Underground water quality monitoring equipment control method, system, equipment and medium

Abstract

The invention relates to the technical field of new-generation information, and discloses an underground water quality monitoring equipment control method, system, equipment and medium, the method comprises the following steps: connecting sampling equipment to obtain sampling equipment information; acquiring an underground water sampling task, and determining sampling task monitoring parameters; task interval time span analysis is carried out, and an identification control relation is established; and performing matching analysis according to the running state, the sampling data and the sampling task monitoring parameters, performing control identification, and positioning the equipment layout position to perform equipment control operation. The technical problems that in the prior art, underground water quality monitoring equipment is high in continuous operation failure rate, large in energy consumption and incapable of achieving centralized management are solved, and the technical effects that equipment standby, sleep awakening, remote control and multi-equipment networking management are achieved, equipment operation stability is improved, energy consumption is reduced, and management efficiency is improved are achieved.

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 an important part of water resources, the water quality of groundwater is of vital importance to the ecological environment, human health and social and economic development. With the increasing attention paid to groundwater protection and management, groundwater quality monitoring has become increasingly critical. Traditional groundwater quality monitoring often relies on manual regular collection of water samples and sending them back to the laboratory for analysis. This method not only consumes a lot of manpower, material resources and time, but also has certain limitations in data timeliness and accuracy. Driven by the development of modern technology, automated groundwater quality monitoring equipment has emerged. For example, some monitoring systems can automatically collect groundwater quality data through sensors and transmit the data to the data processing center for analysis. These systems have made progress in the automation of data collection, reduced manual intervention, and increased the frequency and convenience of data acquisition. However, these existing technologies still have obvious defects in many key aspects. In terms of equipment control, although the equipment can run automatically, it lacks refined operation status management. Most devices cannot flexibly adjust the working mode according to actual monitoring needs. For example, they cannot automatically enter standby or sleep mode between tasks to reduce energy consumption, but continue to be in a high-energy consumption operation state, resulting in serious energy waste. At the same time, the long-term uninterrupted operation of the equipment accelerates the wear of parts and components, causing a significant increase in the failure rate, increasing maintenance costs and equipment downtime, and affecting the continuity of monitoring work. In terms of multi-device collaborative work, the existing technology is relatively weak. Although multiple devices can collect data at the same time, there is a lack of effective coordination mechanism between the devices. They cannot perform intelligent task allocation and resource coordination based on factors such as the overall requirements of the sampling task and the layout location of the equipment, and cannot give full play to the advantages of multi-device collaboration, which reduces the overall efficiency of monitoring work. For example, when facing a large-scale groundwater monitoring area, it is impossible to reasonably arrange the sampling order and frequency of equipment at different locations to achieve efficient monitoring of water quality in the entire area. In addition, in terms of responding to changes in the external environment, the existing technology has not performed well, especially under extreme weather conditions such as heavy rain, floods, high or low temperatures, and the equipment lacks an effective self-protection mechanism. Due to the lack of real-time monitoring and analysis of weather data, the operating status of the equipment cannot be adjusted in time, and the equipment is easily damaged by bad weather, which in turn affects the accuracy and integrity of the monitoring data, and even causes equipment failure, which requires a lot of time and resources to repair, seriously affecting the stability and reliability of the monitoring work.

[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 high failure rate, high energy consumption and inability to centrally manage groundwater quality monitoring equipment during continuous operation.

[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 solution of the present invention is as follows: The present application provides a method for controlling a groundwater quality monitoring device, the method comprising: Connect the sampling device and obtain the sampling device information, wherein the sampling device information 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 control relationship between the device control parameters and the sampling task monitoring parameters, wherein 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; perform matching analysis based on the operating status and sampling data with the sampling task monitoring parameters, perform control identification using the matching sampling task monitoring parameters and the identification control relationship, and locate the device layout to perform device control operations.

[0007] The second aspect of the present application provides a groundwater quality monitoring equipment control system, the system comprising: A sampling device information acquisition module, the sampling device information acquisition module is used to connect the sampling device and obtain the 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 match and analyze the sampling task monitoring parameters based on the operating status and sampling data, perform control identification by matching the sampling task monitoring parameters with the identification control relationship, and locate the device layout position to perform device control operation.

