Groundwater quality monitoring equipment control method, system, equipment and medium

Abstract

The present invention relates to the field of new generation information technology, and discloses a method, system, equipment and medium for controlling groundwater quality monitoring equipment. The method comprises: connecting a sampling device to obtain sampling device information; obtaining a groundwater sampling task and determining sampling task monitoring parameters; performing task interval time span analysis and establishing an identification control relationship; performing matching analysis based on the operating status and sampling data and the sampling task monitoring parameters, performing control identification, and locating the equipment layout to perform equipment control operations. The present invention solves the technical problems in the prior art of high failure rate, high energy consumption and inability to centrally manage groundwater quality monitoring equipment during continuous operation, and achieves the technical effects of realizing equipment standby, sleep wake-up, remote control and multi-device network management, improving equipment operation stability, reducing energy consumption and improving management efficiency.

Description

Technical Field

[0001] The present invention relates to the field of new generation information technology, and in particular to a groundwater quality monitoring equipment control method, system, equipment and medium. Background Art

[0002] As a vital component of water resources, groundwater quality is of vital importance to the ecological environment, human health, and socioeconomic development. With the increasing emphasis on groundwater protection and management, groundwater quality monitoring has become increasingly crucial. Traditional groundwater quality monitoring often relies on manual, periodic sampling and laboratory analysis. This approach not only consumes significant manpower, material resources, and time, but also has limitations in terms of data timeliness and accuracy. Driven by modern technological advances, automated groundwater quality monitoring equipment has emerged. For example, some monitoring systems can automatically collect groundwater quality data through sensors and transmit this data to data processing centers for analysis. These systems have made progress in automating data collection, reducing manual intervention and increasing the frequency and convenience of data acquisition. However, these existing technologies still have significant shortcomings in several key areas. Regarding device control, while devices can operate automatically, they lack sophisticated operational status management. Most devices cannot flexibly adjust their operating modes based on actual monitoring needs. For example, they cannot automatically enter standby or sleep mode between tasks to reduce energy consumption, and instead operate continuously in a high-energy-consuming state, resulting in significant energy waste. At the same time, long periods of uninterrupted equipment operation accelerate component wear, significantly increasing failure rates, increasing maintenance costs and equipment downtime, and impacting the continuity of monitoring operations. Existing technologies are relatively weak in multi-device collaboration. While multiple devices can collect data simultaneously, they lack effective coordination mechanisms. They cannot intelligently allocate tasks and coordinate resources based on factors such as the overall sampling task requirements and device location, failing to fully leverage the advantages of multi-device collaboration and reducing overall monitoring efficiency. For example, when monitoring large groundwater areas, it is difficult to rationally arrange the sampling sequence and frequency of devices in different locations to achieve efficient water quality monitoring across the entire area. Furthermore, existing technologies are less than ideal in responding to external environmental changes, particularly in extreme weather conditions such as heavy rain, flooding, and high or low temperatures, where the equipment lacks effective self-protection mechanisms. Without real-time monitoring and analysis of weather data, equipment operating status cannot be adjusted promptly, making the equipment susceptible to damage from severe weather, which in turn affects the accuracy and integrity of monitoring data and may even lead to equipment failure, requiring significant time and resources to repair, seriously impacting the stability and reliability of monitoring operations.

[0003] The existing technology has technical problems such as high failure rate of groundwater quality monitoring equipment during continuous operation, high energy consumption, and inability to centrally manage. Summary of the Invention

[0004] The present application provides a groundwater quality monitoring equipment control method, system, equipment and medium, which are used to solve the technical problems in the prior art of groundwater quality monitoring equipment such as high failure rate during continuous operation, high energy consumption and inability to be centrally managed.

[0005] In view of the above problems, the present application provides a groundwater quality monitoring equipment control method, system, equipment and medium.

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] The present application provides a method for controlling groundwater quality monitoring equipment, the method comprising:

[0008] Connect the sampling device and obtain the sampling device information, which includes the device layout location, operating status, and sampling data; obtain the groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine the sampling task monitoring parameters; perform task interval time span analysis based on the sampling task monitoring parameters, and establish an identification and control relationship between the device control parameters and the sampling task monitoring parameters. The identification and control relationship describes the sampling task monitoring parameters corresponding to the device control parameters, where the device control parameters include device standby, sleep wake-up, and remote switch control; perform matching analysis with the sampling task monitoring parameters based on the operating status and sampling data, use the matching sampling task monitoring parameters and the identification and control relationship to perform control identification, and locate the device layout position to perform device control operations.

[0009] A second aspect of the present application provides a groundwater quality monitoring equipment control system, the system comprising:

[0010] A sampling device information acquisition module, the sampling device information acquisition module is used to connect the sampling device and obtain sampling device information, the sampling device information includes the device layout location, operating status, and sampling data; a groundwater sampling task acquisition module, the groundwater sampling task acquisition module is used to obtain the groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine the sampling task monitoring parameters; a span analysis module, the span analysis module is used to perform task interval time span analysis based on the sampling task monitoring parameters, and establish an identification control relationship between the device control parameters and the sampling task monitoring parameters, the identification control relationship describes the sampling task monitoring parameters corresponding to the device control parameters, wherein the device control parameters include device standby, sleep wake-up, and remote switch control; a device control operation module, the device control operation module is used to perform matching analysis with the sampling task monitoring parameters based on the operating status and sampling data, perform control identification using the matching sampling task monitoring parameters and the identification control relationship, and locate the device layout position for device control operation.

[0011] The third aspect of the present application provides a device comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute a groundwater quality monitoring equipment control method provided in the present application.

[0012] The fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which is used to execute a groundwater quality monitoring equipment control method provided by the present application.

[0013] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0014] Connect the sampling device and obtain sampling device information; obtain the groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine the sampling task monitoring parameters; perform task interval time span analysis based on the sampling task monitoring parameters, and establish an identification and control relationship between the device control parameters and the sampling task monitoring parameters; perform matching analysis based on the operating status and sampling data with the sampling task monitoring parameters, use the matching sampling task monitoring parameters with the identification and control relationship to perform control identification, and locate the device layout to perform device control operations. This achieves the technical effects of realizing device standby, sleep wake-up, remote control, and multi-device network management, improving device operation stability, reducing energy consumption, and improving management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 A flow chart of a groundwater quality monitoring equipment control method provided in an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the control system structure of a groundwater quality monitoring device provided in an embodiment of the present application.

