor, is 1.05, the system determines that the item is a normalization failure item. At the same time, the system will also check whether there are any abnormal missing items due to sensor communication interruptions, etc., that is, their values ​​are specific null value markers, such as NaN (Not a null terminator). For all risk factors identified as normalization failures or anomalously missing items, the system will remove them from the current calculation cycle and immediately generate a high-priority system maintenance alert. The system will record the type, original value, and corresponding sensor source of the anomalous factor in detail and notify the operation and maintenance personnel to investigate and handle it. After the verification and removal operations are completed, all internal risk factors that have passed verification and whose values ​​are in the range of [0, 1], namely the pile saturation factor, the precious liquid pool capacity vulnerability factor, the accident pool capacity vulnerability factor, and the slope instability risk factor, will be integrated with the expected rainfall impact factor that has also been normalized to form a structured data set, which is the multidimensional risk factor set.

[0039] The steps to obtain the environmental risk exposure index are as follows: Based on a multidimensional risk factor set, the environmental risk exposure index is calculated using the following formula: ; in, For environmental risk exposure index, For the first Individual internal risk factor values, For the first The weighting coefficients of each internal risk factor. For the first Risk acceleration index of each internal risk factor The total number of internal risk factors. The normalized rainfall influencing factors are used to predict rainfall in the next 24 hours. This represents the amplification factor for the impact of rainfall.

[0040] Specifically, the formula: The advantage of the formula is that... A partial simulation of the reliability model of a series system was performed, treating each sub-risk item as a potential failure point. Representing the The "survival probability" of each component is calculated by multiplying the survival probabilities of all components together to obtain the "survival probability" of the entire system. Subtracting this value from 1 gives the "failure probability" of the system, i.e., the internal comprehensive risk. This structure can capture the synergistic amplification effect between risks, making it particularly suitable for evaluating complex engineering systems with multiple potential failure modes. Furthermore, by introducing a risk acceleration index... The model can reflect the nonlinear characteristics of different risks, that is, the growth rate of some risks will accelerate sharply as their magnitude increases. Finally, the external rainfall influencing factors are considered. As a multiplier, and an amplification factor is introduced. This characterizes the nonlinear amplification effect of rainfall as an external trigger and exacerbating factor on the internal risks of the entire system, making the risk assessment results closer to real-world scenarios. parameter For the first The internal risk factor values ​​are dimensionless values ​​after normalization, ranging from [0, 1]. These factors are obtained from the multidimensional risk factor set formed in the previous step. In this example, there are a total of 4 internal risk factors (i.e., ...). ), respectively: Represents the pile saturation factor. Represents the vulnerability factor of the precious liquid pool. Represents the vulnerability factor of the accident pool capacity. These represent slope instability risk factors. These factor values ​​are dynamically changing and reflect the current internal safety status of the heap leaching site. For example, at a certain moment, the value obtained from the multidimensional risk factor set might be: heap saturation factor. Vulnerability factors of precious liquid pools Vulnerability factors of accident pool capacity Slope instability risk factors .

[0041] parameter For the first The weight coefficient of each internal risk factor is a dimensionless value representing the relative importance of each internal risk factor to the overall environmental risk. The sum of all weight coefficients is 1. The weights are determined using the Analytic Hierarchy Process (AHP). First, an evaluation group of 3-5 experts in safety engineering, hydrogeology, and mining constructs a pairwise comparison judgment matrix. Experts score the relative importance of each risk factor on a scale of 1-9 based on their experience. For example, if experts unanimously agree that the risk consequences of "slope instability" are far more severe than "the capacity of the precious liquid pool," they would award it 7 points. Then, the largest eigenvalue of the judgment matrix and its corresponding eigenvector are calculated, and the eigenvector is normalized to obtain the weight of each factor. Finally, a consistency check is performed to ensure that the consistency ratio (CR) is less than 0.1, thus guaranteeing the rationality of the judgment logic. For example, after calculation and verification, the weight allocation obtained is as follows: weight of the pile saturation factor. Weighting of vulnerability factor in precious liquid pool capacity Vulnerability factor weight of accident pool capacity Slope instability risk factor weights The sum is 1.0.

