Remediation control method and system for soil pollution, medium and computer program product

Abstract

The invention provides a soil pollution remediation control method and system, a medium and a computer program product, and the method comprises the steps: obtaining initial soil pollution data of an actual polluted land parcel, and obtaining a corresponding target remediation scheme; the target repair scheme comprises a plurality of sub-repair schemes, and each sub-repair scheme comprises at least one repair technology; obtaining a target mapping model matched with the sub-repair scheme; based on the target mapping model, generating a repair control instruction corresponding to the sub-repair scheme, and determining a matched repair device; and issuing the repair control instruction to the matched repair equipment to execute corresponding repair operation. According to the method, remote remediation control can be performed on field equipment based on a platform, soil change, equipment state and the like in the remediation process can be dynamically monitored in real time, targeted remediation adjustment is performed in a timely and adaptive manner, the accuracy and timeliness of remediation control are ensured, the remediation quality and efficiency of soil are improved, and the remediation efficiency is improved. And the intelligent operation construction effect is achieved.

Description

Technical Field

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

[0002] For soil pollution scenarios, the current remediation solutions generally have problems such as low efficiency, lack of timeliness and accuracy, or causing secondary pollution, and cannot meet the higher remediation requirements. Summary of the invention

[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 invention solves the above technical problems through the following technical solutions:

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

[0006] The repair control method comprises:

[0007] Obtaining initial soil pollution data of the actual polluted land, and obtaining a corresponding target remediation plan based on the initial pollution data;

[0008] Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology;

[0009] Acquire a target mapping model in the mapping model library that matches the sub-repair solution;

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

[0011] The repair control instruction is sent to the matching repair device to execute the corresponding repair operation.

[0012] Preferably, the repair control method further comprises:

[0013] Acquire actual status data corresponding to the actual polluted land during the restoration process;

[0014] Generating a corresponding repair adjustment plan for the repair equipment according to the actual status data;

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

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

[0017] The actual operation data fed back by the remediation equipment during the remediation process, the actual soil pollution data of the actual contaminated land during the remediation process, and the actual geological change data;

[0018] and / or,

[0019] Using a preset feedback control strategy to generate the corresponding repair adjustment plan of the repair equipment according to the actual state data;

[0020] Among them, different repair devices correspond to different preset feedback control strategies.

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

[0022] Acquire several groups of sample data, each group of sample data including sample contamination data and corresponding marked sample repair solutions;

[0023] Based on the plurality of groups of sample data, a preset network model is trained to obtain a target repair solution generation model;

[0024] The step of obtaining a corresponding target repair solution based on the initial pollution data includes:

[0025] The initial pollution data is input into the remediation solution generation model to output the corresponding target remediation solution.

[0026] Preferably, the step of training a preset model based on several groups of sample data to obtain a target repair solution generation model includes:

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

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

[0029] Preferably, after the step of sending the repair control instruction to the matching repair device to perform the corresponding repair operation, the method further includes:

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

[0031] Monitoring the repair progress of the repair 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 repair device to stop the repair operation;

[0032] and / or,

[0033] The repair equipment includes multi-media mixing equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment.

[0034] The present disclosure also provides a soil pollution remediation control system, which is applied in a contaminated site remediation decision platform, wherein a mapping model library is constructed in the contaminated site remediation decision platform, wherein the mapping model library includes a mapping model corresponding to each remediation device in an actual contaminated plot, and the mapping model is communicatively connected with the matching remediation device;

[0035] The repair control system comprises:

[0036] An initial pollution data acquisition module is used to obtain initial soil pollution data of the actual polluted land;

[0037] A target repair solution acquisition module, used to obtain a corresponding target repair solution based on the initial pollution data;

[0038] Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology;

[0039] A target model acquisition module, used to acquire a target mapping model matching the sub-repair solution in the mapping model library;

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

[0041] A repair control module is used to send the repair control instruction to the matching repair device to perform the corresponding repair operation

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

[0043] An actual status data acquisition module is used to acquire the actual status data corresponding to the actual polluted land during the restoration process;

[0044] A repair adjustment scheme generating module, used for generating a repair adjustment scheme for the corresponding repair device according to the actual status data;

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

[0046] Preferably, the actual status data includes at least one of the following:

[0047] The actual operation data fed back by the remediation equipment during the remediation process, the actual soil pollution data of the actual contaminated land during the remediation process, and the actual geological change data;

[0048] and / or,

[0049] The repair adjustment scheme generating module is also used to generate the repair adjustment scheme of the corresponding repair device according to the actual state data by adopting a preset feedback control strategy;

[0050] Among them, different repair devices correspond to different preset feedback control strategies.

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

[0052] A sample data acquisition module, used to acquire several groups of sample data, each group of sample data includes sample contamination data and a corresponding marked sample repair solution;

[0053] A model training module, used to train a preset network model based on several groups of sample data to obtain a target repair solution generation model;

[0054] The target remediation solution acquisition module is used to input the initial pollution data into the remediation solution generation model to output the corresponding target remediation solution.

