Remediation control methods, systems, media, and computer program products for soil pollution

Abstract

The present disclosure provides a soil pollution remediation control method, system, medium and computer program product, the method comprising: obtaining initial soil pollution data of an actual contaminated site to obtain a corresponding target remediation scheme; the target remediation scheme comprises a plurality of sub-remediation schemes, each sub-remediation scheme comprising at least one remediation technology; obtaining a target mapping model matched with the sub-remediation scheme; generating a remediation control instruction corresponding to the sub-remediation scheme based on the target mapping model, and determining a matched remediation device; issuing the remediation control instruction to the matched remediation device to perform corresponding remediation operations. The present disclosure can remotely control the on-site device based on the platform, and can also dynamically monitor the soil changes and device states during the remediation process in real time, so as to timely and adaptively make targeted remediation adjustments to ensure the accuracy and timeliness of the remediation control, improve the remediation quality and efficiency of the soil, and achieve the effect of intelligent operation construction.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of pollution treatment, in particular to a soil pollution remediation control method, system, medium and computer program product. BACKGROUND

[0002] For soil pollution scenarios, the current remediation scheme generally has low processing efficiency, is not timely or accurate, or causes secondary pollution, and cannot meet the higher requirements of remediation. SUMMARY

[0003] The technical problem to be solved by the present disclosure is to overcome the above-mentioned defects in the prior art, and to provide a soil pollution remediation control method, system, medium and computer program product.

[0004] The present disclosure solves the above technical problems by the following technical solutions:

[0005] The present disclosure provides a soil pollution remediation control method, which is applied in a pollution site remediation decision platform, wherein a mapping model library is constructed in the pollution site remediation decision platform, the mapping model library includes a mapping model corresponding to each remediation device in an actual pollution plot, and the mapping model is in communication connection with the matched remediation device.

[0006] The remediation control method includes:

[0007] Obtaining initial soil pollution data of an actual pollution plot, and obtaining a corresponding target remediation scheme based on the initial pollution data;

[0008] The target remediation scheme includes a plurality of sub-remediation schemes, and each sub-remediation scheme includes at least one remediation technology.

[0009] Obtaining a target mapping model in the mapping model library matched with the sub-remediation scheme;

[0010] Generating a remediation control instruction corresponding to the sub-remediation scheme based on the target mapping model, and determining the matched remediation device;

[0011] The remediation control instruction is issued to the matched remediation device to perform corresponding remediation operations.

[0012] Preferably, the remediation control method further includes:

[0013] Obtaining actual state data corresponding to the actual pollution plot during remediation;

[0014] Generating a remediation adjustment scheme for the remediation device corresponding to the actual state data;

[0015] Based on the repair adjustment scheme, a corresponding correction adjustment instruction is generated by using the matched target mapping model, and is issued to the corresponding repair device to adjust the corresponding repair operation.

[0016] Preferably, the actual state data includes at least one of the following:

[0017] Actual operation data fed back by the repair device during the repair process, actual soil pollution data of the actual contaminated land during the repair process, and actual geological change data;

[0018] And / or,

[0019] A preset feedback control strategy is used to generate the repair adjustment scheme of the repair device according to the actual state data;

[0020] Different repair devices correspond to different preset feedback control strategies.

[0021] Preferably, before the step of obtaining the corresponding target repair scheme based on the initial pollution data, the method further comprises:

[0022] Obtaining a plurality of groups of sample data, each group of sample data including sample pollution data and a corresponding labeled sample repair scheme;

[0023] Training a preset network model based on a plurality of groups of sample data to obtain a target repair scheme generation model;

[0024] The step of obtaining the corresponding target repair scheme based on the initial pollution data comprises:

[0025] Inputting the initial pollution data into the repair scheme generation model to output the corresponding target repair scheme.

[0026] Preferably, the step of training a preset model based on a plurality of groups of sample data to obtain a target repair scheme generation model comprises:

[0027] Based on a plurality of groups of sample data, the preset network model is pre-trained to obtain an intermediate repair scheme generation model;

[0028] In response to the intermediate repair scheme generation model not meeting the training condition, iterative training is performed until the target repair scheme generation model is obtained.

[0029] Preferably, after the step of issuing the repair control instruction to the matched repair device to perform the corresponding repair operation, the method further comprises:

[0030] Collecting actual operation data of the repair device in several dimensions during the repair process;

[0031] monitoring a repair progress of the repair device based on the actual operation data; and / or, judging whether an abnormal condition occurs, and if so, generating a stop control instruction to control the corresponding repair device to stop the repair operation;

[0032] and / or,

[0033] The repair device includes a multi-medium mixed transport device, a targeted drilling device, a solubility extraction device, or a fracturing injection device.

[0034] The present disclosure also provides a soil pollution repair control system applied in a contaminated site repair decision platform, wherein a mapping model library is constructed in the contaminated site repair decision platform, the mapping model library includes a target mapping model corresponding to each repair device in an actual contaminated plot, and the mapping model is in communication connection with the matched repair device.

[0035] The repair control system includes:

[0036] An initial pollution data acquisition module is configured to acquire initial soil pollution data of an actual contaminated plot.

[0037] A target repair scheme acquisition module is configured to obtain a corresponding target repair scheme based on the initial pollution data.

[0038] The target repair scheme includes a plurality of sub-repair schemes, and each sub-repair scheme includes at least one repair technology.

[0039] A target model acquisition module is configured to acquire a target mapping model in the mapping model library that matches the sub-repair scheme.

[0040] A matching module is configured to generate a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determine the matched repair device.

[0041] A repair control module is configured to issue the repair control instruction to the matched repair device to perform the corresponding repair operation.

[0042] Preferably, the repair control system further includes:

[0043] An actual state data acquisition module is configured to acquire actual state data of the actual contaminated plot during the repair process.

[0044] A repair adjustment scheme generation module is configured to generate a repair adjustment scheme of the corresponding repair device according to the actual state data.

[0045] The repair adjustment module is configured to generate corresponding correction adjustment instructions based on the repair adjustment scheme and the matched target mapping model, and deliver the correction adjustment instructions to the corresponding repair device to adjust the corresponding repair operation.

[0046] Preferably, the actual state data comprises at least one of the following:

[0047] Actual operation data fed back by the repair device during the repair process, actual soil pollution data of the actual contaminated land during the repair process, and actual geological change data.