[0018] Figure 3 A schematic diagram of the structure of a device provided in this application.

[0019] Explanation of the reference numerals: sampling equipment information acquisition module 10 , groundwater sampling task acquisition module 20 , span analysis module 30 , equipment control operation module 40 , processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0020] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below. Example 1

[0022] This application provides a groundwater quality monitoring equipment control method, system, equipment and medium to solve the technical problems in the prior art of high failure rate, high energy consumption and inability to centrally manage groundwater quality monitoring equipment during continuous operation.

[0023] like Figure 1 As shown, the present application provides a method for controlling groundwater quality monitoring equipment, the method comprising:

[0024] Step S100: Connecting a sampling device and obtaining sampling device information, wherein the sampling device information includes device location, operating status, and sampling data.

[0025] Specifically, a stable connection with the sampling equipment is established. Data links are established with distributed sampling equipment via wired or wireless communication methods (such as RS485, Bluetooth, Wi-Fi, 4G / 5G, etc.) to ensure reliable and real-time data transmission. After successful connection, sampling equipment information is obtained. The sampling equipment's built-in positioning module (such as GPS or Beidou) obtains precise latitude and longitude coordinates, clearly defining its geographic location within the monitoring area. This is crucial for determining the sampling range and analyzing regional water quality differences. Operating status information, including whether the equipment is currently operating normally, in standby mode, or in a fault state, is obtained through the equipment's internal status monitoring sensors and feedback mechanisms. For example, the device's internal circuit monitoring module provides real-time feedback on power supply and circuit connection status, while motor operation sensors provide information on the operating status of equipment such as the sampling pump. Sampling data is water quality parameter data collected by the sampling equipment after sampling groundwater, such as pH, dissolved oxygen (DO), electrical conductivity (EC), temperature, and ion concentrations. This data directly reflects groundwater quality and provides fundamental data support for subsequent analysis and decision-making.

[0026] Step S200: obtaining a groundwater sampling task, performing parameter decomposition on the groundwater sampling task, and determining monitoring parameters of the sampling task.

[0027] Specifically, groundwater sampling task information is obtained from the monitoring system's task management module or external input, and then detailed parameter decomposition is performed. Groundwater sampling tasks contain multiple monitoring objectives and requirements. For example, the task may stipulate long-term groundwater quality monitoring within a specific area to assess groundwater pollution trends; or short-term high-frequency sampling of groundwater around a suspected pollution source to quickly determine the spread of pollution. During the parameter decomposition process, the sampling task monitoring parameters are determined, such as the sampling time interval (e.g., hourly, daily, weekly, etc.), which determines the frequency of monitoring groundwater quality changes; the sampling depth range, as groundwater quality at different depths may vary, requiring a clear vertical sampling interval; and the types of water quality indicators to be monitored, determining the water quality parameters that require focus based on the task objectives (e.g., heavy metal content, organic pollutant concentration, etc.). Through accurate parameter decomposition, abstract sampling tasks are transformed into specific, actionable monitoring parameters, providing clear guidance for subsequent equipment control and data collection.

[0028] Step S300: Based on the sampling task monitoring parameters, perform task interval time span analysis and establish an identification and control relationship between the device control parameters and the sampling task monitoring parameters. The identification and control relationship describes the sampling task monitoring parameters corresponding to the device control parameters, where the device control parameters include device standby, sleep wake-up, and remote switch control.

[0029] Specifically, an in-depth analysis of the time span between sampling tasks is conducted based on sampling task monitoring parameters. When sampling task monitoring parameters indicate a long sampling interval, such as during quarterly or annual long-term water quality trend monitoring, where sampling is not required for extended periods, a reasonable sleep time threshold should be considered. This threshold is determined based on multiple factors. For example, if the water level is stabilizing during sampling, the time required for the device to enter sleep mode can affect the timing of the device entering sleep mode. Furthermore, the time required for sleep and wake-up operations, as well as the time span over which these operations may affect sampling, should be carefully considered. For example, some sampling devices require a certain amount of time to wake up from sleep mode and stabilize, potentially inaccurate data collected during this period. This time delay should be taken into account. Based on these factors, a sleep time threshold is determined that effectively saves energy while not excessively impacting sampling operations, allowing the device to enter sleep mode during extended periods without sampling tasks, thereby reducing energy consumption. At the same time, corresponding device control parameter relationships are established for different sampling task monitoring parameters. When the task requires high-frequency sampling, such as hourly or shorter intervals, the device control parameters should be set to reduce standby time to ensure that the device can quickly respond to sampling tasks at any time, that is, shorten the transition time from standby to working state. For low-frequency sampling tasks, the device standby time is appropriately extended to save energy. For remote on / off control, based on the specific time schedule of the task, such as starting or stopping sampling in a specific season or time period, the remote on / off control time is precisely set to ensure that the device is turned on or off within the specified time. This establishes a precise identification and control relationship between device control parameters (device standby, sleep wake-up, remote on / off control) and sampling task monitoring parameters, making device operation highly compatible with sampling task requirements.

[0030] Step S400: performing matching analysis with the sampling task monitoring parameters according to the operating status and sampling data, performing control identification using the matching sampling task monitoring parameters and the identification control relationship, and locating the equipment layout position to perform equipment control operations.