[0055] Preferably, the model training module is used to pre-train the preset network model based on several groups of sample data to obtain an intermediate repair solution generation model;

[0056] In response to the intermediate repair solution generation model not satisfying the training condition, iterative training is performed until the target repair solution generation model is obtained.

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

[0058] An operation data collection module is used to collect actual operation data of the repair device in several dimensions during the repair process;

[0059] a progress monitoring module, used to monitor the repair progress of the repair device based on the actual operation data; and / or, an abnormality judgment module, used 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; and / or,

[0060] The repair equipment includes multi-media mixing equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment.

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

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

[0063] The present disclosure also provides a computer program product, comprising a computer program, wherein the steps of the above method are implemented when the computer program is executed by a processor.

[0064] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0065] The positive and progressive effects of this disclosure are:

[0066] In the present disclosure, 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 requiring remediation, the corresponding sub-remediation scheme is automatically generated based on the respective functional characteristics of different remediation equipment and all pollution data corresponding to the actual contaminated plot. Then, the 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 the corresponding remediation control instruction, which is then sent to the matching remediation equipment. After receiving the remediation instruction, the remediation equipment executes the corresponding remediation action, thereby achieving the purpose of remote remediation control of on-site equipment based on the platform, ensuring the accuracy and timeliness of remediation control, improving the remediation quality and efficiency of soil, and achieving the effect of intelligent operation construction.

[0067] In addition, when using different remediation equipment to remediate actual contaminated sites, real-time dynamic monitoring of soil changes, equipment status, etc. in the actual contaminated sites is carried out, and targeted remediation adjustments are made in a timely and adaptive manner to achieve precise remediation, reduce remediation costs and the possibility of secondary pollutants, and be able to dynamically adjust each remediation action in real time, thereby further ensuring the accuracy and timeliness of remediation control, improving the quality and efficiency of soil remediation, and improving the effectiveness of construction operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 This is a flow chart of the soil contamination remediation control method of Example 1 of the present disclosure.

[0069] Figure 2 This is a flow chart of the soil contamination remediation control method of Example 2 of the present disclosure.

[0070] Figure 3 This is an overall schematic diagram of the soil contamination remediation control according to Example 2 of the present disclosure.

[0071] Figure 4 This is a module schematic diagram of the soil contamination remediation control system of Example 3 of the present disclosure.

[0072] Figure 5 This is a module schematic diagram of the soil contamination remediation control system of Example 4 of the present disclosure.

[0073] Figure 6 This is a module diagram of the contaminated site remediation decision platform of Example 5 of the present disclosure.

[0074] Figure 7 This is a schematic diagram of the structure of an electronic device according to Embodiment 6 of the present disclosure. DETAILED DESCRIPTION

[0075] The present disclosure is further described below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0076] Example 1

[0077] The soil contamination remediation control method of this embodiment 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 land. The mapping model is connected to the matching remediation device in communication.

[0078] Among them, different remediation equipment includes multi-media mixed delivery equipment, targeted drilling equipment, solubilization and extraction equipment or fracturing injection equipment, etc. The contaminated site remediation decision platform is configured with matching multi-media mixed delivery modules, targeted drilling modules, solubilization and extraction modules or fracturing injection modules, etc. These modules are specifically connected to the corresponding on-site remediation equipment through the equipment control system in the platform.

[0079] The mapping model is constructed based on each group's repair technology, repair control instructions and corresponding repair equipment.

[0080] like Figure 1 As shown, the repair control method of this embodiment includes:

[0081] S101, obtaining initial soil pollution data of the actual polluted land, and obtaining a corresponding target remediation plan based on the initial pollution data;

[0082] Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology;

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

[0084] Specifically, based on the decision algorithm (deep learning method) integrated in the remediation decision-making platform, a remediation plan that matches the initial soil pollution data is automatically determined; different remediation equipment corresponds to different remediation processing functions. When the pollution state of the actual polluted land reaches a certain level, a sub-remediation plan corresponding to each remediation device is automatically generated based on the collected initial soil pollution data. Each sub-remediation plan may involve only one set of remediation technologies, or may include a combination of multiple remediation technologies, which is determined based on the actual situation.

[0085] S102, obtaining a target mapping model matching the sub-repair solution in a mapping model library;

[0086] S103, generating a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determining a matching repair device;

[0087] S104: Send the repair control instruction to the matching repair device to execute the corresponding repair operation.

[0088] Among them, different repair equipment includes multi-media mixed transmission equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment, etc. The platform communicates with these repair equipment in real time to realize functions such as collecting field data, issuing instructions and remote control.

[0089] In this scheme, the contaminated site remediation decision-making 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-making platform. For any actual contaminated plot, once it reaches the state requiring remediation, the corresponding sub-remediation scheme is automatically generated based on the respective functional characteristics of different remediation equipment and all pollution data corresponding to the actual contaminated plot. Then, the 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 the corresponding remediation control instruction, which is then sent to the matching remediation equipment. After receiving the remediation instruction, the remediation equipment executes the corresponding remediation action, thereby achieving the purpose of remote remediation control of on-site equipment based on the platform, ensuring the accuracy and timeliness of remediation control, improving the quality and efficiency of soil remediation, and achieving the effect of intelligent operation construction.