[0042] parameter For the first The risk acceleration index of an internal risk factor is a dimensionless index used to describe the non-linear characteristics of risk growth, that is, the degree to which an increase in the value of a risk factor accelerates its contribution to the final risk. This indicates that the risk is accelerating. Indicates linear growth. This indicates a slowdown in risk growth. The numerical value is determined based on statistical analysis and numerical simulation of similar historical accident cases. By analyzing a large amount of data, the evolution curve of each risk factor from normal to critical state is fitted, and its optimal exponential form is determined. For example, through retrospective analysis of historical slope instability cases, it was found that the decrease in the safety factor exponentially accelerates the increase in the instability probability, with a fitted acceleration index of 1.5. In contrast, the risk growth of the precious liquid pool capacity is relatively linear, with an acceleration index close to 1.0. Based on this analysis, the risk acceleration index is set for each factor as follows: [Further details about the risk acceleration index are needed for the context.] Vulnerability factors of precious liquid pools Vulnerability factors of accident pool capacity Slope instability risk factors .

[0043] parameter The total number of internal risk factors is defined in this system as four items: reactor saturation, precious liquid pool capacity, emergency pool capacity, and slope stability. .

[0044] parameter This is the normalized rainfall impact factor for the predicted rainfall over the next 24 hours. It's a dimensionless value directly obtained from a multidimensional risk factor set, ranging from [0, 1]. It quantifies the additional stress that the expected rainfall event may cause to the system. This value is obtained by normalizing the rainfall forecast provided by the meteorological service API through max-min normalization. For example, if the current system's predicted rainfall for the next 24 hours is 30 mm, and the set normalization interval is [0, 80] mm, then... .

[0045] parameter The rainfall amplification factor is a dimensionless empirical coefficient used to adjust for the amplification effect of rainfall on overall risk. Its value is primarily determined based on the hydrogeological conditions of the area where the heap leaching site is located, the catchment area, the drainage system capacity, and the impermeability of the heap structure. It is calibrated through analysis of response data from historical heavy rainfall events. For example, in a site with poor drainage capacity and a large catchment area, a moderate rainfall event may cause a sharp increase in reservoir capacity and heap saturation; in this case, a higher amplification factor should be set. A higher value is set for a lower value, and vice versa. Based on the site's engineering geological report and operational experience, the expert evaluation team set this coefficient to [value missing]. .

[0046] Calculation process: Substitute the specific values ​​of the above parameters into the environmental risk exposure index. In the calculation formula, The values ​​for each parameter are as follows: , , ; , , , ; , , , ; ; ; ; Calculate the internal comprehensive risk component: ; ; ; ; ; ; Calculate the final environmental risk exposure index : ; ; ; ; The results indicate that the current environmental risk exposure index is 0.7075, which is a quantitative risk score that integrates all internal risk factors and takes into account the impact of future rainfall. The index value falls within the range of [0, 1]. The closer the value is to 1, the higher the overall environmental risk faced by the system. This value will serve as the direct basis for risk level judgment. In subsequent steps, the system will compare the value of 0.7075 with the preset "warning line" and "danger line" thresholds to determine the risk response level and corresponding control strategies that the system should adopt at the current time.

[0047] The steps to obtain the system risk response level are as follows: Based on the environmental risk exposure index, preset warning thresholds and danger thresholds are invoked. The environmental risk exposure index is compared with the warning thresholds and danger thresholds one by one to determine the risk level range to which the environmental risk exposure index belongs and generate the system risk response level.

[0048] Specifically, based on the environmental risk exposure index, the system retrieves warning thresholds and danger thresholds from a preset risk management configuration library. These two thresholds are comprehensively formulated based on the hydrogeological report, environmental impact assessment report, and historical operating data of the heap leaching site. The process for setting the warning threshold is as follows: Historical environmental risk exposure indices of all operating days marked as "requiring special attention" but without actual incidents within the past three years are statistically analyzed to form a data sequence. The maximum and minimum values ​​are removed to eliminate interference from extreme and accidental events. Then, the 90th percentile of this sequence is calculated and used as the baseline value for the warning threshold. For example, if the 90th percentile obtained through statistical calculation is 0.58, considering a certain safety redundancy, it is rounded up and the warning threshold is set to 0.60. The danger threshold is set based on numerical simulation of extreme operating conditions. A hydraulic and stability coupling model of the heap leaching site is established, and the "once-in-a-century" level specified in the design standards is input. Other 24-hour rainfall data is used, and internal risk factors are set to their respective worst-case scenarios, such as the pile saturation reaching the critical saturation value and the slope stability safety factor dropping to the design lower limit of 1.2. The environmental risk exposure index calculated under these extreme conditions is simulated, and 95% of the simulation result is used as the danger threshold. For example, if the simulated extreme risk index is 0.89, the danger threshold is set to 0.85. After setting the threshold, the system compares the currently calculated environmental risk exposure index, such as 0.7075, with these two thresholds. The judgment logic is as follows: if the index is less than 0.60, the risk level range is determined to be "normal"; if the index is greater than or equal to 0.60 and less than 0.85, the risk level range is determined to be "alert"; if the index is greater than or equal to 0.85, the risk level range is determined to be "dangerous". Based on this judgment result, the system risk response level is finally generated.