[0031] Specifically, the sampling equipment's operating status and sampling data are monitored and collected in real time. The operating status covers key aspects of the equipment, including whether the power supply is stable, whether components (such as sampling pumps and sensors) are functioning properly, and whether the communication module is functioning properly. The sampling data includes various groundwater quality indicators, such as pH, dissolved oxygen (DO), electrical conductivity (EC), temperature, and the concentration of various pollutants. This operating status information and sampling data are meticulously matched and analyzed with pre-set monitoring parameters for the sampling task. For example, the water quality indicators in the sampling data are compared to see if they fall within the monitoring ranges required by the sampling task, and whether the equipment's operating hours in the operating status meet the specified time intervals. The matching results are then combined with previously established identification and control relationships for control identification. If the sampling data indicates that a water quality indicator consistently deviates from the normal range and exceeds a set threshold, the identification and control relationships are used to determine whether the equipment needs to adjust its sampling frequency or depth to obtain more accurate data. If the operating status indicates excessive energy consumption or unstable communication, the identification and control relationships are used to determine whether the equipment's operating mode should be adjusted (e.g., switching from standby to sleep mode or adjusting the remote on / off control strategy). Finally, based on the equipment's location information, with the help of geographic information systems (GIS) or positioning navigation technology, the geographical location of the sampling equipment that has problems or needs adjustment can be accurately located, and the equipment can be remotely controlled to perform corresponding control operations, such as parameter calibration, component replacement, and status adjustment. This ensures that the sampling equipment can always operate accurately according to the requirements of the sampling task, thereby ensuring the effectiveness and reliability of groundwater quality monitoring.

[0032] In one possible implementation, step S100 further includes:

[0033] Step S110: When the sampling device information includes multiple sampling devices, a correlation analysis is performed based on the device layout locations and groundwater sampling tasks to build a device connection network.

[0034] Step S120: Establishing a collaborative control relationship based on the device connecting to the network.

[0035] Step S130: Analyze the constraint relationship of the device control parameters according to the collaborative control relationship.

[0036] Step S140: performing interactive control identification based on the constraint relationship and the identification control relationship, obtaining a collaborative control strategy, and performing collaborative sampling device positioning control operations using the collaborative control strategy.

[0037] Specifically, when multiple sampling devices are present, the first step is to collect the location information of each sampling device. This location information, including latitude and longitude coordinates, is accurately acquired using the device's built-in positioning system (such as a GPS module). Simultaneously, a detailed analysis of the groundwater sampling task is conducted to clarify requirements such as the task objectives, sampling scope, and sampling frequency. Based on the device's location, Geographic Information System (GIS) technology is used to mark each sampling device on a map, visually presenting its distribution. Based on the requirements of the sampling task, such as sampling in a specific area or watershed, it is determined which sampling devices are geographically related, such as those located in the same water system or adjacent monitoring areas. Through this correlation analysis, the relevant sampling devices are connected to form a device connection network. In this network, each sampling device serves as a node, and the connections between devices represent their collaborative relationships within the sampling task, such as shared data and coordinated sampling times, laying the foundation for subsequent collaborative control.

[0038] Based on the constructed device connection network, collaborative control relationships are further established. Connection information between nodes (sampling devices) is extracted from the network. For a single groundwater sampling task, the synchronous influence relationships between sampling devices in the sampling water area are analyzed. For example, for devices located upstream and downstream of the same river, the sampling results of the upstream device may affect the sampling strategy of the downstream device. This allows for the extraction of intra-task collaborative node relationships. Furthermore, considering multiple groundwater sampling tasks, the interactive influence relationships between sampling devices across different tasks are analyzed. For example, sampling devices at the boundary of two adjacent areas need to coordinate sampling times and parameters to avoid duplicate sampling or data conflicts. This allows for the extraction of inter-task coordination node relationships. These intra-task collaborative node relationships and inter-task coordination node relationships are detailedly annotated, including information such as relationship type and impact level. Subsequently, these multiple collaborative relationships are integrated to establish a comprehensive and systematic collaborative control relationship, clarifying how sampling devices work together in different task scenarios to improve overall sampling efficiency and data accuracy.

[0039] Based on the established collaborative control relationship, the constraints of the device control parameters are deeply analyzed. The device standby, sleep wake-up, remote switch control and other device control parameters have mutual constraints and influences in collaborative work scenarios. For example, in a certain period of time, in order to ensure the continuity of the overall sampling task, some devices need to remain in standby mode and ready to respond at any time, while other devices may enter sleep mode according to task priority to save energy, but they must ensure that they can be quickly awakened and work collaboratively when needed. Analyze the time sequence, logical relationship and other constraints between these control parameters of different sampling devices under the collaborative control relationship. Considering factors such as communication delay and data transmission requirements between devices, determine the reasonable value range and change pattern of device control parameters in various collaborative work situations, and provide a basis for achieving accurate interactive control identification.

[0040] The system comprehensively considers the operating status of each sampling device, the sampling data, and the specific requirements of the current sampling task. For example, if the sampling data from a particular device indicates abnormal fluctuations in water quality indicators, the system, combined with the identified control relationships, determines whether the sampling frequency or depth of that device needs to be adjusted. Based on the constraints, the system then analyzes the impact of this adjustment on other cooperating sampling devices, such as whether it will cause data transmission conflicts or disrupted cooperating sampling times. Through complex logical operations and data analysis, the optimal control actions for each device in different situations are determined, resulting in a coordinated control strategy. This strategy specifies in detail when each cooperating sampling device should enter standby, sleep, or wake-up mode, when to remotely power it on and off, and how to adjust sampling parameters (such as flow rate and depth). Then, leveraging device location information, positioning technologies (such as GPS or base station positioning) are used to precisely locate the geographic location of each cooperating sampling device. Finally, based on the coordinated control strategy, each sampling device is precisely positioned and controlled through remote communication (such as 4G / 5G networks) or on-site operations, ensuring that all sampling devices work together in the groundwater sampling task, achieving efficient and accurate water quality monitoring.

[0041] In one possible implementation, step S110 further includes:

[0042] Step S111: extracting sampling range, sampling period, and sampling target parameters according to the groundwater sampling task.

[0043] Step S112: locating the sampling water source according to the equipment layout positions, and establishing an equipment location distribution network according to the interval distances of the layout positions, wherein each sampling device serves as a network node of the equipment location distribution network.

[0044] Step S113: Associating and configuring the sampling device nodes in the device location distribution network according to the sampling range, and marking the sampling task parameters of the sampling device nodes according to the sampling period and sampling target parameters to construct the device connection network.