[0008] The third aspect of the present application provides a device, which includes: 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.

[0009] 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 in the present application.

[0010] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: Connect the sampling equipment and obtain the sampling equipment 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 the identification and control relationship between the equipment control parameters and the sampling task monitoring parameters; 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 equipment layout to perform equipment control operations. The technical effects of realizing equipment standby, sleep wake-up, remote control, and multi-device networking management are achieved, which improves the equipment operation stability, reduces energy consumption, and improves management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] 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.

[0012] Figure 1 A schematic flow chart of a groundwater quality monitoring equipment control method provided in an embodiment of the present application.

[0013] Figure 2 A schematic diagram of the structure of a groundwater quality monitoring equipment control system provided in an embodiment of the present application.

[0014] Figure 3 A schematic diagram of the structure of a device provided in this application.

[0015] 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

[0016] In order to more clearly understand the above-mentioned purpose, 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 the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] 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 protection scope of the present invention is not limited to the specific embodiments disclosed below. Example 1

[0018] 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 high failure rate of groundwater quality monitoring equipment during continuous operation, high energy consumption and inability to be centrally managed.

[0019] like Figure 1 As shown, the present application provides a method for controlling groundwater quality monitoring equipment, the method comprising: Step S100: Connect the sampling device and obtain the sampling device information, which includes the device layout location, operation status, and sampling data.

[0020] Specifically, establish a stable connection with the sampling equipment. Establish data links with sampling equipment distributed in different locations through wired or wireless communication methods (such as RS485, Bluetooth, Wi-Fi, 4G / 5G, etc.) to ensure the reliability and real-time nature of data transmission. After the connection is successful, obtain the sampling equipment information. The equipment layout location information obtains accurate latitude and longitude coordinates through the built-in positioning module (such as GPS, Beidou, etc.) of the sampling equipment to clarify its geographical location in the monitoring area, which is crucial for subsequent determination of the sampling range and analysis of regional water quality differences. The operating status information includes whether the equipment is currently operating normally, whether it is in standby, faulty, etc., and is obtained through the equipment's own status monitoring sensors and feedback mechanisms. For example, the circuit monitoring module inside the equipment can provide real-time feedback on power supply, circuit connection, etc. The motor operation sensor can provide the working status of equipment such as sampling pumps. The sampling data is the water quality parameter data obtained after the sampling equipment collects groundwater, such as pH, dissolved oxygen (DO), conductivity (EC), temperature, various ion concentrations, etc. These data directly reflect the water quality of groundwater and provide basic data support for subsequent analysis and decision-making.

[0021] Step S200: Acquire a groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine monitoring parameters of the sampling task.

[0022] Specifically, the groundwater sampling task information is obtained from the task management module of the monitoring system or external input, and then a detailed parameter decomposition is performed. The groundwater sampling task contains a variety of monitoring objectives and requirements. For example, the task stipulates long-term water quality monitoring of groundwater in a specific area to evaluate the trend of groundwater pollution; or short-term high-frequency sampling of groundwater around a suspected pollution source to quickly determine the spread of pollution. In the parameter decomposition process, the sampling task monitoring parameters are determined, such as the sampling time interval (such as hourly, daily, weekly, etc.), which determines the frequency of monitoring groundwater quality changes; the sampling depth range, the groundwater quality at different depths may be different, and the vertical interval of sampling needs to be clarified; the types of water quality indicators to be monitored, and the water quality parameters that need to be focused on (such as heavy metal content, organic pollutant concentration, etc.) are determined according to the task objectives. Through accurate parameter decomposition, the abstract sampling task is converted into specific and operational monitoring parameters, providing clear guidance for subsequent equipment control and data collection.

[0023] Step S300: 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.