[0090] Example 2

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

[0092] In one feasible solution, Figure 2 As shown, after step S104, the following steps are further included:

[0093] S105, obtaining actual status data corresponding to the actual polluted land during the restoration process;

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

[0095] S107: Based on the repair adjustment scheme, a corresponding correction adjustment instruction is generated using a matching target mapping model, and is sent to a corresponding repair device to adjust a corresponding repair operation.

[0096] In this solution, when using different remediation equipment to remediate the actual contaminated land, real-time dynamic monitoring of soil changes, equipment status, etc. in the actual contaminated land is carried out, and targeted remediation adjustments are made in a timely and adaptive manner to achieve precise remediation, reduce remediation costs and the possibility of secondary pollutants, and be able to dynamically adjust each remediation action in real time, thereby further ensuring the accuracy and timeliness of remediation control, improving the quality and efficiency of soil remediation, and improving the effectiveness of construction operations.

[0097] In one feasible solution, the actual status data includes at least one of the following:

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

[0099] In this solution, it is possible to consider monitoring whether the execution action of the repair equipment is in place and whether an abnormality occurs during the repair process to determine whether corresponding repair adjustments are needed;

[0100] It is also possible to consider monitoring the changes in different dimensions of soil parameters in the site during the remediation process, including data characterizing the pollution situation and data on geological changes, to determine whether corresponding remediation adjustments are needed;

[0101] Of course, other influencing parameters can be taken into consideration according to actual needs to effectively ensure precise repair control.

[0102] In one feasible solution, step S106 includes:

[0103] Using the preset feedback control strategy to generate the corresponding repair adjustment plan for the repair equipment according to the actual status data;

[0104] Among them, different repair devices correspond to different preset feedback control strategies.

[0105] In this solution, feedback control schemes corresponding to different repair equipment are determined in advance, so that corresponding schemes can be used during the repair process to automatically generate accuracy adjustment schemes based on the actual status data of the site to achieve the effect of precise repair.

[0106] In an implementable solution, before the step of obtaining a corresponding target remediation solution based on the initial pollution data, the method further includes:

[0107] Obtain several groups of sample data, each group of sample data includes sample contamination data and a corresponding marked sample repair solution;

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

[0109] The steps of obtaining the corresponding target remediation plan based on the initial pollution data include:

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

[0111] In an implementable solution, the steps of training a preset model based on a plurality of sets of sample data to obtain a target repair solution generation model include:

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

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

[0114] In this solution, several groups of sample data are obtained from the sample database, and then the preset network model is pre-trained and iteratively trained. Preferably, the network model can be trained while marking samples to achieve rapid training of the model, so that the model accuracy meets the training conditions and precise prediction is achieved, thereby ensuring the accuracy and timeliness of subsequent repair control.

[0115] In addition, the repair control method also includes:

[0116] Based on the pre-set repair scheme decomposition rules corresponding to different repair devices, the sub-repair scheme is decomposed into several sub-repair units;

[0117] Among them, the repair plan decomposition rules can also be adaptively adjusted according to actual scenario requirements.

[0118] The target mapping model is used to convert each sub-repair unit into a corresponding first control instruction, and the first control instruction is sent to the corresponding repair device through the communication interface.

[0119] In this scheme, the sub-repair scheme decomposition rules corresponding to different remediation equipment are determined in advance, and then the entire sub-repair scheme is converted into an executable process, and then these executable processes are subdivided into several sub-repair schemes, and then these sub-repair schemes are converted into corresponding control instructions and sent to the remediation equipment. The remediation equipment will execute the corresponding remediation actions in sequence according to the different control instructions received in succession, thereby realizing refined and precise control of remote remediation and ensuring the quality and efficiency of soil remediation.

[0120] In an implementable solution, after step S104, the method further includes:

[0121] Collect actual operation data of the repair equipment in several dimensions during the repair process;

[0122] Monitor the repair progress of the repair equipment based on actual operation data; and / or determine whether an abnormal condition occurs. If so, generate a stop control instruction to control the corresponding repair equipment to stop the repair operation.

[0123] In this solution, the actual operation data of the repair equipment in the actual scene can be monitored in real time through sensors, etc. to determine the current repair progress. If the current task has been completed, the control executes the next task; if the current task has not been completed, the control continues to execute the current task.

[0124] It is also possible to determine whether there are any abnormalities such as faults by repairing the actual operating data of the equipment. If so, the repair will be stopped in time to ensure the safety of the equipment's operation and avoid unnecessary dangerous situations.

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

[0126] like Figure 3 As shown in the figure, the remediation of contaminated sites mainly includes data collection, formulation of sub-remediation plans, remediation construction plans, dynamic evaluation and other links. Once the corresponding remediation control instructions are generated and issued based on the sub-remediation plans, the remediation command and control are coordinated through different remediation equipment to achieve synchronous remediation construction of actual contaminated sites using different remediation equipment.