[0048] And / or,

[0049] The repair adjustment scheme generation module is further configured to generate the repair adjustment scheme of the corresponding repair device based on the actual state data and a preset feedback control strategy.

[0050] Different repair devices correspond to different preset feedback control strategies.

[0051] Preferably, the repair control system further comprises:

[0052] A sample data acquisition module is configured to acquire a plurality of groups of sample data, each group of sample data comprising sample pollution data and a corresponding labeled sample repair scheme.

[0053] A model training module is configured to train a preset network model based on the plurality of groups of sample data to obtain a target repair scheme generation model.

[0054] The target repair scheme acquisition module is configured to input the initial pollution data into the repair scheme generation model to output the corresponding target repair scheme.

[0055] Preferably, the model training module is configured to pre-train the preset network model based on the plurality of groups of sample data to obtain an intermediate repair scheme generation model.

[0056] In response to the intermediate repair scheme generation model not meeting a training condition, iteratively training until the target repair scheme generation model is obtained.

[0057] Preferably, the repair control system further comprises:

[0058] An operation data acquisition module is configured to acquire actual operation data of the repair device in a plurality of dimensions during the repair process.

[0059] a progress monitoring module configured to monitor a progress of the remediation of the remediation device based on the actual operation data; and / or, an abnormality judging module configured to judge whether an abnormality occurs, and if so, generate a stop control instruction to control the corresponding remediation device to stop the remediation operation; and / or,

[0060] The remediation device includes a multi-medium mixed transport device, a targeted drilling device, a solubilization extraction device, or a fracturing injection device.

[0061] The present disclosure also provides a contaminated site remediation decision platform, which comprises the soil pollution remediation control system described above.

[0062] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of the method described above.

[0063] The present disclosure also provides a computer program product comprising a computer program, the computer program being executed by a processor to implement the steps of the method described above.

[0064] On the basis of common sense in the art, the various preferred conditions can be combined arbitrarily, i.e., to obtain various preferred embodiments of the present disclosure.

[0065] The positive progress effect of the present disclosure is that:

[0066] In the present disclosure, the contaminated site remediation decision platform can communicate and interact with different remediation devices in actual contaminated plots, the remediation control scheme is run in the contaminated site remediation decision platform, for any actual contaminated plot, once it reaches a state that needs to be remediated, a corresponding sub-remediation scheme is automatically generated based on all the pollution data of the actual contaminated plot according to the respective functional characteristics of different remediation devices, then a target mapping model in the mapping model library is matched based on the sub-remediation scheme, and the target mapping model is used to output a corresponding remediation control instruction, which is then issued to the matched remediation device, the remediation device performs the corresponding remediation action after receiving the remediation instruction, thereby achieving the purpose of remote remediation control of the on-site device based on the platform, ensuring the accuracy and timeliness of remediation control, improving the remediation quality and efficiency of the soil, and achieving the effect of intelligent operation construction.

[0067] In addition, when different remediation devices are used to remediate actual contaminated plots, the soil changes and device states in the actual contaminated plots are dynamically monitored in real time, and targeted remediation adjustments are made in a timely and adaptive manner to achieve precise remediation, reduce remediation costs and the possibility of secondary pollution, and dynamically adjust each remediation action in real time, thereby further ensuring the accuracy and timeliness of remediation control, improving the remediation quality and efficiency of the soil, and improving the effect of operation construction. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 Flow chart of the remediation control method for soil pollution according to Embodiment 1 of the present disclosure.

[0069] Figure 2 Flow chart of the remediation control method for soil pollution according to Embodiment 2 of the present disclosure.

[0070] Figure 3 Overall schematic diagram of the remediation control for soil pollution according to Embodiment 2 of the present disclosure.

[0071] Figure 4 Module schematic diagram of the remediation control system for soil pollution according to Embodiment 3 of the present disclosure.

[0072] Figure 5 Module schematic diagram of the remediation control system for soil pollution according to Embodiment 4 of the present disclosure.

[0073] Figure 6 Module schematic diagram of the contaminated site remediation decision platform according to Embodiment 5 of the present disclosure.

[0074] Figure 7 Structure schematic diagram of the electronic device according to Embodiment 6 of the present disclosure. DETAILED DESCRIPTION

[0075] The present disclosure will be further illustrated by way of embodiments below, but the present disclosure is not limited in the scope of the embodiments.

[0076] Embodiment 1

[0077] The remediation control method for soil pollution according to the present embodiment is applied in a contaminated site remediation decision platform, which is configured with a mapping model library including a mapping model corresponding to each remediation device in an actual contaminated site, and the mapping model is in communication connection with the matched remediation device.

[0078] The different remediation devices include a multi-medium mixed transport device, a targeted drilling device, a solubility-enhanced extraction device, or a fracturing injection device, etc., and the contaminated site remediation decision platform is configured with a corresponding multi-medium mixed transport module, a targeted drilling module, a solubility-enhanced extraction module, or a fracturing injection module, etc., which are in communication connection with the corresponding remediation devices on site through the device control system in the platform.

[0079] The mapping model is constructed based on the remediation technology, the remediation control instruction, and the corresponding remediation device of each group.

[0080] As shown in FIG. 1, the remediation control method according to the present embodiment includes: Figure 1

[0081] ​S101, obtain initial soil pollution data of the actual contaminated land block, and obtain a corresponding target remediation scheme based on the initial pollution data;

[0082] The target remediation scheme includes a plurality of sub-remediation schemes, and each sub-remediation scheme includes at least one remediation technology.

[0083] Specifically, various types of data collection equipment arranged in the actual contaminated land block are used to collect soil state characterization parameters in multiple dimensions at any time or time period, which are used as initial soil pollution data.

[0084] Specifically, the decision-making algorithm (deep learning method) integrated in the remediation decision-making platform is used to automatically determine the remediation scheme matched with the initial soil pollution data; different remediation devices correspond to different remediation processing functions, and when the pollution state of the actual contaminated land block reaches a certain degree, the sub-remediation scheme corresponding to each remediation device is automatically generated based on the collected initial soil pollution data. Each sub-remediation scheme can involve only one set of remediation technology, or can include a combination of multiple remediation technologies, which is determined according to the actual situation.