[0024] Specifically, an in-depth task interval time span analysis is carried out based on the sampling task monitoring parameters. When the sampling task monitoring parameters indicate that the sampling interval is long, for example, when conducting quarterly or annual long-term water quality trend monitoring, there is no need to perform sampling work for a long time. At this time, consider setting a reasonable sleep time threshold. The setting of this threshold is based on many factors. On the one hand, based on the operating status of the equipment, such as the water level of the sampling equipment has a deep process during sampling, the time required for the water level to stabilize will affect the timing of the equipment entering the sleep state; on the other hand, the time required for the sleep operation and the wake-up operation itself, as well as the time range of the impact of these two operations on the sampling work, are fully considered. For example, it takes a certain amount of time for some sampling devices to wake up from the sleep state to the stable working state. During this period, the data collected may be inaccurate, so this part of the impact time should be taken into consideration. Based on these factors, a sleep time threshold that can effectively save energy without excessively affecting the sampling work is determined, so that the equipment enters the sleep state when there is no sampling task for a long time to reduce energy consumption. At the same time, for different sampling task monitoring parameters, the corresponding equipment control parameter relationship is established. When the task requires high-frequency sampling, such as every hour or shorter time intervals, the equipment control parameters should be set to reduce the standby time, so that the equipment can respond quickly to the sampling task at any time, that is, shorten the conversion time from standby to working state; and for low-frequency sampling tasks, the equipment standby time is appropriately extended to save energy. For remote switch control, according to the specific time schedule of the task, such as starting or stopping sampling in a specific season or time period, the remote switch control time is accurately set to ensure that the equipment is turned on or off within the specified time, thereby establishing an accurate identification and control relationship between the equipment control parameters (equipment standby, sleep wake-up, remote switch control) and the sampling task monitoring parameters, so that the equipment operation is highly adapted to the sampling task requirements.

[0025] Step S400: performing matching analysis with the sampling task monitoring parameters according to the operating status and sampling data, performing control identification by matching the sampling task monitoring parameters with the identification control relationship, and locating the equipment layout position to perform equipment control operation.

[0026] Specifically, the operating status and sampling data of the sampling equipment are monitored and collected in real time. The operating status covers all key aspects of the equipment, including whether the power supply is stable, whether the components (such as sampling pumps, sensors, etc.) are operating normally, and whether the communication module is working properly. The sampling data includes a variety of groundwater quality indicators, such as pH, dissolved oxygen (DO), conductivity (EC), temperature, and concentrations of various pollutants. These operating status information and sampling data are carefully matched and analyzed with the pre-set sampling task monitoring parameters. For example, compare whether the water quality indicators in the sampling data are within the monitoring indicator range required by the sampling task, and check whether the working time of the equipment in the operating state meets the time interval requirements specified in the task. Then, use the matching results and the previously established identification and control relationship for control identification. If the sampling data shows that a water quality indicator continues to deviate from the normal range and exceeds the set threshold, combined with the identification and control relationship, it is determined whether the equipment needs to adjust the sampling frequency or depth to obtain more accurate data; if the operating status shows that the equipment energy consumption is too high or the communication is unstable, it is determined whether to adjust the working mode of the equipment (such as switching from standby to sleep or adjusting the remote switch control strategy) based on the identification and control relationship. Finally, based on the equipment location information, with the help of geographic information system (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 equipment parameter calibration, component replacement, status adjustment, etc., to ensure that the sampling equipment can always operate accurately according to the requirements of the sampling task, and to ensure the effectiveness and reliability of groundwater quality monitoring.

[0027] In a possible implementation, step S100 further includes: Step S110: When the sampling equipment information includes multiple sampling equipment, a correlation analysis is performed according to the equipment layout locations and groundwater sampling tasks, and an equipment connection network is constructed.

[0028] Step S120: Establishing a collaborative control relationship based on the device connecting to the network.

[0029] Step S130: Analyze the constraint relationship of the device control parameters according to the collaborative control relationship.

[0030] 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 equipment positioning control operations using the collaborative control strategy.