[0127] In this solution, remediation equipment such as multi-media mixed transmission equipment, targeted drilling equipment, solubilization extraction equipment, and fracturing injection equipment communicate with the contaminated site remediation decision-making platform through the MQTT (Message Queuing Telemetry Transport) protocol, receive instructions issued by the platform, and report real-time parameters of different dimensions on site to the platform to ensure the operability of remote remediation control and realize all-round and timely monitoring, control and management based on the platform.

[0128] The implementation principle of the repair control method of this embodiment is described below in conjunction with a specific repair device:

[0129] In this embodiment, the remediation equipment of the contaminated site includes a multi-media mixed delivery device, a targeted drilling device, a solubilization extraction device or a fracturing injection device, etc. The contaminated site remediation decision platform is configured with a matching multi-media mixed delivery module, a targeted drilling module, a solubilization extraction module or a fracturing injection module, etc. Specifically:

[0130] 1. For multi-media mixed transmission module

[0131] (1) The multi-media mixed transport module can collect data related to the multi-media mixed transport process through sensors pre-arranged on site, including the characteristics of different media, soil pollution level, mixed transport targets, etc.;

[0132] (2) Combine the collected relevant data with relevant environmental standards, regulations, etc., and use the association rules established by the deep learning algorithm and the industry expert scoring model to determine the corresponding sub-remediation plan, such as the concentration of the repair agent in each borehole, the injection volume and injection sequence, etc. Specifically: generate the execution sequence of the multi-media mixed transmission equipment, generate the dosage and ratio of each agent, generate the injection sequence and transmission volume, etc.;

[0133] (3) Based on the medium type such as fracturing fluid, oxidant, reductant, etc., the sub-repair scheme is decomposed into multiple sub-repair schemes, each of which includes the stirring times of the corresponding medium, the amount of agent for stirring once, the amount of clean water for stirring once, etc., and these sub-repair schemes are converted into corresponding repair instructions, and sent to the equipment control module based on the MQTT protocol, so as to issue the repair instructions to the on-site multi-media mixing equipment to perform the corresponding repair operation;

[0134] (4) During the repair process, the actual operation data, soil status data, geological change data, etc. of the on-site multi-media mixed transmission equipment are collected and reported to the platform through the equipment control module, and then the received data is cleaned, processed, and analyzed in the platform; if the analysis determines that the current task has not been completed, the control continues to execute the current task, otherwise the control executes the next task, such as executing the fracturing task and preparing to open the delivery valve;

[0135] Among them, the actual operation data of the multi-media mixed transmission equipment includes: fracturing fluid mixing tank data, fracturing fluid storage tank data, module operation basic situation data, oxidant mixing tank data, oxidant storage tank data, valve closing situation data, reductant mixing tank data, reductant storage tank data, signal connection situation data, etc.

[0136] Specifically, the platform uses supervised learning and reinforcement learning to construct a liquid target mapping model, which is set in the equipment control module; through communication with the interface of the multi-media mixing device, the output results of the model are transmitted to the multi-media mixing device to control it to perform repair operations, including the addition, mixing and stirring of liquids;

[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 back the collected data 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-media mixing module according to the direction and size of the difference. For example, if the flow rate is too high, the flow rate can be reduced or the injection can be stopped; if the flow rate is too low, the flow rate can be increased or the injection speed can be adjusted to achieve precise control of the multi-media mixing equipment, and then achieve the effect of precise repair.

[0138] 2. For the targeted drilling module

[0139] (1) Various data of the drilling process, such as geological structure, drill bit position, permeability coefficient, hydraulic conductivity coefficient, water level conductivity coefficient, pressure conductivity coefficient, water supply degree, etc., are acquired in real time through sensors and data acquisition systems, and these data are transmitted to the platform's targeted drilling module; deep learning algorithms are used to combine factors such as target location and drilling parameters, generate corresponding sub-repair plans through historical data and geological information, and according to user needs, optimize drilling path planning, and generate the best drilling path to achieve accurate targeted drilling, so as to realize intelligent control and management of the drilling module and achieve accurate targeted drilling operations;

[0140] (2) After determining that the on-site targeted drilling equipment is turned on, a corresponding repair instruction is generated based on the sub-repair scheme, and sent to the equipment control module based on the MQTT protocol, so as to issue the repair instruction to the corresponding targeted drilling module to perform the corresponding repair operation;

[0141] (3) During the repair process, the actual operation data of the targeted drilling equipment, soil status data, geological change data, etc. are collected and reported to the platform through the equipment control module, and then the received data is cleaned, processed, and analyzed on the platform; if the analysis determines that the current task has not been completed, the control continues to execute the current task, otherwise the control executes the next task;

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

[0143] (4) Generate a repair adjustment plan based on geological changes, drill bit status and other factors to automatically adjust drilling parameters and paths, and adjust parameters such as direction, inclination and torque in real time;

[0144] Specifically, the targeted drilling module is mainly used for data collection and analysis, building machine learning and deep learning models, path planning and optimization, and real-time adjustment and control;

[0145] The data acquisition function mainly collects data in real time through sensors equipped with the targeted drilling module, including inertial navigation system, gyroscope, accelerometer, magnetometer, etc., to measure the position, direction and motion state of the module. In addition, geological sensors and temperature sensors can also be included to monitor geological conditions and geological structures, permeability coefficient, water conductivity coefficient, water level conductivity coefficient, pressure conductivity coefficient, water supply degree, etc.