[0045] Specifically, key information is extracted from received groundwater sampling task instructions or task configuration files through in-depth analysis. By analyzing task descriptions, requirements, and related parameters, the sampling scope is accurately determined, defining the geographic boundaries of the sampling area. For example, for monitoring a specific watershed, groundwater aquifer, or groundwater surrounding an industrial park, the scope may be defined by geographic coordinates, administrative boundaries, or specific hydrogeological units. Furthermore, the sampling period is extracted to determine the intervals at which repeated sampling will be conducted, such as daily, weekly, or monthly. This depends on the monitoring objective. Monitoring the impact of short-term pollution events requires high-frequency sampling, while studying long-term water quality trends requires longer sampling periods. Furthermore, the sampling target parameters are carefully analyzed to identify key groundwater quality indicators for monitoring. These include conventional indicators such as pH, dissolved oxygen (DO), electrical conductivity (EC), and temperature, as well as the concentrations of specific target pollutants, such as heavy metals (lead, mercury, and cadmium) and organic pollutants (benzene, pesticide residues, etc.). These parameters will directly guide the operating mode and data collection priorities of subsequent sampling equipment.

[0046] Based on the equipment layout location data in the sampling equipment information, geographic information system (GIS) technology is used to accurately locate the sampling water source location corresponding to each sampling equipment on the map. This location information is usually presented in the form of longitude and latitude coordinates. By connecting to a geographic information database or map service, the geographical distribution of the sampling equipment can be visualized. Then, based on the interval distance between the layout locations of each sampling equipment, a device location distribution network is constructed. The straight-line distance between each device or the actual path distance based on the topography is calculated, and a network structure that reflects the spatial distribution relationship of the equipment is established with the distance as the edge and the sampling equipment as the node. In this network, sampling equipment that is closer to each other has a stronger correlation in the sampling task. For example, within the same small watershed or on adjacent groundwater flow paths, the data they collect may be more relevant and complementary, providing a geographic spatial infrastructure for subsequent collaborative work and data analysis.

[0047] Sampling device nodes in the device location distribution network are associated and configured to determine whether each sampling device is within the sampling range. If so, it is marked as a valid sampling node. Further analysis is performed on its relationship with other nodes within the sampling range, such as whether it is located in a peripheral area, a central area, or relative to other unique geographical features (such as river confluences or near pollution sources). Detailed sampling task parameters are then annotated for each sampling device node based on the sampling period and sampling target parameters. For the sampling period, the sampling time and frequency requirements for each device are clearly defined, e.g., a device sampling at 9:00 AM every Monday, Wednesday, and Friday. For the sampling target parameters, the specific water quality indicators to be collected and monitored by each device are annotated, along with the corresponding accuracy requirements. For example, device A focuses on monitoring heavy metal concentrations with an accuracy requirement of 0.01 mg / L. This approach integrates sampling task requirements with the device's location and functional characteristics to construct a complete device connection network. Each sampling device has a clear role and collaborative relationships within the network, laying a solid foundation for subsequent collaborative control and data collection.

[0048] In one possible implementation, step S120 further includes:

[0049] Step S121: Based on the same groundwater sampling task, according to the synchronous influence relationship of the sampling water area location, the collaborative node relationship of the same task is extracted.

[0050] Step S122: Analyze the sampling interaction influence relationship of multiple groundwater sampling tasks and extract the interaction task coordination node relationship.

[0051] Step S123: performing relationship annotation on the same-task collaborative node relationship and the interactive task coordination node relationship, integrating multiple collaborative relationships based on the relationship annotation, and establishing the collaborative control relationship.

[0052] Specifically, when performing the same groundwater sampling task, the synchronous influence relationship between the sampling water area locations of each sampling device is studied in depth. By analyzing the groundwater system, including factors such as water flow direction, flow velocity, water level changes, and the layout of the sampling equipment, it is determined which sampling equipment have mutual influence in the same sampling task. For example, for sampling equipment located on the main stream of the same river, the water quality data collected by the upstream equipment will affect the monitoring results of the downstream equipment along with the water flow, and there is data transmission and association between them. Based on this synchronous influence relationship, the collaborative node relationship of the same task is extracted to clarify which equipment needs to work together under the sampling task, such as synchronizing the sampling time to obtain continuous water quality change data of the water flow at different locations, or adjusting the sampling depth or frequency of the downstream equipment according to the upstream water quality changes, thereby ensuring the consistency and effectiveness of the entire sampling task in the same water area, and providing comprehensive data support for the accurate assessment of the groundwater quality status in the area.

[0053] When multiple groundwater sampling tasks are involved, the sampling interaction relationships of the sampling equipment between the tasks should be carefully analyzed. Different sampling tasks may cover different geographical areas, but in border areas or areas with hydraulic connections, there may be interactions between the sampling equipment. For example, in two adjacent groundwater monitoring areas, one task focuses on monitoring the impact of industrial pollution on groundwater, and the other task focuses on agricultural non-point source pollution. At the junction of the two areas, the data collected by the sampling equipment may be affected by the combined effects of the two pollution sources and need to be coordinated and integrated. By analyzing these interaction relationships, the interaction task coordination node relationships are extracted, and it is determined which equipment needs to share data, coordinate sampling times, or adjust sampling parameters between different tasks to avoid duplicate sampling and data conflicts. At the same time, the data complementarity of multi-task sampling is fully utilized to improve the overall sampling efficiency and data quality, providing an accurate basis for the comprehensive assessment of groundwater quality conditions over a larger area.

[0054] Graph theory algorithms are used to process the relationships between nodes involved in same-task collaboration and coordination of interactive tasks to establish collaborative control relationships. First, each sampling device is considered a node in the graph, and relationships between nodes involved in same-task collaboration and coordination of interactive tasks are represented by edges. Relationship annotations use weights to reflect the strength of the relationships. For example, the weight of edges between nodes with strong correlations is set to a high value (e.g., 0.8-1.0), moderate correlations to a medium value (e.g., 0.4-0.7), and weak correlations to a low value (e.g., 0.1-0.3). Furthermore, directional markers are used to indicate the direction of influence (e.g., a one-way arrow indicates one-way influence, and a two-way arrow indicates two-way influence). Based on these annotations, multiple collaborative relationships are integrated. Using a graph traversal algorithm (breadth-first search), starting from the start node, the entire graph is traversed along the edges. During the traversal, the comprehensive influence factor of each node is calculated based on the weights and directions of the edges between nodes. For example, if a node is influenced by multiple adjacent nodes, its comprehensive influence factor is the sum of the adjacent node weights multiplied by the node's own weight adjustment coefficient (determined based on the node's characteristics). Then, based on the comprehensive impact factors, the roles and control strategies of the nodes in collaborative control are determined. Nodes with higher comprehensive impact factors may be identified as key control nodes, and adjustments to their control parameters will have a greater impact on other nodes. Nodes with lower comprehensive impact factors may be more likely to follow the control instructions of key nodes. In this way, a collaborative control relationship is established, clarifying how the various sampling devices work together in different mission scenarios, thereby achieving efficient and accurate groundwater sampling and monitoring.