[0085] S102, obtain a target mapping model matched with the sub-remediation scheme in the mapping model library;

[0086] S103, generate a remediation control instruction corresponding to the sub-remediation scheme based on the target mapping model, and determine a matched remediation device;

[0087] S104, issue the remediation control instruction to the matched remediation device to perform the corresponding remediation operation.

[0088] Different remediation devices include multi-medium mixed transport equipment, targeted drilling equipment, solubility extraction equipment, or fracturing injection equipment, etc. The platform communicates with these remediation devices in real time to realize the functions of collecting on-site data, issuing instructions for remote control, etc.

[0089] In the scheme, the contaminated site remediation decision platform can communicate and interact with different remediation equipment in the actual contaminated plot. The remediation control scheme runs in the contaminated site remediation decision platform. For any actual contaminated plot, once it reaches the state that needs to be repaired, a corresponding sub-repair scheme is automatically generated based on the respective functional characteristics of different repair equipment and all the pollution data corresponding to the actual contaminated plot. Then, the target mapping model in the mapping model library is matched based on the sub-repair scheme, and the target mapping model is used to output the corresponding repair control instruction. The repair control instruction is then issued to the matched repair equipment. After receiving the repair instruction, the repair equipment performs the corresponding repair action, thereby achieving the purpose of remote repair control of the on-site equipment based on the platform, ensuring the accuracy and timeliness of the repair control, improving the repair quality and efficiency of the soil, and achieving the effect of intelligent operation construction.

[0090] Embodiment 2

[0091] The repair control method of the present embodiment is a further improvement of Embodiment 1, specifically:

[0092] In an implementable scheme, as shown in Figure 2 the step S104 further includes:

[0093] S105, acquiring actual state data of the actual contaminated plot in the repair process;

[0094] S106, generating a repair adjustment scheme for the corresponding repair equipment according to the actual state data;

[0095] S107, generating a correction adjustment instruction using a matched target mapping model based on the repair adjustment scheme, and issuing the correction adjustment instruction to the corresponding repair equipment to adjust the corresponding repair operation.

[0096] In the scheme, when different repair equipment is used to repair the actual contaminated plot, the soil changes, equipment state, etc. in the actual contaminated plot are dynamically monitored in real time, and targeted repair adjustment is made in a timely and adaptive manner to achieve accurate repair, reduce repair costs and the possibility of secondary pollution, and dynamically adjust each repair action in real time, thereby further ensuring the accuracy and timeliness of the repair control, improving the repair quality and efficiency of the soil, and improving the effect of operation construction.

[0097] In an implementable scheme, the actual state data includes at least one of the following:

[0098] actual operation data fed back by the repair equipment during the repair process, actual soil pollution data of the actual contaminated plot during the repair process, and actual geological change data.

[0099] In the scheme, it can be considered that whether the execution action of the repair equipment is in place, whether an abnormality occurs, etc. is monitored during the repair process, so as to determine whether corresponding repair adjustment is needed;

[0100] It can also be considered that the change of the detection parameters of different dimensions in the soil in the site, including data representing the pollution situation and data representing the change of geology, etc. is monitored during the repair process, so as to determine whether corresponding repair adjustment is needed;

[0101] Of course, other influence parameters can also be considered according to actual needs, so as to effectively ensure accurate repair control.

[0102] In an implementable scheme, step S106 comprises:

[0103] A repair adjustment scheme of the repair equipment is generated according to the actual state data by using a preset feedback control strategy;

[0104] Different repair equipment corresponds to different preset feedback control strategies.

[0105] In the scheme, the feedback control schemes corresponding to different repair equipment are determined in advance, so that the adjustment scheme with accurate degree is automatically generated according to the actual state data of the site by using the corresponding scheme during the repair process, so as to achieve the effect of accurate repair.

[0106] In an implementable scheme, before the step of obtaining the corresponding target repair scheme based on the initial pollution data, further comprising:

[0107] Obtain a plurality of groups of sample data, each group of sample data comprising sample pollution data and a corresponding labeled sample repair scheme;

[0108] Based on a plurality of groups of sample data, a preset network model is trained to obtain a target repair scheme generation model;

[0109] The step of obtaining the corresponding target repair scheme based on the initial pollution data comprises:

[0110] The initial pollution data is input into the repair scheme generation model to output the corresponding target repair scheme.

[0111] In an implementable scheme, the step of training the preset model based on a plurality of groups of sample data to obtain a target repair scheme generation model comprises:

[0112] Based on a plurality of groups of sample data, a preset network model is pre-trained to obtain an intermediate repair scheme generation model;

[0113] In response to the intermediate repair scheme generation model not meeting the training condition, iterative training is performed until the target repair scheme generation model is obtained.

[0114] In this scheme, a plurality of sets of sample data are obtained based on a sample database, and then a preset network model is pre-trained and iteratively trained; preferably, the network model can be trained while the samples are labeled, so as to quickly realize the training model, so that the model precision meets the training conditions, precise prediction is realized, and the accuracy and timeliness of subsequent repair control are ensured.

[0115] In addition, the repair control method further comprises:

[0116] Based on the pre-set repair scheme decomposition rules corresponding to different repair devices, the sub-repair scheme is decomposed into a plurality of sub-repair units.

[0117] The repair scheme decomposition rules can also be adaptively adjusted according to actual scene requirements.

[0118] Each sub-repair unit is converted into a corresponding first control instruction by using a target mapping model, and is issued to the corresponding repair device through a communication interface.

[0119] In this scheme, the sub-repair scheme decomposition rules corresponding to different repair devices are determined in advance, and then the entire sub-repair scheme is converted into executable processes, and these executable processes are further subdivided into a plurality of sub-repair schemes, and then these sub-repair schemes are converted into corresponding control instructions and issued to the repair device. The repair device will execute the corresponding repair action in sequence according to the different control instructions received in sequence, so as to realize fine and accurate control of remote repair, and ensure the repair quality and efficiency of the soil.

[0120] In an implementable scheme, after step S104, the method further comprises:

[0121] Collecting actual operation data of the repair device in the repair process in a plurality of dimensions;

[0122] Monitoring the repair progress of the repair device based on the actual operation data; and / or, determining whether an abnormal condition occurs, if so, generating a stop control instruction to control the corresponding repair device to stop the repair operation.