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

[0147] The platform is embedded with machine learning and deep learning models, and stores a large amount of drilling-related data. It uses training models to learn and predict the best drilling path, adjust parameters, etc. Historical data and geological information are used to build a model, which continuously adjusts weights and parameters through back propagation and optimization algorithms to improve the system's prediction accuracy and decision-making ability. Path planning and optimization can generate the best drilling path based on factors such as geological structure, target location and user requirements. The path planning algorithm combines technologies such as machine learning, optimization algorithms and rule engines to achieve precise targeted drilling.

[0148] In addition, the system can automatically adjust the drilling parameters and paths according to the real-time data and feedback information to ensure the accuracy and stability of the drilling. For example, according to geological changes, the system can automatically control the module to adjust the parameters such as the drill direction, inclination and torque.

[0149] 3. For the solubilization and extraction module

[0150] (1) The solubilization and extraction module first analyzes and predicts the data in the contaminated soil, specifically analyzing the characteristics of soil pollutants and soil parameter data, including soil pH, water content, organic matter content, etc., to establish a model to determine the sub-remediation plan, such as determining the execution order and vacuum requirements of the solubilization and extraction wells;

[0151] (2) Based on the vacuum degree, water replenishment range, negative pressure range, liquid level range, delay requirements, etc., 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 equipment control module based on the MQTT protocol, so as to send the repair instructions to the corresponding solubilization and extraction equipment to perform the corresponding repair operation;

[0152] (3) During the remediation process, the actual operation data of the solubilization and extraction equipment, soil status data, geological change data, etc. are collected and reported to the platform through the equipment control module, and then the received data is cleaned, processed, and analyzed in the platform; if the analysis determines that the current task has not been completed, the control continues to execute the current task, otherwise the control executes the next task;

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

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

[0155] 4. For the fracturing injection module

[0156] (1) During the in-situ remediation of contaminated soil, fracturing can increase the transport capacity of liquids. By applying pressure, the remediation agent can be better transported to the contaminated area, improving the remediation effect. By fracturing the soil, the soil pore structure can be changed, promoting contact and reaction between pollutants and remediation agents, and accelerating the remediation process. Fracturing can create or expand these seepage channels, increase the permeability of the soil, increase the distribution range of the remediation agent and accelerate the remediation process;

[0157] For the fracturing injection module corresponding to the fracturing injection module, the main function is to control the pressure and position of the fracturing injection module during the fracturing process to accurately repair the contaminated soil and reduce the cost and time of repair;

[0158] The fracturing injection module is mainly used to clean, denoise, detect outliers and normalize the data collected by the sensor, including key parameters such as pressure, position, flow, temperature, etc., to ensure the quality and consistency of the data; and to extract and analyze the characteristics of formation fracturing related data by combining relevant variance analysis, information gain and recursive feature elimination methods and SVM (support vector machine), clustering algorithm and other processing methods. Then, by analyzing the formation data, historical fracturing data and modeling the formation and fracturing process, a fracturing plan is formulated, that is, using artificial intelligence and machine learning algorithms to accurately control the fracturing injection of the fracturing injection module;

[0159] (2) Decomposing the corresponding sub-repair scheme into multiple sub-repair schemes based on fracturing depth, injection sequence, sealing pressure, liquid injection requirements, gas injection requirements, etc., and converting them into corresponding repair instructions, which are then sent to the equipment control module based on the MQTT protocol, so as to send the repair instructions to the corresponding solubilization and extraction module to perform the corresponding repair operation;

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

[0161] The actual operation data of the fracturing injection module includes: basic equipment operation data, signal connection data, fracturing injection data, sealing data, etc.

[0162] (4) During the fracturing process, the real-time monitoring data is quickly processed and analyzed to extract key features and indicators, including formation physical parameters, pressure sensor data, fracture expansion information, and feedback control strategies are used to adjust the pressure and size. For example, the 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 the automatic control module adjusts the injection rate and pressure of the liquid or gas to achieve the appropriate pressure;

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

[0164] Data analysis and feature processing are: the data collected by the sensor includes key parameters such as pressure, position, flow, temperature, etc. The system performs data cleaning, denoising, missing value processing, outlier detection and data normalization on the collected data to ensure data quality and consistency. Combined with relevant information gain and support vector machine algorithm, the data features related to formation fracturing are extracted and analyzed.

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

[0166] Fracturing parameter optimization is a system that uses the established formation property model to associate formation properties with fracturing parameters. By analyzing the relationship between formation properties 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 by using actual formation data and fracturing operation data, combined with machine learning algorithms. The optimal fracturing parameter settings are found by training model algorithms. This function optimizes the fracturing process, improves production capacity, reduces costs and reduces environmental impact.