[0055] In one possible implementation, step S400 further includes:

[0056] Step S410: Acquire weather data, perform time series analysis on the weather data, and determine weather impact time series characteristics.

[0057] Step S420: Acquire the sampling timing constraint characteristics of the weather data and the groundwater sampling task.

[0058] Step S430: performing time alignment on the weather impact timing characteristics and the sampling timing constraint characteristics to identify a device forced sleep timing node, where the device forced sleep timing node is a timing period node where the weather impact exceeds the sampling constraint impact.

[0059] Specifically, by establishing a data transmission channel with a professional meteorological data platform or meteorological monitoring agency, real-time weather data covering multiple parameters, including temperature, humidity, air pressure, wind speed, precipitation probability, and light intensity, with precise timestamps, is acquired. After acquiring the data, it is processed using an algorithm combining the Autoregressive Integrated Moving Average (ARIMA) model with exponential smoothing. For historical weather data, the ARIMA model is used to analyze the key factors and temporal patterns of how different meteorological parameters affect sampling equipment under similar past weather conditions. This model explores long-term trends, seasonal variations, and random fluctuations in the data, identifying temporal correlations between high temperatures and equipment failure frequency. Furthermore, exponential smoothing is used to weight recent real-time weather data, giving higher weight to recent data to more accurately capture current weather trends and predict the direction of various meteorological parameters over the next few hours or days. For example, the team can predict whether the temperature will continue to rise beyond the normal operating range of the equipment and for how long. This analysis identifies the temporal characteristics of weather impacts, including the time period, duration, and rate of change of extreme weather events. This provides a critical basis for subsequent comparison with the temporal constraints of the sampling task and decision-making, ensuring equipment safety and sampling effectiveness.

[0060] Relevant sampling timing constraints are extracted from the pre-set configuration file or task management system for groundwater sampling tasks. The sampling interval is a key parameter. For example, a sampling interval of six hours establishes a basic sampling frequency rhythm, which determines the distribution of the equipment's duty cycles throughout the day. Sampling duration is also crucial. For example, each sampling session must last 15 minutes, which limits the length of time the equipment can be actively collecting data within each sampling cycle. Furthermore, given the dynamic nature of groundwater quality and the timeliness of data processing and transmission, a permissible sampling delay is set. For example, sampling within 10 minutes before or after the specified sampling time is considered valid; sampling outside this range may affect data accuracy and timeliness. Furthermore, in-depth analysis of groundwater quality variations at different time scales is based on long-term research into the hydrogeological conditions of the monitoring area, the emission patterns of surrounding pollution sources, and the hydrodynamic characteristics of the groundwater. For example, in some areas, groundwater levels and quality may fluctuate significantly in the early morning and evening. Therefore, intensive sampling during these times can yield more representative data. During other, more stable periods, the sampling interval can be appropriately adjusted to balance data quality and equipment energy consumption. By integrating the sampling time requirements stipulated by these tasks and the temporal variation characteristics of groundwater quality itself, the sampling timing constraint characteristics between weather data and groundwater sampling tasks are accurately obtained, providing a solid foundation for subsequent comparison and collaborative decision-making with weather-affected timing characteristics, and ensuring that in various complex situations, the sampling work can not only meet the task objectives but also adapt to changes in actual environmental conditions.

[0061] The weather-affected timing features are precisely aligned with the sampling timing constraint features. Using the timeline as a reference, key time nodes in the weather-affected timing features, such as the start and end times of the high-temperature period and the expected arrival and duration of the storm, are mapped one-to-one with the sampling time window and delay time range in the sampling timing constraint features to identify device forced sleep timing nodes. For example, when the predicted intensity of a storm is such that it could flood the sampling equipment, and this period overlaps with the normal execution time of the sampling task, this is determined to be a device forced sleep timing node. Alternatively, in hot weather, if the device's operating ambient temperature is predicted to exceed the device's normal operating tolerance limit for a prolonged period, and sampling during this high-temperature period cannot guarantee data accuracy and poses a high risk of damage to the device, this high-temperature period is also identified as a forced sleep timing node. Once these device forced sleep timing nodes are identified, device control instructions are automatically triggered, putting the sampling equipment into a sleep state to prevent damage from extreme weather conditions and ensure its safety and service life. When weather conditions return to an acceptable range and meet the sampling timing constraints, the device will be awakened according to the preset strategy to continue the groundwater sampling task, thereby maintaining the continuity of the sampling work and data quality to the greatest extent while ensuring the safety of the equipment.

[0062] In one possible implementation, step S140 further includes:

[0063] Step S141: obtaining the time series variation characteristics of the operating parameters of each sampling device, and fitting the benefit loss evaluation relationship of each sampling device in combination with the identification control relationship, wherein the benefit loss evaluation relationship includes the time series loss impact.

[0064] Step S142: establishing a constraint timing relationship of each collaborative sampling device according to the constraint relationship.

[0065] Step S143: performing interest conflict analysis on the interest loss evaluation relationship using the constraint time sequence relationship to obtain an interest conflict time sequence.

[0066] Step S144: performing a balancing process on the interest conflict sequence to obtain the collaborative control strategy.