[0123] In this scheme, the actual operation data of the repair device in the actual scene can be monitored in real time by using a sensor, so as to determine the current repair progress. If the current task has been completed, the next task is controlled to be executed; if the current task has not been completed, the current task is controlled to be continuously executed.

[0124] The actual operation data of the repair device can also be used to determine whether there is a fault or other abnormality. If there is, the repair is stopped in time to ensure the operation safety of the device and avoid unnecessary dangerous situations.

[0125] In one feasible approach, the repair equipment includes multi-media mixing equipment, targeted drilling equipment, solubilization extraction equipment, or fracturing injection equipment.

[0126] like Figure 3 As shown, the remediation of contaminated sites mainly includes data collection, development of sub-remediation plans, remediation construction plans, and dynamic assessment. Once the corresponding remediation control instructions are generated and issued based on the sub-remediation plans, different remediation equipment is used to coordinate remediation command and control, so as to achieve synchronous remediation construction of the actual contaminated site using different remediation equipment.

[0127] In this solution, remediation equipment such as multi-media mixed transport equipment, targeted drilling equipment, solubilization extraction equipment, and fracturing injection equipment communicate with the contaminated site remediation decision platform through protocols such as MQTT (Message Queuing Telemetry Transport). The equipment receives instructions from the platform and reports real-time parameters from different dimensions on-site to the platform to ensure the operability of remote remediation control. Based on the platform, comprehensive and timely monitoring, control, and management are achieved.

[0128] The implementation principle of the repair control method in this embodiment will be explained below with reference to specific repair equipment:

[0129] In this embodiment, the remediation equipment for contaminated sites includes multi-media mixed-transport equipment, targeted drilling equipment, solubilization extraction equipment, or fracturing injection equipment, etc. The contaminated site remediation decision platform is configured with matching multi-media mixed-transport modules, targeted drilling modules, solubilization extraction modules, or fracturing injection modules, etc., specifically:

[0130] I. For multi-media mixed transmission modules

[0131] (1) The multi-media mixed transport module can collect data related to the multi-media mixed transport process through sensors pre-deployed on site, including the characteristics of different media, the degree of soil pollution, and the mixed transport target;

[0132] (2) By combining the collected data with relevant environmental standards and regulations, and using association rules established through deep learning algorithms and industry expert scoring models, corresponding sub-remediation schemes are determined, such as the concentration of remediation agents in each borehole, the injection volume, and the injection sequence. Specifically: the execution sequence of the multi-media mixed transport equipment is generated, the dosage and ratio of each agent are generated, and the injection sequence and transmission volume are generated, etc.

[0133] (3) Based on the medium type such as fracturing fluid, oxidizing agent, reducing agent, etc., the sub-repair scheme is decomposed into multiple sub-repair schemes, each sub-repair scheme includes the stirring times of the corresponding medium, the amount of medicament for one stirring, the amount of water for one stirring, etc., and these sub-repair schemes are converted into corresponding repair instructions and sent to the device control module based on the MQTT protocol. The repair instructions are sent to the field multi-medium mixed transportation equipment to execute the corresponding repair operation.

[0134] (4) During the repair process, the actual operation data of the multi-medium mixed transportation equipment, soil state data, geological change data, etc. are collected and reported to the platform through the device control module, and then the received data is cleaned and processed, analyzed, etc. in the platform. If it is determined that the current task has not been completed, the control continues to execute the current task, otherwise the next task is executed, such as executing the fracturing task and preparing to open the delivery valve.

[0135] Among them, the actual operation data of the multi-medium mixed transportation equipment includes: fracturing fluid stirring tank data, fracturing fluid storage tank data, module running basic situation data, oxidizing agent stirring tank data, oxidizing agent storage tank data, valve closing situation data, reducing agent stirring tank data, reducing agent storage tank data, signal connection situation data, etc.

[0136] Specifically, the platform uses supervised learning and reinforcement learning, etc. to build a liquid target mapping model, which is located in the device control module. Through communication with the interface of the multi-medium mixed transportation equipment, the output result of the model is transmitted to the multi-medium mixed transportation equipment to control its execution of repair operations, including the addition, mixing and stirring of liquids, etc.

[0137] During the liquid injection process, sensors are used to detect key parameters such as liquid flow, liquid level, pressure, etc. These sensors can feed the collected data back to the platform in real time; by comparing the difference between the actual parameter value and the target parameter value, the platform can determine whether the injection process is normal. If there is a difference between the actual parameter value and the target parameter value, the platform will automatically adjust the parameters of the liquid injection multi-medium mixed transportation module according to the direction and size of the difference. For example, if the flow is too high, the flow can be reduced or the injection stopped; if the flow is too low, the flow can be increased or the injection speed adjusted to achieve precise control of the multi-medium mixed transportation equipment, thereby achieving the effect of precise repair.

[0138] II. For the targeted drilling module

[0139] (1) Real-time acquisition of various data in the drilling process through sensors and data acquisition systems, such as geological structure, drill bit position, permeability coefficient, hydraulic conductivity, water level conductivity, pressure conductivity, specific yield, etc., and transmission of these data to the targeted drilling module of the platform; using deep learning algorithm, combining target position and drilling parameters, etc., through historical data and geological information, according to user demand, etc., to generate corresponding sub-repair scheme, optimize drilling path planning, generate the best drilling path, to realize accurate targeted drilling, to realize intelligent control and management of drilling module, to realize accurate targeted drilling operation;

[0140] (2) After determining that the on-site targeted drilling equipment is turned on, generate corresponding repair instructions based on the sub-repair scheme, and send them to the device control module based on the MQTT protocol, then send the repair instructions to the corresponding targeted drilling module to execute the corresponding repair operation;

[0141] (3) During the repair process, collect the actual running data of the targeted drilling equipment, soil state data, geological change data, etc., and report them to the platform through the device control module, then clean and process the received data, and analyze them in the platform; if it is determined that the current task has not been completed, control continues to execute the current task, otherwise control executes the next task;

[0142] Among them, the actual running data of the targeted drilling equipment includes: device running basic data, signal connection data, drilling rig running data, drilling depth data, etc.

[0143] (4) According to geological changes, drill bit state, etc., generate repair adjustment scheme to automatically adjust drilling parameters and path, real-time adjust direction, inclination and torque, etc.

[0144] Specifically, the targeted drilling module is mainly used for data acquisition, analysis, construction of machine learning and deep learning model, path planning and optimization, real-time adjustment and control, etc.