[0167] Example 3

[0168] like Figure 4 As shown, the soil pollution remediation control system of this embodiment is characterized in that the remediation control system is applied in a contaminated site remediation decision platform, and a mapping model library is constructed in the contaminated site remediation decision platform, and the mapping model library includes a mapping model corresponding to each remediation device in the actual contaminated land, and the mapping model is communicatively connected with the matching remediation device;

[0169] Among them, different remediation equipment includes multi-media mixed delivery equipment, targeted drilling equipment, solubilization and extraction equipment or fracturing injection equipment, etc. The contaminated site remediation decision platform is configured with matching multi-media mixed delivery modules, targeted drilling modules, solubilization and extraction modules or fracturing injection modules, etc. These modules are specifically connected to the corresponding on-site remediation equipment through the equipment control system in the platform.

[0170] The mapping model is constructed based on each group's repair technology, repair control instructions and corresponding repair equipment.

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

[0172] The initial pollution data acquisition module 1 is used to obtain the initial soil pollution data of the actual polluted land;

[0173] A target repair solution acquisition module 2 is used to obtain a corresponding target repair solution based on the initial pollution data;

[0174] Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology;

[0175] Specifically, various types of data collection equipment installed in the actual contaminated land are used to collect soil state characterization parameters in multiple dimensions of the actual contaminated land at any time or time period as initial soil contamination data;

[0176] Specifically, based on the decision algorithm (deep learning method) integrated in the remediation decision-making platform, a remediation plan that matches the initial soil pollution data is automatically determined; different remediation equipment corresponds to different remediation processing functions. When the pollution state of the actual polluted land reaches a certain level, a sub-remediation plan corresponding to each remediation device is automatically generated based on the collected initial soil pollution data. Each sub-remediation plan may involve only one set of remediation technologies, or may include a combination of multiple remediation technologies, which is determined based on the actual situation.

[0177] A target model acquisition module 3 is used to acquire a target mapping model matching the sub-repair solution in the mapping model library;

[0178] A matching module 4, used for generating a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determining the matching repair device;

[0179] The repair control module 5 is used to send the repair control instruction to the matching repair device to perform the corresponding repair operation.

[0180] Among them, different repair equipment includes multi-media mixed transmission equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment, etc. The platform communicates with these repair equipment in real time to realize functions such as collecting field data, issuing instructions and remote control.

[0181] For other specific contents of the soil pollution remediation control system of this embodiment, please refer to the contents recorded in Example 1.

[0182] In this scheme, the contaminated site remediation decision-making 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-making platform. For any actual contaminated plot, once it reaches the state requiring remediation, the corresponding sub-remediation scheme is automatically generated based on the respective functional characteristics of different remediation equipment and all pollution data corresponding to the actual contaminated plot. Then, the 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 the corresponding remediation control instruction, which is then sent to the matching remediation equipment. After receiving the remediation instruction, the remediation equipment executes the corresponding remediation action, thereby achieving the purpose of remote remediation control of on-site equipment based on the platform, ensuring the accuracy and timeliness of remediation control, improving the quality and efficiency of soil remediation, and achieving the effect of intelligent operation construction.

[0183] Example 4

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

[0185] In one feasible solution, Figure 5 As shown, the repair control system of this embodiment also includes:

[0186] The actual state data acquisition module 6 is used to obtain the actual state data corresponding to the actual polluted land during the restoration process;

[0187] A repair adjustment scheme generating module 7 is used to generate a repair adjustment scheme for the corresponding repair equipment according to the actual status data;

[0188] The repair adjustment module 8 is used to generate corresponding correction adjustment instructions based on the repair adjustment scheme and adopt a matching target mapping model, and send the instructions to the corresponding repair device to adjust the corresponding repair operation.

[0189] In this solution, when using different remediation equipment to remediate the actual contaminated land, real-time dynamic monitoring of soil changes, equipment status, etc. in the actual contaminated land is carried out, and targeted remediation adjustments are made in a timely and adaptive manner to achieve precise remediation, reduce remediation costs and the possibility of secondary pollutants, and be able to dynamically adjust each remediation action in real time, thereby further ensuring the accuracy and timeliness of remediation control, improving the quality and efficiency of soil remediation, and improving the effectiveness of construction operations.

[0190] In one feasible solution, the actual status data includes at least one of the following:

[0191] The actual operation data fed back by the remediation equipment during the remediation process, the actual soil pollution data of the actual contaminated land during the remediation process, and the actual geological change data;

[0192] In this solution, it is possible to consider monitoring whether the execution action of the repair equipment is in place and whether an abnormality occurs during the repair process to determine whether corresponding repair adjustments are needed;

[0193] It is also possible to consider monitoring the changes in different dimensions of soil parameters in the site during the remediation process, including data characterizing the pollution situation and data on geological changes, to determine whether corresponding remediation adjustments are needed;

[0194] Of course, other influencing parameters can be taken into consideration according to actual needs to effectively ensure precise repair control.

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

[0196] Among them, different repair devices correspond to different preset feedback control strategies.