[0067] Specifically, a high-precision sensor network and intelligent data acquisition modules are used to accurately and in real time capture the operating parameters of each sampling device. These parameters include measuring the voltage and current of the device's power supply using high-precision current transformers and voltage transformers to precisely calculate energy consumption; using rotary encoders to measure motor speed; and frequency sensors to monitor pump operating frequency to reflect operating intensity. Network traffic monitoring technology and data storage and analysis are used to monitor data transmission rate and volume. The collected data is recorded at regular intervals (e.g., every second) and transmitted to a data processing center. At the data processing center, a time series analysis algorithm combining the ARIMA model and wavelet analysis is used to remove noise and accurately identify parameter trends and cyclical characteristics. For example, ARIMA is used to predict energy consumption trends, while wavelet analysis is used to extract cyclical fluctuations in motor speed. Then, the time series variation characteristics of operating parameters are deeply integrated with the identified control relationships using big data processing technology and decision tree regression machine learning algorithms. A model is constructed with control states as branches and operating parameter variation ranges as leaf nodes to explore the impact of correlation patterns on profit losses. Finally, using multiple linear regression or neural network modeling methods, we can quantify the correlation between benefit factors such as energy savings (energy consumption reduction ratio), task completion on time (deviation from the specified time), and equipment health (failure prediction probability) and loss factors such as energy waste (difference in energy consumption exceeding the normal range), task delay (actual and planned time delay), and equipment overwork (duration exceeding a reasonable threshold). The time weighting coefficient is used to highlight the impact of timing loss, providing a basis for subsequent decision-making.

[0068] Based on the constraints on device control parameters identified in the previous analysis, the timing requirements for each collaborative sampling device during sampling task execution were thoroughly analyzed. For synchronization, high-precision clock synchronization technologies (such as the NTP protocol or GPS timing system) were utilized to ensure precise synchronization of multiple devices that needed to initiate sampling tasks simultaneously, with errors controlled within milliseconds. For priority, each device was assigned a clear priority value (e.g., 1-10, with lower values ​​indicating higher priorities) based on the importance and urgency of the sampling task and its impact on the overall monitoring objectives. During task scheduling, sampling operations were prioritized for high-priority devices. For dependency, data links and communication protocols were established to ensure that subsequent devices received trigger signals and initiated their own sampling tasks promptly after the previous device completed sampling and successfully transmitted data. For example, when monitoring the spread of specific contaminants in groundwater, the sampling and analysis results of the upstream device served as the basis for downstream devices to adjust sampling depth and frequency. This approach established a rigorous and orderly network of constrained timing relationships between collaborative sampling devices, ensuring a smooth and logical sampling workflow.

[0069] Based on the constructed constraint temporal relationships and the fitted benefit-loss evaluation relationships, a comprehensive analysis of potential conflicts of interest during the operation of each collaborative sampling device is conducted. To address task delays, a task scheduling simulation algorithm is used to simulate the task execution times of different devices under various possible operational scenarios. When a device's task is delayed beyond the maximum allowed delay due to waiting for data or resources from other devices, this time point and the device combination involved are recorded as part of the conflict of interest temporal sequence. To address resource contention, a resource allocation optimization model is used to analyze the resource usage of devices (such as energy consumption and network bandwidth usage) in real time. When the demand for limited resources by multiple devices simultaneously exceeds the resource supply capacity, the time range and the associated devices are identified as conflict of interest temporal nodes. Regarding priority conflicts, the actual execution order of the devices is checked for consistency with the preset priority order. If a low-priority device preempts the resources or time window of a high-priority device, the corresponding time point and device are marked. This detailed analysis accurately identifies the temporal intervals where conflicts of interest may arise throughout the sampling process, providing clear targets for subsequent balancing.

[0070] Multiple optimization algorithms are employed to balance identified conflicting interest sequences. First, using the Nash equilibrium algorithm from game theory, each sampling device is considered a participant in the game, with its control strategy serving as a strategy choice and its loss of interest as a payoff function. Through iterative calculations, a stable strategy combination is found that minimizes the loss of interest for each device while taking into account the strategies of other devices. Simultaneously, a linear programming and integer programming approach is employed, with minimizing the overall system loss of interest as the objective function and constraints such as the constraint timing and device operating parameters, to determine the optimal device control parameter adjustment scheme. For example, the optimal values ​​for parameters such as standby, wakeup, and sampling frequency for each device are determined at the moment of conflict. Furthermore, the system possesses dynamic adaptive capabilities, updating the constraints and objective function in real time and recalculating the optimal control strategy based on real-time monitored environmental changes (such as sudden water quality anomalies or device failures) and task adjustments (such as the urgent addition of sampling points). This multi-level, dynamic balancing approach ultimately results in a stable and efficient collaborative control strategy, ensuring that each sampling device can work collaboratively to meet its own mission requirements while minimizing conflicts of interest and maximizing the overall system benefit.

[0071] In one possible implementation, step S141 further includes:

[0072] ;

[0073] in, For the system at time Total profit loss, including all equipment Timing loss, data quality loss, and energy loss caused by the deviation of the operating state, is the number of collaborative sampling devices, The device index is used to represent each device. For equipment At the moment The quality of the collected data is in the range of [0, 1]. The closer the value is to 1, the higher the data quality. Deviation loss function factor of the equipment under different operating conditions, is the weight factor of data quality deviation, which indicates the impact of data quality lower than the expected target on the overall loss. For devices At the moment Whether the device is in energy-saving mode, For devices Energy consumption per unit time, Energy saving efficiency factor, which indicates the energy saving ratio of the device when entering standby / sleep state. is the weight factor of the equipment operating state deviation, which indicates the proportion of the loss caused by the deviation when the equipment is running in a non-energy-saving state in the total loss. is the weight factor of energy saving in the total loss, indicating the importance of energy saving. It is the weight factor of equipment deviation loss, indicating the influence of loss caused by operation deviation on total loss.