[0145] The data acquisition function is mainly through the sensors equipped in the targeted drilling module to collect real-time data, including inertial navigation system, gyroscope, accelerometer, magnetometer, etc., to measure the position, direction and motion state of the module, etc. In addition, it can also include geological sensors and temperature sensors, etc. to monitor geological conditions and geological structure, permeability coefficient, hydraulic conductivity, water level conductivity, pressure conductivity, specific yield, etc.

[0146] The data analysis function is to transmit the collected real-time running data to the module control system of the platform for processing and analysis. This function mainly involves through data processing algorithms and technologies, such as filtering, feature extraction and data interpolation, etc., to extract useful information for decision making;

[0147] The platform has embedded machine learning and deep learning models, and stores a large amount of drilling-related data internally. The trained models are used to learn and predict the best drilling path, adjust parameters, etc. Historical data and geological information are used to build models that continuously adjust weights and parameters through backpropagation and optimization algorithms, improving system prediction accuracy and decision-making capabilities. Path planning and optimization can generate the best drilling path based on geological structures, target locations, and user requirements, etc. The path planning algorithm combines machine learning, optimization algorithms, and rule engines to achieve precise targeted drilling.

[0148] In addition, real-time adjustments and controls are made based on real-time data and feedback information, automatically adjusting drilling parameters and paths to ensure drilling accuracy and stability. For example, based on geological changes, the system can automatically control the module to adjust drill bit direction, inclination, and torque parameters.

[0149] Three, for the solubilization extraction module

[0150] (1) The solubilization extraction module first analyzes and predicts the data in the contaminated soil, including the characteristics of the soil pollutants and the data of the soil parameters, such as soil pH, water content, and organic matter content, to establish a model to determine the appropriate remediation scheme, such as determining the execution order of the solubilization extraction well and the vacuum requirement;

[0151] (2) Based on the vacuum, water supply range, negative pressure range, liquid level range, and delay requirement, the sub-repair scheme is decomposed to obtain multiple sub-repair schemes, and these sub-repair schemes are converted into corresponding repair instructions and sent to the device control module based on the MQTT protocol. The following repair instructions are sent to the corresponding solubilization extraction equipment to perform the corresponding repair operation;

[0152] (3) During the repair process, the actual operation data of the solubilization extraction equipment, soil state data, and geological change data are collected and reported to the platform through the device control module, and then the received data are cleaned, processed, and analyzed in the platform. If the current task is not completed, the control continues to execute the current task, otherwise the next task is executed;

[0153] Among them, the actual operation data of the solubilization extraction equipment includes: basic data of equipment operation, signal connection data, water quality monitoring, liquid separation, etc.

[0154] (4) According to the real-time soil parameters, sensor collected module state and fault information, soil parameter conditions, pollutant concentration, etc., through PID (proportional-integral-derivative) control algorithm, model predictive control (MPC) and the like, automatically analyze these data, and adjust the operation and parameter settings of the module as needed to adjust the operating conditions of the module. For example, the stirring speed, temperature and treatment time can be automatically adjusted according to the concentration of pollutants in the soil and the solubilization effect to optimize the effect of solubilization extraction.

[0155] IV. For the fracturing injection module

[0156] (1) In the process of in-situ remediation of contaminated soil, fracturing can increase the transmission capacity of liquid. By applying pressure, the remediation agent can be better delivered to the contaminated area, improving the remediation effect. By fracturing the soil, the soil pore structure can be changed, promoting the contact and reaction between pollutants and remediation agents, and accelerating the remediation speed. Fracturing can create or enlarge these seepage channels, improve the permeability of the soil, increase the distribution range of the remediation agent and speed up the remediation process;

[0157] For the fracturing injection module corresponding to the fracturing injection module, the main function is to control the size and position of the pressure of the fracturing injection module in the fracturing process, to carry out precise remediation of contaminated soil, reduce remediation cost and remediation time;

[0158] The fracturing injection module is mainly used to clean, denoise, detect outliers and normalize the data of key parameters such as pressure, position, flow rate, temperature, etc. collected by the sensor to ensure the quality and consistency of the data; and combined with relevant variance analysis, information gain and recursive feature elimination methods and SVM (Support Vector Machine), clustering algorithm and other processing methods to extract and analyze the data features related to the fracturing of the formation. Then by analyzing the formation data, historical fracturing data and modeling based on the formation and fracturing process, a fracturing plan is developed, i.e. using artificial intelligence and machine learning algorithms to precisely control the fracturing injection of the fracturing injection module;

[0159] (2) The corresponding sub-remediation scheme is decomposed into multiple sub-remediation schemes based on fracturing depth, injection sequence, setting pressure, liquid injection requirements, gas injection requirements, etc. and converted into corresponding remediation instructions, which are then sent to the device control module based on the MQTT protocol. The remediation instructions are sent to the corresponding solubilization extraction module to execute the corresponding remediation operation;

[0160] (3) During the remediation process, the actual operation data of the solubilization extraction module, soil state data, geological change data, etc. are collected and reported to the platform through the device control module. Then the received data is cleaned, processed and analyzed in the platform; if it is determined that the current task has not been completed, the control continues to execute the current task, otherwise the next task is executed.

[0161] The actual operation data of the fracturing injection module include device operation basic data, signal connection data, fracturing injection data, seat sealing data, etc.

[0162] (4) During the fracturing process, real-time monitoring data are quickly processed and analyzed, key features and indicators are extracted, including formation physical property parameters, pressure sensor data, fracture propagation information, and a feedback control strategy is used for pressure and size adjustment. For example, a PID control algorithm is used to adjust the control parameters according to the real-time pressure and position errors, generate feedback signals and control instructions, and automatically control the module to adjust the injection rate and pressure of the liquid or gas to achieve the appropriate pressure size;

[0163] Specifically, the fracturing injection module includes data analysis and feature processing, formation model prediction, and fracturing parameter optimization functions.

[0164] Data analysis and feature processing: The data collected by the sensor, including pressure, position, flow rate, temperature, and other key parameters, are processed by the system for data cleaning, denoising, missing value processing, outlier detection, and data normalization to ensure data quality and consistency. The formation fracturing related data features are extracted and analyzed by combining the relevant information gain and support vector machine algorithm.