[0031] Specifically, when there are multiple sampling devices, the location information of each sampling device is first collected. This location information is accurately obtained through the device's built-in positioning system (such as GPS positioning module), including longitude and latitude coordinates. At the same time, the groundwater sampling task is analyzed in detail to clarify the task objectives, sampling range, sampling frequency and other requirements. Based on the equipment layout location, the geographic information system (GIS) technology is used to mark each sampling device on the map to intuitively present its distribution. According to the requirements of the sampling task, such as sampling in a specific area or basin, determine which sampling devices are geographically related, such as devices located in the same water system or adjacent monitoring areas. Through this correlation analysis, the relevant sampling devices are connected to build a device connection network. In this network, each sampling device is a node, and the connection between devices represents their collaborative relationship in the sampling task, such as sharing data, collaborative sampling time, etc., laying the foundation for subsequent collaborative control.

[0032] Based on the constructed equipment connection network, a collaborative control relationship is further established. The connection information between nodes (sampling devices) is extracted from the network. Based on the same groundwater sampling task, the synchronous influence relationship of each sampling device in the sampling water area is 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, thereby extracting the same-task collaborative node relationship. At the same time, considering the situation of multiple groundwater sampling tasks, the interactive influence relationship between sampling devices of different tasks is analyzed. For example, for sampling tasks in two adjacent areas, the sampling devices at the boundary need to coordinate the sampling time and parameters with each other to avoid repeated sampling or data conflicts, and then extract the interactive task coordination node relationship. These same-task collaborative node relationships and interactive task coordination node relationships are annotated in detail, including relationship type, influence degree and other information. Then, multiple collaborative relationships are integrated to establish a comprehensive and systematic collaborative control relationship, clarifying how each sampling device works together in different task scenarios to improve overall sampling efficiency and data accuracy.

[0033] According to the established collaborative control relationship, the constraint relationship of the device control parameters is deeply analyzed. The device standby, sleep wake-up, remote switch control and other device control parameters have mutual constraints and influences in the collaborative work scenario. 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 state and be ready to respond at any time, while other devices may enter sleep state according to task priority to save energy, but it is necessary to 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 law of device control parameters in various collaborative work situations, and provide a basis for accurate interactive control identification.

[0034] Comprehensively consider the operating status, sampling data and specific requirements of the current sampling task of each sampling device. For example, if the sampling data of a certain device shows abnormal fluctuations in water quality indicators, combined with the identification control relationship, it is determined that the sampling frequency or depth of the device needs to be adjusted; then, based on the constraint relationship, analyze the impact of this adjustment on other collaborative sampling devices, such as whether it will cause data transmission conflicts or collaborative sampling time confusion. Through complex logical operations and data analysis, the optimal control action of each device in different situations is determined to obtain a collaborative control strategy. The strategy specifies in detail when each collaborative sampling device should enter standby, sleep or wake-up state, the control timing of remote switches, and the adjustment method of sampling parameters (such as flow, depth, etc.). Then, using the device layout location information, the geographical location of each collaborative sampling device is accurately locked with the help of positioning technology (such as GPS or base station positioning). Finally, according to the collaborative control strategy, each sampling device is accurately positioned and controlled through remote communication means (such as 4G / 5G network) or on-site operation to ensure that all sampling devices work together in the groundwater sampling task to achieve efficient and accurate water quality monitoring.

[0035] In a possible implementation, step S110 further includes: Step S111: extracting sampling range, sampling period and sampling target parameters according to the groundwater sampling task.

[0036] Step S112: Locate the sampling water source according to the equipment layout positions, and establish 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.

[0037] 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.

[0038] Specifically, from the received groundwater sampling task instructions or task configuration files, in-depth analysis and extraction of key information. Through the analysis of task descriptions, requirements and related parameters, the sampling scope is accurately obtained, and the boundaries of the geographical area covered by this sampling work are clarified. For example, for monitoring of a specific river basin, groundwater aquifer area or groundwater around an industrial park, its scope may be defined by geographical coordinates, administrative area boundaries or specific hydrogeological units. At the same time, the sampling cycle is extracted to determine how long the sampling is repeated, such as daily, weekly, monthly and other different frequencies, which depends on the purpose of monitoring. If the impact of short-term pollution events is monitored, high-frequency sampling is required, while long-term water quality trend research uses a longer sampling cycle. In addition, the sampling target parameters are carefully sorted out to determine the groundwater quality indicators that need to be monitored, including conventional indicators such as pH, dissolved oxygen (DO), conductivity (EC), temperature, etc., as well as the concentrations of specific target pollutants such as heavy metals (lead, mercury, cadmium, etc.) and organic pollutants (benzene, pesticide residues, etc.). These parameters will directly guide the working mode and data collection focus of subsequent sampling equipment.