[0049] The steps for obtaining the control instruction program to be executed by the PLC are as follows: Based on the system risk response level, the basic spraying task sequence is called and it is determined whether the system risk response level has reached the warning threshold. If the environmental risk exposure index value has not reached the warning threshold, the basic spraying task sequence remains unchanged. If it has reached or exceeded the warning threshold, the risk avoidance subroutine is called. The overall spraying intensity is reduced by adjusting the values ​​of spraying duration and spraying flow rate in the task sequence item by item, and the risk avoidance adjusted spraying task sequence is generated. Based on the system risk response level and the spraying task sequence adjusted for risk avoidance, it is determined whether the environmental risk exposure index value has reached the danger threshold. If it has reached or exceeded the danger threshold, the emergency limiting subroutine is called to delete and reconstruct the task items in the spraying task sequence adjusted for risk avoidance, and generate a control instruction program for PLC to execute.

[0050] Specifically, based on the system risk response level, the system first calls the complete basic spraying task sequence generated in the preceding steps and executes the decision branch according to the system risk response level. If the system risk response level is "normal," meaning the environmental risk exposure index value is less than the preset warning threshold of 0.60, the system determines that the current operating condition is safe and no intervention is needed on the original production plan. In this case, the risk avoidance subroutine is not activated, and the system directly outputs the original basic spraying task sequence. If the system risk response level is "warning," meaning the environmental risk exposure index value reaches or exceeds the warning threshold of 0.60 but does not reach the danger threshold of 0.85, the system activates the risk avoidance subroutine. The goal of this subroutine is to proactively control the further escalation of risk by reducing the overall infiltration volume. Its core operation is to calculate a "spraying intensity reduction coefficient," which is linearly determined based on the relative position of the current risk index within the warning range. The calculation method is: Reduction coefficient = (Current environmental risk exposure index - Warning threshold) / (Danger threshold - (Warning threshold), for example, if the current index is 0.7075, then the reduction coefficient is (0.7075 - 0.60) / (0.85 - 0.60), which is approximately equal to 0.43. Then, the subroutine multiplies this reduction coefficient by a preset "maximum allowable reduction ratio", which is set by the process engineer according to the ore properties, for example, 50%, to ensure the basic wetting effect. The actual reduction ratio is 0.43 multiplied by 50%, which equals 21.5%. Finally, the subroutine iterates through each task unit in the basic spraying task sequence and multiplies the values ​​of the spraying duration command and spraying flow command by (1 - 21.5%), that is, by 0.785, thereby generating a new sequence in which the spraying intensity of all tasks is proportionally reduced. This sequence is the risk-avoidance adjusted spraying task sequence.