[0165] Formation model and prediction: The formation data are trained using machine learning algorithms to establish a prediction model of formation characteristics. The system combines soil pollution types and formation characteristics, uses deep neural network algorithms, and uses geological knowledge and machine learning techniques to model the collected formation data to obtain information such as formation physical property distribution and fracture network.

[0166] Fracturing parameter optimization: The system uses the established formation characteristic model to associate formation properties with fracturing parameters. By analyzing the relationship between formation characteristics and fracturing parameters, the appropriate parameter combination is selected to optimize the fracturing effect. The mapping relationship between formation response and fracturing parameters is established using actual formation data and fracturing operation data combined with machine learning algorithms. The optimal fracturing parameter settings are found by training the model algorithm. This function optimizes the fracturing process, improves productivity, reduces costs, and reduces environmental impact.

[0167] Embodiment 3

[0168] As shown in Figure 4 The soil pollution remediation control system of the present embodiment is characterized in that the remediation control system is applied in a contaminated site remediation decision platform, a mapping model library is constructed in the contaminated site remediation decision platform, the mapping model library includes a mapping model corresponding to each remediation device in the actual contaminated plot, and the mapping model is in communication connection with the matched remediation device.

[0169] Different repair devices include a multi-medium mixed transport device, a targeted drilling device, a solubilization extraction device, or a fracturing injection device, etc., and the contaminated site repair decision platform is configured with corresponding multi-medium mixed transport modules, targeted drilling modules, solubilization extraction modules, or fracturing injection modules, etc. These modules are specifically connected in communication with the corresponding repair devices on site through the device control system in the platform.

[0170] The mapping model is constructed based on the repair technology, repair control instruction, and corresponding repair device of each group.

[0171] The repair control system of the embodiment includes:

[0172] An initial pollution data acquisition module 1 is configured to acquire initial soil pollution data of an actual contaminated site.

[0173] A target repair scheme acquisition module 2 is configured to obtain a corresponding target repair scheme based on the initial pollution data.

[0174] The target repair scheme includes several sub-repair schemes, and each sub-repair scheme includes at least one repair technology.

[0175] Specifically, various types of data collection devices arranged in the actual contaminated site are used to collect soil state characterization parameters in multiple dimensions at any time or time period, which are used as initial soil pollution data.

[0176] The repair decision platform integrates a decision algorithm (a deep learning method) to automatically determine a repair scheme matching the initial soil pollution data. Different repair devices correspond to different repair processing functions. When the pollution state of the actual contaminated site reaches a certain level, a sub-repair scheme corresponding to each repair device is automatically generated based on several initial soil pollution data collected. Each sub-repair scheme can involve only one set of repair technology, or a combination of multiple repair technologies, which is determined according to actual conditions.

[0177] A target model acquisition module 3 is configured to acquire a target mapping model in the mapping model library matching the sub-repair scheme.

[0178] A matching module 4 is configured to generate a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determine the matching repair device.

[0179] A repair control module 5 is configured to issue the repair control instruction to the matching repair device to perform corresponding repair operations.

[0180] Different repair devices include a multi-medium mixed transport device, a targeted drilling device, a solubilization extraction device, or a fracturing injection device, and the platform communicates with these repair devices in real time to realize the functions of collecting on-site data, issuing instructions for remote control, and the like.

[0181] Other specific contents of the soil pollution repair control system of the present embodiment can be found in the contents described in Embodiment 1.

[0182] In the present scheme, the pollution site repair decision platform can communicate with different repair devices in the actual pollution plot. The repair control scheme runs in the pollution site repair decision platform. For any actual pollution plot, once it reaches the repairable state, a corresponding sub-repair scheme is automatically generated based on the respective functional characteristics of different repair devices and all pollution data corresponding to the actual pollution plot. Then, a target mapping model in the mapping model library is matched based on the sub-repair scheme, and the target mapping model is used to output a corresponding repair control instruction. The repair control instruction is then issued to the matched repair device. The repair device executes the corresponding repair action after receiving the repair instruction, thereby achieving the purpose of remote repair control of the platform on the on-site device, ensuring the accuracy and timeliness of the repair control, improving the repair quality and efficiency of the soil, and achieving the effect of intelligent operation construction.

[0183] Embodiment 4

[0184] The repair control system of the present embodiment is a further improvement of Embodiment 3, specifically:

[0185] In an implementable scheme, as shown in Figure 5 the repair control system of the present embodiment further comprises:

[0186] An actual state data acquisition module 6 is configured to acquire actual state data corresponding to the actual pollution plot during the repair process.

[0187] A repair adjustment scheme generation module 7 is configured to generate a repair adjustment scheme of the corresponding repair device according to the actual state data.

[0188] A repair adjustment module 8 is configured to generate a correction adjustment instruction using a matched target mapping model based on the repair adjustment scheme, and issue the correction adjustment instruction to the corresponding repair device to adjust the corresponding repair operation.

[0189] In the scheme, when different repair devices are used to repair the actual contaminated land, the soil changes and device states in the actual contaminated land are monitored in real time and dynamically, and targeted repair adjustments are made in a timely and adaptive manner, so that accurate repair is achieved, the repair cost and the possibility of secondary pollution are reduced, each repair action can be dynamically adjusted in real time, thereby further ensuring the accuracy and timeliness of repair control, improving the repair quality and efficiency of the soil, and improving the effect of operation construction.

[0190] In an implementable scheme, the actual state data includes at least one of the following:

[0191] Actual operation data fed back by the repair device during the repair process, actual soil pollution data of the actual contaminated land during the repair process, and actual geological change data;

[0192] In the scheme, it can be considered whether the execution action of the repair device is in place and whether an abnormality occurs during the repair process, so as to determine whether corresponding repair adjustments are needed;

[0193] It can also be considered that the changes of detection parameters of different dimensions in the soil in the site are monitored during the repair process, including data representing pollution conditions and data representing geological changes, so as to determine whether corresponding repair adjustments are needed;

[0194] Of course, other influence parameters can also be considered according to actual needs, so as to effectively ensure accurate repair control.

[0195] In an implementable scheme, the repair adjustment scheme generation module 6 is further configured to generate a repair adjustment scheme of the corresponding repair device according to the actual state data by using a preset feedback control strategy;

[0196] Different repair devices correspond to different preset feedback control strategies.