[0039] Based on the equipment layout location data in the sampling equipment information, the geographic information system (GIS) technology is used to accurately locate the sampling water source location corresponding to each sampling equipment on the map. These location information are usually presented in the form of longitude and latitude coordinates. By connecting with the geographic information database or map service, the geographical distribution of the sampling equipment can be visualized. Then, according to the interval distance between the layout locations of each sampling equipment, the equipment 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 with a closer distance has a stronger correlation in the sampling task. For example, in 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.

[0040] The 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 it is within the range, it is marked as a valid sampling node, and its relationship with other nodes in the sampling range is further analyzed, such as whether it is in the edge area, the central area, or the relative position relationship with other special geographical features (such as the confluence of rivers, near pollution sources, etc.). Then, according to the sampling cycle and sampling target parameters, detailed sampling task parameters are marked for each sampling device node. For the sampling cycle, the sampling time point and frequency requirements of each device are clearly defined, for example, a certain device samples at 9 am every Monday, Wednesday, and Friday; for the sampling target parameters, the specific water quality indicators and corresponding accuracy requirements that each device needs to collect and monitor are marked, such as device A focuses on monitoring heavy metal concentrations, and the accuracy requirement is 0.01 mg / L. In this way, the requirements of the sampling task are combined with the geographical location and functional characteristics of the equipment to build a complete equipment connection network, so that each sampling device has a clear task role and collaborative relationship in the network, laying a solid foundation for subsequent collaborative control and data collection.

[0041] In a possible implementation, step S120 further includes: Step S121: Based on the same groundwater sampling task, extract the collaborative node relationship of the same task according to the synchronous influence relationship of the sampling water area location.

[0042] Step S122: Analyze the sampling interaction influence relationship of multiple groundwater sampling tasks, and extract the interaction task coordination node relationship.

[0043] Step S123: perform relationship annotation on the same-task collaborative node relationship and the interactive task coordination node relationship, integrate multiple collaborative relationships based on the relationship annotation, and establish the collaborative control relationship.

[0044] Specifically, when performing the same groundwater sampling task, the synchronous influence relationship between the sampling waters where each sampling device is located is studied in depth. Through the analysis of the groundwater hydrological 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 has 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 same task collaborative node relationship 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, so as to ensure the consistency and effectiveness of the entire sampling task in the same waters, and provide comprehensive data support for the accurate assessment of the groundwater quality status in the area.

[0045] 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 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 two pollution sources and need to be coordinated and integrated. By analyzing these interaction relationships, extracting the coordination node relationships of interactive tasks, determining which equipment needs to share data, coordinate sampling time, or adjust sampling parameters between different tasks, so as to avoid repeated sampling and data conflicts, and at the same time make full use of the data complementarity of multi-task sampling, improve the overall sampling efficiency and data quality, and provide an accurate basis for the comprehensive assessment of groundwater quality conditions in a larger range.