[0074] Specifically, The expression takes into account all co-sampling devices There are many factors in the operation process. The expression is expressed by the summation symbol The losses of each device are accumulated. At the moment The loss caused by the deviation of its operating state is Measure, where Reflects the deviation between the equipment's operating status and the ideal state. is the deviation loss function factor, Indicates the quality of the data (between 0 and 1, the closer to 1, the higher the quality), is the data quality deviation weight factor, is the weight factor of the equipment operation state deviation; the loss of equipment energy saving is given by express, Indicates whether the device is in energy-saving state. is the energy consumption per unit time, is the energy-saving efficiency factor, To save energy consumption weight factor; other losses caused by equipment operation deviation are used calculate, is the device deviation loss weight factor. This expression comprehensively quantifies the impact of timing loss, data quality loss, and energy loss caused by device operating state deviation on the overall system benefit. It provides a key basis for evaluating device operating efficiency and optimizing control strategies in groundwater quality monitoring. It helps adjust device operating modes based on losses, such as performing maintenance or optimizing parameters when deviation losses are large, and arranging standby sleep when energy conservation is not feasible, to ensure efficient and accurate monitoring. Example 2

[0075] Based on the same inventive concept as the groundwater quality monitoring equipment control method in the above embodiment, Figure 2 As shown, the present application provides a groundwater quality monitoring equipment control system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0076] The sampling device information acquisition module 10 is used to connect to the sampling device and obtain sampling device information, wherein the sampling device information includes the device layout location, operation status, and sampling data.

[0077] The groundwater sampling task acquisition module 20 is used to acquire a groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine the monitoring parameters of the sampling task.

[0078] The span analysis module 30 is used to perform task interval time span analysis based on the sampling task monitoring parameters, establish an identification and control relationship between the device control parameters and the sampling task monitoring parameters, and describe the sampling task monitoring parameters corresponding to the device control parameters, wherein the device control parameters include device standby, sleep wake-up, and remote switch control.

[0079] The equipment control operation module 40 is used to perform matching analysis based on the operating status and sampling data with the sampling task monitoring parameters, perform control identification by matching the sampling task monitoring parameters with the identification control relationship, and locate the equipment layout position to perform equipment control operation.

[0080] Furthermore, the sampling device information acquisition module 10 further includes:

[0081] The device connection network construction unit is used to perform correlation analysis based on the device layout location and groundwater sampling tasks when the sampling device information includes multiple sampling devices, and to construct a device connection network.

[0082] A collaborative control relationship establishing unit is used to establish a collaborative control relationship based on the network connection of the device.

[0083] A constraint relationship parsing unit is used to parse the constraint relationship of the device control parameters according to the collaborative control relationship.

[0084] A collaborative control strategy acquisition unit is configured to perform interactive control identification based on the constraint relationship and the identification control relationship to obtain a collaborative control strategy, and to perform collaborative sampling device positioning control operations using the collaborative control strategy.

[0085] Furthermore, the device connection network construction unit further includes:

[0086] A sampling task execution unit is used to extract sampling range, sampling period, and sampling target parameters according to the groundwater sampling task.

[0087] The device location distribution network establishment unit is used to locate the sampling water source according to the device layout location and establish the device location distribution network according to the interval distance of the layout location, wherein each sampling device serves as a network node of the device location distribution network.

[0088] An association configuration unit is used to associate and configure the sampling device nodes in the device location distribution network according to the sampling range, and to label the sampling task parameters of the sampling device nodes according to the sampling period and sampling target parameters to build the device connection network.

[0089] Furthermore, the collaborative control relationship establishing unit further includes:

[0090] The same-task collaborative node relationship extraction unit extracts the same-task collaborative node relationship based on the same groundwater sampling task and according to the synchronous influence relationship of the sampling water area location.

[0091] The interactive task coordination node relationship extraction unit is used to analyze the sampling interaction influence relationship of multiple groundwater sampling tasks and extract the interactive task coordination node relationship.

[0092] A multi-cooperative relationship integration unit is used to perform relationship annotation on the same-task collaborative node relationship and the interactive task coordination node relationship, perform multi-cooperative relationship integration based on the relationship annotation, and establish the collaborative control relationship.

[0093] Furthermore, the device control operation module 40 also includes:

[0094] The weather impact time series feature determination unit is used to obtain weather data, perform impact time series analysis on the weather data, and determine weather impact time series features.

[0095] A sampling timing constraint feature acquisition unit is used to acquire sampling timing constraint features of the weather data and the groundwater sampling task.

[0096] The device forced sleep timing node identification unit is used to time-align the weather impact timing characteristics and sampling timing constraint characteristics, and identify the device forced sleep timing node. The device forced sleep timing node is a timing period node where the weather impact exceeds the sampling constraint impact.

[0097] Furthermore, the collaborative control strategy acquisition unit further includes:

[0098] A benefit-loss evaluation relationship fitting unit is used to obtain the time-series variation characteristics of the operating parameters of each sampling device, and fit the benefit-loss evaluation relationship of each sampling device in combination with the identification control relationship, wherein the benefit-loss evaluation relationship includes the time-series loss impact.

[0099] A constraint timing relationship establishing unit is configured to establish a constraint timing relationship of each collaborative sampling device according to the constraint relationship.

[0100] The interest conflict time sequence acquisition unit is used to perform interest conflict analysis on the interest loss evaluation relationship by using the constraint time sequence relationship to obtain the interest conflict time sequence.

[0101] A balancing processing unit is used to balance the interest conflict time sequence to obtain the collaborative control strategy.

[0102] Furthermore, the benefit-loss evaluation relationship fitting unit further includes:

[0103] ;

[0104] in, For the system at time Total profit loss, including all equipment Timing loss, data quality loss, and energy loss caused by the deviation of the operating state, is the number of collaborative sampling devices, The device index is used to represent each device. For equipment At the moment The quality of the collected data is in the range of [0, 1]. The closer the value is to 1, the higher the data quality. Deviation loss function factor of the equipment under different operating conditions, is the weight factor of data quality deviation, which indicates the impact of data quality lower than the expected target on the overall loss. For equipment At the moment Whether the device is in energy-saving mode, For equipment Energy consumption per unit time, Energy saving efficiency factor, which indicates the energy saving ratio of the device when entering standby / sleep state. is the weight factor of the equipment operating state deviation, which indicates the proportion of the loss caused by the deviation when the equipment is running in a non-energy-saving state in the total loss. is the weight factor of energy saving in the total loss, indicating the importance of energy saving. It is the weight factor of equipment deviation loss, indicating the influence of loss caused by operation deviation on total loss. Example 3

[0105] Figure 3 This is a structural diagram of an electronic device provided in accordance with a third embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 3 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 3 The bus connection is taken as an example.