[0197] In the scheme, the feedback control schemes corresponding to different repair devices are determined in advance, so that accurate adjustment schemes can be automatically generated according to the actual state data of the site by using the corresponding schemes during the repair process, so as to achieve the effect of accurate repair.

[0198] In an implementable scheme, the repair control system further includes:

[0199] A sample data acquisition module 9 is configured to acquire a plurality of groups of sample data, each group of sample data including sample pollution data and a corresponding labeled sample repair scheme;

[0200] A model training module 10 is configured to train a preset network model based on the plurality of groups of sample data to obtain a target repair scheme generation model;

[0201] The target repair scheme acquisition module is configured to input the initial pollution data into the repair scheme generation model to output the corresponding target repair scheme.

[0202] In an implementable scheme, the model training module 10 is configured to pre-train the preset network model based on a plurality of sets of sample data to obtain an intermediate repair scheme generation model.

[0203] In response to the intermediate repair scheme generation model not meeting the training condition, iterative training is performed until the target repair scheme generation model is obtained.

[0204] In this scheme, a plurality of sets of sample data are obtained based on a sample database, and the preset network model is pre-trained and iteratively trained. Preferably, the network model can be trained while the samples are labeled, so as to quickly realize the training model, so that the model accuracy meets the training condition, accurate prediction is realized, and the accuracy and timeliness of subsequent repair control are ensured.

[0205] In addition, the repair control system also decomposes the sub-repair scheme into a plurality of sub-repair units based on the pre-set repair scheme decomposition rules corresponding to different repair devices; converts each sub-repair unit into a corresponding first control instruction by using a target mapping model, and transmits the first control instruction to the corresponding repair device through a communication interface.

[0206] The repair scheme decomposition rules can also be adaptively adjusted according to actual scene requirements.

[0207] In this scheme, the sub-repair scheme decomposition rules corresponding to different repair devices are pre-determined, and then the entire sub-repair scheme is converted into executable processes, and the executable processes are further subdivided into a plurality of sub-repair schemes, and then the sub-repair schemes are converted into corresponding control instructions and transmitted to the repair devices. The repair devices will execute the corresponding repair actions in sequence according to the different control instructions received in sequence, so as to realize fine and accurate control of remote repair, and ensure the repair quality and efficiency of the soil.

[0208] In an implementable scheme, the repair control system further comprises:

[0209] The operation data acquisition module 11 is configured to acquire actual operation data of the repair device in a plurality of dimensions during the repair process.

[0210] The progress monitoring module 12 is configured to monitor the repair progress of the repair device based on the actual operation data; and / or, the abnormality judgment module is configured to judge whether an abnormal condition occurs, and if so, generate a stop control instruction to control the corresponding repair device to stop the repair operation.

[0211] In the scheme, the actual operation data of the repair equipment in the actual scene can be monitored in real time through a sensor or the like to determine the current repair progress. If the current task is completed, the next task is controlled to be executed. If the current task is not completed, the current task is controlled to be continuously executed.

[0212] The actual operation data of the repair equipment can also be used to determine whether there is a fault or the like. If there is, the repair is controlled to be stopped in time to ensure the operation safety of the equipment and avoid unnecessary dangerous situations.

[0213] In an implementable scheme, the repair equipment includes a multi-medium mixed transport equipment, a targeted drilling equipment, a solubilization extraction equipment, or a fracturing injection equipment, or the like.

[0214] As shown in Figure 3 For the repair of a contaminated site, the repair mainly includes data collection, development of a sub-repair scheme, a repair construction plan, dynamic evaluation, and the like. Once the corresponding repair control instruction is generated based on the sub-repair scheme and is issued, different repair equipment is used to cooperatively repair the instruction control to realize synchronous repair construction of the actual contaminated land block by using different repair equipment.

[0215] In the scheme, the multi-medium mixed transport equipment, the targeted drilling equipment, the solubilization extraction equipment, the fracturing injection equipment, and the like, communicate with the contaminated site repair decision platform through an MQTT protocol or the like, receive the instruction issued by the platform, and report the live parameters of different dimensions in the field to the platform to ensure the operability of the remote repair control. The platform is used to realize all-around, timely monitoring, control, and management.

[0216] Other specific contents of the soil pollution repair control system of the embodiment can be referred to the contents described in Embodiment 2.

[0217] Embodiment 5

[0218] As shown in Figure 6 The present disclosure also provides a contaminated site repair decision platform 100. The contaminated site repair decision platform includes the soil pollution repair control system 200 described above.

[0219] The pollution site remediation decision platform of the embodiment is integrated with the soil pollution remediation control system in Embodiment 3 or 4, and can communicate and interact with different remediation devices in an actual pollution plot. The remediation control scheme runs in the pollution site remediation decision platform. For any actual pollution plot, once it reaches a state requiring remediation, the data associated with the remediation device is extracted from all the pollution data corresponding to the actual pollution plot according to the respective functional characteristics of different remediation devices, and a corresponding sub-remediation scheme is generated. Then, a corresponding target mapping model is used to generate a remediation control instruction recognizable by the remediation device and deliver it to the remediation instruction. After receiving the remediation instruction, the remediation device performs the corresponding remediation action, thereby achieving the purpose of remote remediation control of the on-site device based on the platform, ensuring the accuracy and timeliness of the remediation control, improving the remediation quality and efficiency of the soil, and achieving the effect of intelligent operation construction.

[0220] Embodiment 6

[0221] The embodiment provides an electronic device, Figure 7 A schematic diagram of the modules of the electronic device is shown. The electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the route table maintenance method of Embodiment 1 when executing the program. Figure 7 The electronic device 30 shown is merely an example and should not limit the functions and use range of the embodiments of the present disclosure.

[0222] As Figure 7 shown, the electronic device 30 can be in the form of a general computing device, for example, it can be a server device. The components of the electronic device 30 can include but are not limited to: the above-mentioned at least one processor 31, the above-mentioned at least one memory 32, a bus 33 connecting different system components including the memory 32 and the processor 31.

[0223] The bus 33 includes a data bus, an address bus, and a control bus.

[0224] The memory 32 can include volatile memory, such as random access memory (RAM) 321 and / or cache memory 322, and can further include read-only memory (ROM) 323.

[0225] The memory 32 can also include programs / utilities 325 having a set of (at least one) program modules 324, such as an operating system, one or more application programs, other program modules, and program data, each of which or some combination of which can include the implementation of a network environment.