[0046] Graph theory algorithms are used to process the same-task collaborative node relationship and the interactive task coordination node relationship to establish a collaborative control relationship. First, each sampling device is regarded as a node in the graph, and the same-task collaborative node relationship and the interactive task coordination node relationship are represented by edges. For relationship annotation, weight values ​​are used to reflect the strength of the relationship. For example, the weight of the edge between strongly correlated nodes is set to a higher value (such as 0.8-1.0), the weight of the middle correlation is set to a middle value (such as 0.4-0.7), and the weight of the weak correlation is set to a lower value (such as 0.1-0.3); at the same time, the direction mark is used to indicate the direction of influence (such as a one-way arrow for one-way influence, and a two-way arrow for two-way influence). Based on these annotations, multiple collaborative relationships are integrated. Using the graph traversal algorithm (breadth-first search), starting from the starting node, the entire graph is traversed along the edges. During the traversal process, the comprehensive influence factor of each node is calculated according to the weight and direction of the edges between nodes. For example, a node is affected by multiple adjacent nodes, and its comprehensive influence factor is the sum of the weights of the adjacent nodes multiplied by its own weight adjustment coefficient (determined according to the characteristics of the node itself). Then, according to the comprehensive influencing factors, the roles and control strategies of the nodes in collaborative control are determined. Nodes with higher comprehensive influencing factors may be identified as key control nodes, and the adjustment of their control parameters will have a greater impact on other nodes; while nodes with lower comprehensive influencing factors may follow the control instructions of key nodes more. In this way, a collaborative control relationship is established to clarify how each sampling device works together in different task scenarios to achieve efficient and accurate groundwater sampling and monitoring.

[0047] In a possible implementation, step S400 further includes: Step S410: Acquire weather data, perform time series analysis on the weather data, and determine the time series characteristics of weather impact.

[0048] Step S420: Acquire the sampling timing constraint characteristics of the weather data and the groundwater sampling task.

[0049] Step S430: timing-align the weather impact timing characteristics and the sampling timing constraint characteristics to identify the 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.

[0050] Specifically, by establishing a data transmission channel with a professional meteorological data platform or meteorological monitoring agency, weather data with precise timestamps and covering multiple parameters such as temperature, humidity, air pressure, wind speed, precipitation probability, and light intensity can be obtained in real time. After obtaining the data, the data is processed using an algorithm that combines the autoregressive moving average (ARIMA) model with the exponential smoothing method. For historical weather data, the ARIMA model is used to analyze the key factors and time patterns of the impact of different meteorological parameters on sampling equipment under similar weather conditions in the past, to mine the long-term trends, seasonal changes, and random fluctuations in the data, and to determine the time correlation pattern between high temperature weather and equipment failure frequency. At the same time, the exponential smoothing method is used to weight the recent real-time weather data, giving the recent data a higher weight, so as to more accurately capture the current weather change trend and predict the change trend of various meteorological parameters in the next few hours or days, such as predicting whether the temperature will continue to rise beyond the normal working range of the equipment and how long it will last. Through the above analysis, the time series characteristics of weather impact are determined, including information such as the time period, duration, and rate of change of extreme weather, which provides a key basis for subsequent comparison and decision-making with the time series constraint characteristics of the sampling task, and ensures the safety of equipment and the effectiveness of sampling work.

[0051] Extract relevant sampling timing constraint information from the preset configuration file or task management system of the groundwater sampling task. Among them, the sampling time interval is one of the key parameters. For example, sampling is required to be performed every 6 hours, which determines a basic sampling frequency rhythm and determines the distribution of the working cycle of the equipment within a day. At the same time, the sampling duration is also crucial. For example, each sampling process must last for 15 minutes, which limits the length of time the equipment is in an active collection state during each sampling cycle. In addition, considering the dynamic characteristics of groundwater quality and the timeliness requirements of data processing and transmission, the allowable sampling delay time will be set. For example, sampling within 10 minutes before and after the specified sampling time point is still considered valid. Exceeding this range may affect the accuracy and timeliness of the data. On the other hand, in-depth analysis of the changing laws of groundwater quality at different time scales is based on long-term research on the hydrogeological conditions of the monitoring area, the emission laws of surrounding pollution sources, and the hydrodynamic characteristics of groundwater. For example, the groundwater level and water quality in some areas may fluctuate greatly in the early morning and evening, so intensive sampling during these periods can obtain more representative data; in other relatively stable periods, the sampling interval can be appropriately adjusted to balance data quality and equipment energy consumption. Taking into account the sampling time requirements stipulated in 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.