[0106] Memory 22, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the groundwater quality monitoring device control method in the embodiments of the present application. Processor 21 executes the software programs, instructions, and modules stored in memory 22 to execute various functional applications and data processing of the computer device, thereby implementing the above-mentioned groundwater quality monitoring device control method.

[0107] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0109] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for controlling groundwater quality monitoring equipment, characterized in that: The method comprises: Connecting to a sampling device and obtaining sampling device information, wherein the sampling device information includes device location, operating status, and sampling data; Obtaining a groundwater sampling task, performing parameter decomposition on the groundwater sampling task, and determining monitoring parameters of the sampling task; Perform task interval time span analysis based on the sampling task monitoring parameters, and establish an identification and control relationship between the device control parameters and the sampling task monitoring parameters, wherein the identification and control relationship describes the sampling task monitoring parameters corresponding to the device control parameters, wherein the device control parameters include device standby, sleep wake-up, and remote switch control; According to the operating status and sampling data, a matching analysis is performed with the sampling task monitoring parameters, and control identification is performed using the matching sampling task monitoring parameters and the identification control relationship, and the equipment layout position is located to perform equipment control operations; The method further comprises: When the sampling equipment information includes multiple sampling equipment, a correlation analysis is performed based on the equipment layout location and groundwater sampling tasks to build a device connection network; Establishing a collaborative control relationship based on the device connection network; Analyzing the constraint relationship of the device control parameters according to the collaborative control relationship; Perform interactive control identification based on the constraint relationship and the identification control relationship to obtain a collaborative control strategy, and use the collaborative control strategy to perform collaborative sampling equipment positioning control operations; The step of establishing a device connection network includes: Extract sampling range, sampling period and sampling target parameters according to the groundwater sampling task; Locating the sampling water source according to the equipment layout positions, and establishing an equipment location distribution network according to the interval distances of the layout positions, wherein each sampling device serves as a network node of the equipment location distribution network; Associating and configuring the sampling device nodes in the device location distribution network according to the sampling range, and marking the sampling task parameters of the sampling device nodes according to the sampling period and sampling target parameters to construct the device connection network; Wherein, establishing a collaborative control relationship according to the device connection network includes: Based on the same groundwater sampling task, the collaborative node relationship of the same task is extracted according to the synchronous influence relationship of the sampling water area location; Analyze the interactive impact of multiple groundwater sampling tasks and extract the coordination node relationships of interactive tasks; Relationship annotation is performed on the same-task collaboration node relationship and the interactive task coordination node relationship, and multiple collaboration relationships are integrated based on the relationship annotation to establish the collaborative control relationship.

2. The groundwater quality monitoring equipment control method according to claim 1, characterized in that: Locating the equipment deployment position and performing equipment control operations, including: Acquire weather data, perform time series analysis on the weather data, and determine time series characteristics of weather impact; Acquiring sampling timing constraint characteristics of the weather data and the groundwater sampling task; The weather impact timing characteristics and the sampling timing constraint characteristics are time-aligned to identify a device forced sleep timing node, where the device forced sleep timing node is a timing period node where the weather impact exceeds the sampling constraint impact.

3. The groundwater quality monitoring equipment control method according to claim 1, characterized in that: Performing interactive control identification based on the constraint relationship and the identified control relationship to obtain a collaborative control strategy includes: Obtaining the time series variation characteristics of the operating parameters of each sampling device, and fitting the benefit loss evaluation relationship of each sampling device in combination with the identification control relationship, wherein the benefit loss evaluation relationship includes the time series loss impact; According to the constraint relationship, establishing a constraint timing relationship of each collaborative sampling device; Performing interest conflict analysis on the interest loss evaluation relationship using the constraint time sequence relationship to obtain an interest conflict time sequence; The conflict of interest time sequence is balanced to obtain the collaborative control strategy.

4. The groundwater quality monitoring equipment control method according to claim 3, characterized in that: The expression of the benefit loss evaluation relationship is: ； in, For the system at time Total profit loss, including all equipment Timing loss, data quality loss, and energy loss caused by the deviation of the operating state, is the number of collaborative sampling devices, The device index is used to represent each device. For devices At the moment The quality of the collected data is in the range of [0, 1]. The closer the value is to 1, the higher the data quality. Deviation loss function factor of the equipment under different operating conditions, is the weight factor of data quality deviation, which indicates the impact of data quality lower than the expected target on the overall loss. For devices At the moment Whether the device is in energy-saving mode, For devices Energy consumption per unit time, Energy saving efficiency factor, which indicates the energy saving ratio of the device when entering standby / sleep state. is the weight factor of the equipment operating state deviation, which indicates the proportion of the loss caused by the deviation when the equipment is running in a non-energy-saving state in the total loss. is the weight factor of energy saving in the total loss, indicating the importance of energy saving. It is the weight factor of equipment deviation loss, indicating the influence of loss caused by operation deviation on total loss.

5. A groundwater quality monitoring equipment control system, characterized in that: The system is used to implement a groundwater quality monitoring equipment control method according to any one of claims 1 to 4, and the system comprises: A sampling device information acquisition module, which is used to connect to the sampling device and obtain sampling device information, including device location, operating status, and sampling data; A groundwater sampling task acquisition module is used to acquire a groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine monitoring parameters of the sampling task; A span analysis module, the span analysis module is used to perform task interval time span analysis based on the sampling task monitoring parameters, establish an identification control relationship between the device control parameters and the sampling task monitoring parameters, the identification control relationship describes the sampling task monitoring parameters corresponding to the device control parameters, wherein the device control parameters include device standby, sleep wake-up, and remote switch control; The equipment control operation module is used to perform matching analysis based on the operating status and sampling data with the sampling task monitoring parameters, perform control identification by matching the sampling task monitoring parameters with the identification control relationship, and locate the equipment layout position to perform equipment control operation.

6. A device, characterized in that The device comprises: processor; a memory for storing instructions executable by the processor; Wherein, the processor is used to execute a groundwater quality monitoring equipment control method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the groundwater quality monitoring equipment control method according to any one of claims 1 to 4.

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