[0226] The processor 31 performs various function applications and data processing by running the computer programs stored in the memory 32, such as the method for maintaining the routing table of the embodiment 1 of the present disclosure.

[0227] The electronic device 30 can also communicate with one or more external devices 34 such as a keyboard or a pointing device, by way of Input / Output (I / O) interface 35. Furthermore, the model generating device 30 can communicate to one or more networks such as a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet, by way of the network adapter 36. As Figure 7 illustrated, the network adapter 36 communicates to the other modules of the model generating device 30 by way of the bus 33. It should be appreciated that the model generating device 30 can be a part of another device or can be a stand-alone device. Additionally, the model generating device 30 can be connected to another remote device (not shown) by way of the network adapter 36.

[0228] It should be noted that although several means / modules or sub-means / modules are mentioned in the foregoing detailed description, such division into means / modules or sub-means / modules is merely exemplary and not mandatory. Indeed, according to an embodiment of the present disclosure, features and functions of two or more of the above-described means / modules can be embodied in a single means / module. Conversely, a single above-described means / module can be divided into several means / modules to perform the described features and functions.

[0229] Embodiment 7

[0230] In an implementable solution, the present disclosure further provides a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the above method.

[0231] The present embodiment provides a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the steps of the method in the above embodiment.

[0232] More specifically, the readable storage medium can include, but is not limited to, a portable disc, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0233] In a possible implementation, the present disclosure can also be implemented in the form of a program product, which includes program codes for causing a terminal device to perform the steps of the method in the above embodiment when the program product is run on the terminal device.

[0234] The program code, which can be written in any combination of one or more programming languages, can execute entirely on the user's device, partly on the user's device, as a stand-alone software package, partly on the user's device and partly on a remote device or entirely on the remote device.

[0235] Embodiment 8

[0236] The present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the above method.

[0237] The program code, which can be written in any combination of one or more programming languages, can execute entirely on the user's device, partly on the user's device, as a stand-alone software package, partly on the user's device and partly on a remote device or entirely on the remote device.

[0238] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an illustration, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method for remediation control of soil pollution, characterized by, The repair control method is applied to a contaminated site repair decision platform, a mapping model library is constructed in the contaminated site repair decision platform, and the mapping model library includes a mapping model corresponding to each repair device in an actual contaminated site, and the mapping model is in communication connection with the matched repair device; The repair control method includes: Obtaining initial soil pollution data of an actual contaminated site, and obtaining a corresponding target repair scheme based on the initial soil pollution data; Wherein, the target repair scheme includes several sub-repair schemes, and each sub-repair scheme includes at least one repair technology; Obtain the target mapping model in the mapping model library matched with the sub-repair scheme; Based on the target mapping model, a repair control instruction corresponding to the sub-repair scheme is generated, and the matched repair device is determined; The repair control instruction is issued to the matched repair device to perform the corresponding repair operation; The repair control method further includes: Obtaining actual state data corresponding to the actual contaminated site during the repair process; According to the actual state data, a repair adjustment scheme of the corresponding repair device is generated; Based on the repair adjustment scheme, a corresponding correction adjustment instruction is generated by using the matched target mapping model, and is issued to the corresponding repair device to adjust the corresponding repair operation; The actual state data includes at least one of the following: The actual operation data of the repair device during the repair process, the actual soil pollution data of the actual contaminated site during the repair process, and the actual geological change data; And / or, The step of generating a repair adjustment scheme of the corresponding repair device according to the actual state data includes: Using a preset feedback control strategy to generate the repair adjustment scheme of the corresponding repair device according to the actual state data; Wherein, different repair devices correspond to different preset feedback control strategies; The repair device includes a multi-medium mixed transport device, a targeted drilling device, a solubilization extraction device, or a fracturing injection device; The actual operation data of the multi-medium mixed transport device includes fracturing fluid mixing tank data, fracturing fluid storage tank data, module operation data, oxidizing agent mixing tank data, oxidizing agent storage tank data, valve closing condition data, reducing agent mixing tank data, reducing agent storage tank data, and signal connection condition data; The actual operation data of the targeted drilling device includes device operation data, signal connection condition data, drilling rig operation data, and drilling depth data; The actual operation data of the solubilization extraction device includes device operation data, signal connection condition data, water quality monitoring condition, and liquid separation condition.

2. The method for remediation control of soil pollution according to claim 1, characterized by, Before the step of obtaining a corresponding target repair scheme based on the initial soil pollution data, the following steps are further included: Obtain several groups of sample data, each group of sample data includes sample pollution data and corresponding labeled sample repair scheme; Based on several groups of sample data, a preset network model is trained to obtain a target repair scheme generation model; The step of obtaining a corresponding target repair scheme based on the initial soil pollution data includes: inputting the initial soil pollution data into the remediation scheme generation model to output a corresponding target remediation scheme.

3. The method for remediation control of soil pollution according to claim 2, characterized by, The step of training a preset model based on a plurality of sets of sample data to obtain a target remediation scheme generation model comprises: pre-training the preset network model based on a plurality of sets of sample data to obtain an intermediate remediation scheme generation model; iteratively training in response to the intermediate remediation scheme generation model not meeting a training condition until the target remediation scheme generation model is obtained.

4. The method for remediation control of soil pollution according to any one of claims 1 to 3, characterized in that, The step of issuing the remediation control instruction to the matching remediation device to perform a corresponding remediation operation further comprises: collecting actual operation data of the remediation device in several dimensions during remediation; monitoring remediation progress of the remediation device based on the actual operation data; and / or, determining whether an abnormal condition occurs, and if so, generating a stop control instruction to control the corresponding remediation device to stop remediation operation.

5. A remediation control system for soil pollution, characterized in that, The remediation control system is applied in a contaminated site remediation decision platform, the contaminated site remediation decision platform has a mapping model library constructed therein, the mapping model library includes a mapping model corresponding to each remediation device in an actual contaminated plot, and the mapping model is in communication connection with the matching remediation device; and the remediation control system is used to implement the soil pollution remediation control method according to any one of claims 1-4.

6. A contaminated site remediation decision platform characterized by, The contaminated site remediation decision platform comprises the soil pollution remediation control system according to claim 5.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the method according to any one of claims 1-4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by a processor to implement the method according to any one of claims 1-4.

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