[0197] In this solution, feedback control schemes corresponding to different repair equipment are determined in advance, so that corresponding schemes can be used during the repair process to automatically generate accuracy adjustment schemes based on the actual status data of the site to achieve the effect of precise repair.

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

[0199] A sample data acquisition module 9 is used to acquire several groups of sample data, each group of sample data includes sample contamination data and a corresponding marked sample repair solution;

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

[0201] The target remediation solution acquisition module is used to input the initial pollution data into the remediation solution generation model to output the corresponding target remediation solution.

[0202] In an implementable solution, the model training module 10 is used to pre-train the preset network model based on several groups of sample data to obtain an intermediate repair solution generation model;

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

[0204] In this solution, several groups of sample data are obtained from the sample database, and then the preset network model is pre-trained and iteratively trained. Preferably, the network model can be trained while marking samples to achieve rapid training of the model, so that the model accuracy meets the training conditions and precise prediction is achieved, thereby ensuring the accuracy and timeliness of subsequent repair control.

[0205] In addition, the repair control system will also decompose the sub-repair plan into several sub-repair units based on the pre-set repair plan decomposition rules corresponding to different repair devices; use the target mapping model to convert each sub-repair unit into a corresponding first control instruction, and send it to the corresponding repair device through the communication interface.

[0206] Among them, the repair plan decomposition rules can also be adaptively adjusted according to actual scenario requirements.

[0207] In this scheme, the sub-repair scheme decomposition rules corresponding to different remediation equipment are determined in advance, and then the entire sub-repair scheme is converted into an executable process, and then these executable processes are subdivided into several sub-repair schemes, and then these sub-repair schemes are converted into corresponding control instructions and sent to the remediation equipment. The remediation equipment will execute the corresponding remediation actions in sequence according to the different control instructions received in succession, thereby realizing refined and precise control of remote remediation and ensuring the quality and efficiency of soil remediation.

[0208] In one feasible solution, the repair control system further includes:

[0209] The operation data collection module 11 is used to collect actual operation data of the repair equipment in several dimensions during the repair process;

[0210] The progress monitoring module 12 is used to monitor the repair progress of the repair equipment based on the actual operation data; and / or the abnormality judgment module is used to judge whether an abnormal situation occurs. If it occurs, a stop control instruction is generated to control the corresponding repair equipment to stop the repair operation.

[0211] In this solution, the actual operation data of the repair equipment in the actual scene can be monitored in real time through sensors, etc. to determine the current repair progress. If the current task has been completed, the control executes the next task; if the current task has not been completed, the control continues to execute the current task.

[0212] It is also possible to determine whether there are any abnormalities such as faults by repairing the actual operating data of the equipment. If so, the repair will be stopped in time to ensure the safety of the equipment's operation and avoid unnecessary dangerous situations.

[0213] In one feasible solution, the repair equipment includes multi-media mixing equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment, etc.

[0214] like Figure 3 As shown in the figure, the remediation of contaminated sites mainly includes data collection, formulation of sub-remediation plans, remediation construction plans, dynamic evaluation and other links. Once the corresponding remediation control instructions are generated and issued based on the sub-remediation plans, the remediation command and control are coordinated through different remediation equipment to achieve synchronous remediation construction of actual contaminated sites using different remediation equipment.

[0215] In this solution, remediation equipment such as multi-media mixed transmission equipment, targeted drilling equipment, solubilization extraction equipment, and fracturing injection equipment communicate with the contaminated site remediation decision-making platform through the MQTT protocol, receive instructions issued by the platform, and report real-time parameters of different dimensions on site to the platform to ensure the operability of remote remediation control and realize all-round and timely monitoring, control and management based on the platform.

[0216] For other specific contents of the soil pollution remediation control system of this embodiment, please refer to the contents recorded in Example 2.

[0217] Example 5

[0218] like Figure 6 As shown, the present disclosure further provides a contaminated site remediation decision platform 100, which includes the above-mentioned soil contamination remediation control system 200.

[0219] The contaminated site remediation decision platform of this embodiment is integrated with the soil pollution remediation control system in Example 3 or 4. 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 a state requiring remediation, it automatically extracts data associated with the remediation equipment from all pollution data corresponding to the actual contaminated plot according to the respective functional characteristics of different remediation equipment, and then generates a corresponding sub-remediation scheme. Then, the corresponding target mapping model is used to generate remediation control instructions that can be recognized by the remediation equipment and send them to the remediation instruction. After receiving the remediation instruction, the remediation equipment executes the corresponding remediation action, thereby achieving the purpose of remote remediation control of on-site equipment based on the platform, ensuring the accuracy and timeliness of remediation control, improving the remediation quality and efficiency of soil, and achieving the effect of intelligent operation construction.

[0220] Example 6

[0221] This embodiment provides an electronic device, Figure 7 The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for maintaining the routing table of the first embodiment is implemented. Figure 7 The electronic device 30 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0222] like Figure 7 As shown, the electronic device 30 may be in the form of a general-purpose computing device, for example, it may be a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and 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 may include a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read-only memory (ROM) 323 .