[0052] The weather-affected timing features and the sampling timing constraint features are precisely aligned. Based on the time axis, the key time nodes in the weather-affected timing features, such as the start and end time of the high temperature period, the expected arrival time and duration of the storm, etc., are matched one by one with the sampling time window and delay time range in the sampling timing constraint features to identify the equipment forced sleep timing nodes. For example, when the expected intensity of the storm reaches the level that may flood the sampling equipment, and this period overlaps with the normal execution time of the sampling task, it is determined to be a forced sleep timing node for the equipment; or in hot weather, if it is predicted that the operating environment temperature of the equipment will exceed the upper limit of the normal operation of the equipment for a long time, and sampling during this high temperature period cannot guarantee data accuracy, and the risk of damage to the equipment is extremely high, then this high temperature period is also determined as a forced sleep timing node. Once these equipment forced sleep timing nodes are identified, the equipment control instructions will be automatically triggered to put the sampling equipment into a sleep state to avoid damage to the equipment from extreme weather and ensure the safety and service life of the equipment. 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.

[0053] In a possible implementation, step S140 further includes: Step S141: acquiring 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.

[0054] Step S142: establishing a constraint timing relationship of each collaborative sampling device according to the constraint relationship.

[0055] Step S143: Utilizing the constraint time sequence relationship to perform interest conflict analysis on the interest loss evaluation relationship to obtain an interest conflict time sequence.

[0056] Step S144: performing a balancing process on the interest conflict sequence to obtain the collaborative control strategy.

[0057] Specifically, with the help of high-precision sensor networks and intelligent data acquisition modules, the operating parameters of each sampling device are obtained in real time and accurately. These parameters include measuring the voltage and current of the equipment power supply through high-precision current transformers and voltage transformers to accurately calculate energy consumption; using rotary encoders to obtain motor speed, frequency sensors to monitor pump operating frequency to reflect work intensity; relying on network traffic monitoring technology and data storage record analysis to master data transmission rate and data volume. The collected data is recorded and transmitted to the data processing center at fixed time intervals (such as every second). In the data processing center, the time series analysis algorithm combining the ARIMA model and wavelet analysis is used to accurately identify parameter change trends and periodic characteristics after removing noise, such as predicting energy consumption trends through ARIMA and extracting motor speed periodic fluctuations through wavelet analysis. Then, the time series change characteristics of the operating parameters and the identification control relationship are deeply integrated using big data processing technology and decision tree regression machine learning algorithms, and a model is constructed with the control state as the decision tree branch and the range of operating parameter changes as the leaf node to explore the impact of the association pattern on the loss of benefits. Finally, the multivariate linear regression or neural network modeling method is used to quantify the benefit factors such as associated energy savings (energy consumption reduction ratio), task completion on time (deviation from the specified time), equipment health (failure prediction probability) and loss factors such as energy waste (energy consumption difference beyond the normal range), task delay (actual and planned time delay), equipment overwork (exceeding the reasonable threshold time), etc., and the time weighting coefficient is used to highlight the impact of timing loss, providing a basis for subsequent decision-making.

[0058] According to the constraint relationship of the equipment control parameters obtained from the previous analysis, the time sequence requirements of each collaborative sampling device during the execution of the sampling task are deeply analyzed. For the synchronization relationship, with the help of high-precision clock synchronization technology (such as NTP protocol or GPS timing system), multiple devices that need to start sampling tasks at the same time are ensured to be accurately synchronized in time, and the error is controlled within milliseconds. For the priority relationship, according to the importance and urgency of the sampling task and the degree of impact on the overall monitoring target, a clear priority value (such as 1-10, the smaller the value, the higher the priority) is set for each device. In the task scheduling process, high-priority devices are given priority to perform sampling operations. As for the dependency relationship, by establishing a data link and communication protocol, it is ensured that after the previous device completes sampling and successfully transmits data, the next device can receive the trigger signal in time and start its own sampling task. For example, when monitoring the diffusion of specific pollutants in groundwater, the results of the sampling analysis of the upstream device are used as the basis for the downstream device to adjust the sampling depth and frequency. In this way, a rigorous and orderly constraint timing relationship network between the collaborative sampling devices is constructed to ensure the smoothness and logic of the entire sampling workflow.