[0051] Based on the system risk response level and the risk-avoidance adjusted spray task sequence, the system performs the final instruction generation judgment. The input for this step defaults to the risk-avoidance adjusted spray task sequence. If the system risk response level is "normal" or "alert," the current environmental risk exposure index value will necessarily be less than the danger threshold of 0.85. In this case, the system determines that there is no need to activate the highest level of emergency response, the emergency reduction subroutine is not called, and the system directly uses the input risk-avoidance adjusted spray task sequence (which is the original basic spray task sequence under the "normal" level) as the final output. If the system risk response level is "dangerous," that is, the environmental risk exposure index value reaches or exceeds the danger threshold of 0.85, the system immediately calls the emergency reduction subroutine to perform more aggressive deletion and reconstruction on the risk-avoidance adjusted spray task sequence. First, the subroutine calculates the "task reduction ratio," which is calculated as: Task reduction ratio = (current environmental risk exposure index - danger threshold) / (1.0 - danger threshold). For example, if the current index is 0.92, then the task reduction ratio is (0.92 - The calculation of (0.85) / (1.0 - 0.85) yields approximately 46.7%. The subroutine then starts from the end of the risk-avoidance adjusted spray task sequence, i.e., the lowest priority task, and removes 46.7% of the total number of tasks. For example, if the original sequence has 30 tasks, the last 14 tasks are removed. Next, the subroutine reconstructs the remaining tasks, forcibly modifying the spray flow instructions of all remaining tasks to a preset "minimum maintenance flow rate". This flow rate value is determined based on the minimum water requirement to prevent the surface of the pile from drying and to maintain the activity of microorganisms, for example, set to 1.5 liters / square meter·hour. At the same time, the spray duration is recalculated to match this minimum flow rate. After the removal and reconstruction, an instruction sequence with a significantly reduced number of tasks and the spray intensity reduced to the minimum maintenance level is obtained. This sequence is the control instruction program for the PLC to execute.

Claims

1. An intelligent control system for heap leaching spraying based on the Internet of Things, characterized in that, The system includes: The IoT partition status sensing module obtains the current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field partitions from IoT terminals, and arranges them according to their spatial location in the heap leaching field to obtain a real-time heap leaching status matrix. The spraying decision program generation module, based on the real-time heap leaching state matrix, scans the leaching time and current humidity of all partitions in the matrix, filters to obtain a list of partitions to be processed, configures rest leaching program instructions for each target partition according to the list of partitions to be processed, sorts them according to execution priority, and generates a basic spraying task sequence. The heap leaching site risk assessment module obtains the heap saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall for the next 24 hours. It then normalizes these parameters to obtain the heap saturation factor, pool capacity vulnerability factor, slope instability probability factor, and expected rainfall impact factor. A multidimensional risk factor set is established, and the environmental risk exposure index is calculated based on this multidimensional risk factor set. The preventive safety control module compares the environmental risk exposure index with the warning line and danger line thresholds to determine the range to which it belongs, obtains the system risk response level, and generates a control instruction program for PLC to execute based on the system risk response level.

2. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the real-time heap immersion state matrix are as follows: The current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field zones are collected from IoT terminals. Based on the spatial location coordinates of the IoT terminals in each zone of the heap leaching field, the collected parameter values ​​are spatially marked to generate a set of zone parameters with spatial location information. Based on the set of partition parameters with spatial location information, the horizontal and vertical coordinates of the IoT terminals in each partition of the heap leaching field are sorted in two dimensions row by row and column by column to determine the relative position index of each partition in the heap leaching field. The position index is then bound to the corresponding partition parameter value to generate a spatial sorting sequence of partition parameters. Based on the spatial sorting sequence of the partition parameters, the current humidity, permeation rate, ore grade, leaching time and rest leaching state parameters corresponding to each partition are sequentially filled into the array cells corresponding to the position index using a two-dimensional array structure. The filling is completed in an M×N spatial layout to obtain the real-time heap leaching state matrix.

3. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the list of partitions to be processed are as follows: Based on the real-time heap leaching state matrix, the leaching time values ​​of all heap leaching field partitions in the matrix are scanned sequentially. The leaching time values ​​of all heap leaching field partitions are compared, and the heap leaching field partition with the longest leaching time value in the matrix is ​​determined, generating a set of heap leaching field partitions with the longest leaching time value. Based on the set of heap leaching field partitions with the longest leaching time, the current humidity value of the corresponding heap leaching field partition in the real-time heap leaching state matrix is ​​called and compared with the preset dry area threshold value one by one. Heap leaching field partitions with current humidity values ​​less than the dry area threshold are selected to form a set of heap leaching field partitions whose humidity meets the dry area condition. Based on the real-time heap leaching state matrix, the current humidity value and rest leaching duration value of all heap leaching field partitions are called one by one. The current humidity value is compared with the preset saturation threshold, and the rest leaching duration value is compared with the preset minimum rest leaching duration. Heap leaching field partitions whose current humidity value reaches the saturation threshold and whose rest leaching duration value exceeds the minimum rest leaching duration are selected to form a set of heap leaching field partitions with saturated humidity and rest leaching duration that meet the conditions. Based on the set of heap leaching zone partitions that meet the dry zone conditions and the set of heap leaching zone partitions that meet the conditions of humidity saturation and rest leaching duration, a list of partitions to be processed is formed.

4. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the basic spraying task sequence are as follows: Based on the list of partitions to be processed, the ore grade, leaching time, permeation rate and rest leaching duration values ​​corresponding to each heap leaching field partition are extracted one by one. By setting a spray type judgment process, it is identified whether the spray duration configuration conditions, spray flow configuration conditions or rest leaching program configuration conditions are met, and a set of spray configuration instructions to be evaluated is generated based on the judgment results. Based on the set of spray configuration instructions to be evaluated, calculate the spray configuration priority value for each heap leaching zone; Based on the spray configuration priority value, all heap leaching zone partitions are prioritized in descending order. The ranking result is combined with the spray duration instruction, spray flow instruction and rest leaching procedure instruction of each partition to form an execution task unit, thus forming a basic spray task sequence.

5. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the multidimensional risk factor set are as follows: The system obtains values ​​for pile saturation, precious liquid pool capacity, emergency pool capacity, slope stability parameters, and predicted rainfall over the next 24 hours. It then constructs theoretical lower and lower bounds and performs linear normalization on all raw parameters, with slope stability parameters being normalized by inversion. This results in pile saturation factor, precious liquid pool capacity vulnerability factor, emergency pool capacity vulnerability factor, slope instability risk factor, and expected rainfall impact factor. Based on the saturation factor of the heap, the vulnerability factor of the precious liquid pool, the vulnerability factor of the accident pool, and the slope instability risk factor, the values ​​are verified in turn to see if they are within the closed interval. Normalization failures or abnormal missing terms are eliminated, and together with the expected rainfall impact factor, they form a multidimensional risk factor set.

6. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the environmental risk exposure index are as follows: Based on the aforementioned multidimensional risk factor set, the environmental risk exposure index is calculated.

7. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the system risk response level are as follows: Based on the environmental risk exposure index, preset warning threshold and danger threshold are invoked, and the environmental risk exposure index is compared with the warning threshold and danger threshold one by one to determine the risk level range to which the environmental risk exposure index belongs, and the system risk response level is generated.

8. The IoT-based intelligent control system for heap leaching spraying according to claim 1, characterized in that, The steps for obtaining the control instruction program to be executed by the PLC are as follows: Based on the system risk response level, the basic spraying task sequence is invoked and it is determined whether the system risk response level has reached the warning threshold. If the environmental risk exposure index value has not reached the warning threshold, the basic spraying task sequence remains unchanged. If it has reached or exceeded the warning threshold, the risk avoidance subroutine is invoked to reduce the overall spraying intensity by adjusting the spraying duration and spraying flow rate values ​​in the task sequence item by item, thereby generating a risk-avoidance adjusted spraying task sequence. Based on the system risk response level and the spray task sequence adjusted for risk avoidance, it is determined whether the environmental risk exposure index value has reached the danger threshold. If it has reached or exceeded the danger threshold, the emergency limiting subroutine is called to delete and reconstruct the task items in the spray task sequence adjusted for risk avoidance, and generate a control instruction program for PLC to execute.

9. The intelligent control method for heap leaching spraying in an IoT-based intelligent control system according to any one of claims 1-8, characterized in that, Includes the following steps: The current humidity, permeation rate, ore grade, leaching time and resting leaching status parameters of M×N heap leaching field zones are obtained from the Internet of Things terminal and arranged and combined according to their spatial location in the heap leaching field to obtain a real-time heap leaching status matrix. Based on the real-time heap leaching state matrix, the leaching time and current humidity of all partitions in the matrix are scanned to obtain a list of partitions to be processed. Based on the list of partitions to be processed, rest leaching program instructions are configured for each target partition and sorted according to execution priority to generate a basic spraying task sequence. The saturation of the heap, the capacity of the precious liquid pool, the capacity of the emergency pool, the slope stability parameters, and the predicted rainfall for the next 24 hours are obtained and normalized to obtain the heap saturation factor, the capacity vulnerability factor, the slope instability probability factor, and the expected rainfall impact factor. A multidimensional risk factor set is established, and the environmental risk exposure index is calculated based on the multidimensional risk factor set. The environmental risk exposure index is compared with the warning line and danger line thresholds to determine the range to which it belongs, and the system risk response level is obtained. Based on the system risk response level, a control instruction program for PLC execution is generated.

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