[0225] The memory 32 may also include a program / utility 325 having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0226] The processor 31 executes various functional applications and data processing by running the computer programs stored in the memory 32, such as the routing table maintenance method of the first embodiment of the present disclosure.

[0227] The electronic device 30 may also communicate with one or more external devices 34 (e.g., keyboards, pointing devices, etc.). Such communication may be performed via an input / output (I / O) interface 35. Furthermore, the model generating device 30 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 36. Figure 7 As shown, the network adapter 36 communicates with other modules of the model-generated device 30 via the bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated device 30, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0228] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0229] Example 7

[0230] In an operative solution, the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, and the computer program implements the steps of the above method when executed by a processor.

[0231] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method in the above embodiment are implemented.

[0232] The readable storage medium may include but is not limited to: a portable disk, 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 manner, the present disclosure may also be implemented in the form of a program product, which includes program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps in the method of the above embodiment.

[0234] Among them, the program code for executing the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on the remote device.

[0235] Example 8

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

[0237] Among them, the program code for executing the computer program product of the present disclosure can be written in any combination of one or more programming languages, and the program code can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely 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 example, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, but these changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A soil pollution remediation and control method, characterized in that: The remediation control method is applied in a contaminated site remediation decision platform, in which a mapping model library is constructed, the mapping model library includes a mapping model corresponding to each remediation device in an actual contaminated plot, and the mapping model is communicatively connected with the matching remediation device; The repair control method comprises: Obtaining initial soil pollution data of the actual polluted land, and obtaining a corresponding target remediation plan based on the initial pollution data; Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology; Acquire a target mapping model in the mapping model library that matches the sub-repair solution; Generating a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determining the matching repair device; The repair control instruction is sent to the matching repair device to execute the corresponding repair operation.

2. The soil contamination remediation control method according to claim 1, characterized in that: The repair control method further comprises: Acquire actual status data corresponding to the actual polluted land during the restoration process; Generating a corresponding repair adjustment plan for the repair equipment according to the actual status data; Based on the repair adjustment scheme, a corresponding correction adjustment instruction is generated by using the matched target mapping model, and is sent to the corresponding repair device to adjust the corresponding repair operation.

3. The soil contamination remediation control method according to claim 2, characterized in that: The actual status data includes at least one of the following: The actual operation data fed back by the remediation equipment during the remediation process, the actual soil pollution data of the actual contaminated land during the remediation process, and the actual geological change data; and / or, The step of generating a corresponding repair adjustment plan for the repair device according to the actual status data comprises: Using a preset feedback control strategy to generate the corresponding repair adjustment plan of the repair equipment according to the actual state data; Among them, different repair devices correspond to different preset feedback control strategies.

4. The soil contamination remediation and control method according to claim 1, characterized in that: Before the step of obtaining the corresponding target repair solution based on the initial pollution data, the method further includes: Acquire several groups of sample data, each group of sample data including sample contamination data and corresponding marked sample repair solutions; Based on the plurality of groups of sample data, a preset network model is trained to obtain a target repair solution generation model; The step of obtaining a corresponding target repair solution based on the initial pollution data includes: The initial pollution data is input into the remediation solution generation model to output the corresponding target remediation solution.

5. The soil contamination remediation control method according to claim 4, characterized in that: The step of training a preset model based on the plurality of groups of sample data to obtain a target repair solution generation model includes: Based on the plurality of groups of sample data, the preset network model is pre-trained to obtain an intermediate repair solution generation model; In response to the intermediate repair solution generation model not satisfying the training condition, iterative training is performed until the target repair solution generation model is obtained.

6. The soil contamination remediation and control method according to any one of claims 1 to 5, characterized in that: After the step of sending the repair control instruction to the matching repair device to perform the corresponding repair operation, the method further includes: Collecting actual operation data of the repair equipment in several dimensions during the repair process; Monitoring the repair progress of the repair 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 repair device to stop the repair operation; and / or, The repair equipment includes multi-media mixing equipment, targeted drilling equipment, solubilization extraction equipment or fracturing injection equipment.

7. A soil pollution remediation control system, characterized in that: The remediation control system is applied in a contaminated site remediation decision platform, in which a mapping model library is constructed, the mapping model library includes a mapping model corresponding to each remediation device in an actual contaminated plot, and the mapping model is communicatively connected with the matching remediation device; The repair control system comprises: An initial pollution data acquisition module is used to obtain initial soil pollution data of the actual polluted land; A target repair solution acquisition module, used to obtain a corresponding target repair solution based on the initial pollution data; Wherein, the target repair scheme includes several sub-repair schemes, and each of the sub-repair schemes includes at least one repair technology; A target model acquisition module, used to acquire a target mapping model matching the sub-repair solution in the mapping model library; A matching module, used to generate a repair control instruction corresponding to the sub-repair scheme based on the target mapping model, and determine the matching repair device; The repair control module is used to send the repair control instruction to the matching repair device to execute the corresponding repair operation.

8. A contaminated site remediation decision-making platform, characterized in that: The contaminated site remediation decision platform includes the soil contamination remediation control system described in claim 7.

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

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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