[0059] Based on the constructed constraint timing relationship and the fitted benefit loss evaluation relationship, a comprehensive analysis is conducted on the potential conflicts of interest of each collaborative sampling device during operation. In view of task delay, the task scheduling simulation algorithm is used to simulate the task execution time of different devices in various possible operation scenarios. When it is found that a device is delayed due to waiting for data or resources from other devices, which exceeds the maximum allowed delay time, the time point and the device combination involved are recorded as part of the conflict of interest timing. For the resource contention problem, the resource usage of the device (such as energy consumption, network bandwidth occupancy, etc.) is monitored in real time and analyzed using the resource allocation optimization model. When the demand for limited resources by multiple devices at the same time exceeds the resource supply capacity, the time range and related devices at this time are determined as the conflict of interest timing nodes. In terms of priority conflicts, check whether the actual execution order of the devices is consistent with the preset priority order. If a low-priority device occupies the resources or time window of a high-priority device, mark the corresponding time point and device. Through this meticulous analysis, the timing interval where conflicts of interest may occur in the entire sampling process can be accurately identified, providing a clear goal for subsequent balancing processing.

[0060] For the identified conflict of interest sequence, a variety of optimization algorithms are used for equilibrium processing. First, the Nash equilibrium algorithm in game theory is used to treat each sampling device as a game participant, its control strategy as a strategy choice, and the loss of interest as a benefit function. Through iterative calculation, a stable strategy combination is found so that each device can minimize its own loss of interest while considering the strategies of other devices. At the same time, combined with linear programming and integer programming methods, the objective function is to minimize the overall loss of interest of the system, and the constraint timing relationship, equipment operating parameters, etc. are used as constraints to solve the optimal device control parameter adjustment scheme. For example, the optimal values ​​of parameters such as standby, wake-up, and sampling frequency of each device at the moment of conflict of interest are determined. In addition, the system also has dynamic adaptive capabilities. According to the real-time monitored environmental changes (such as sudden abnormalities in water quality, equipment failures, etc.) and task demand adjustments (such as urgent addition of sampling points), the constraints and objective functions are updated in real time, and the optimal control strategy is recalculated. Through this multi-level and dynamic equilibrium processing method, a stable and efficient collaborative control strategy is finally obtained to ensure that each sampling device can not only meet its own task requirements in collaborative work, but also minimize conflicts of interest to the greatest extent, and maximize the overall benefits of the system.

[0061] In a possible implementation, step S141 further includes: ; 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 The energy consumption per unit time, is the energy-saving efficiency factor, which indicates the energy saving ratio of the device when entering the standby / sleep state. is the weight factor of the equipment operation state deviation, which indicates the proportion of the loss caused by the deviation in the total loss when the equipment is running in a non-energy-saving state. 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.

[0062] 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 operating status and the ideal status. is the deviation loss function factor, Indicates data quality (between 0 and 1, the closer to 1, the higher the quality), is the data quality deviation weight factor, is the weight factor of equipment operation status 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 equipment deviation loss weight factor. This expression comprehensively quantifies the impact of timing loss, data quality loss and energy loss caused by equipment operating state deviation on the overall benefits of the system, and provides a key basis for the system to evaluate equipment operating benefits and optimize control strategies in groundwater quality monitoring. It helps to adjust the equipment working mode according to the loss situation, such as performing maintenance or optimizing parameters when the deviation loss is large, and reasonably arranging standby sleep when energy is not saved, so as to ensure efficient and accurate monitoring work. Example 2

[0063] Based on the same inventive concept as the groundwater quality monitoring device control method in the above-mentioned embodiment, Figure 2 As shown, the present application provides a groundwater quality monitoring equipment control system, and the system and method embodiments in the present application embodiments are based on the same inventive concept. The system includes: The sampling device information acquisition module 10 is used to connect to the sampling device and obtain the sampling device information. The sampling device information includes the device layout location, operation status, and sampling data.

[0064] The groundwater sampling task acquisition module 20 is used to acquire the groundwater sampling task, perform parameter decomposition on the groundwater sampling task, and determine the monitoring parameters of the sampling task.

[0065] The span analysis module 30 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, 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.

[0066] The equipment control operation module 40 is used to perform matching analysis with the sampling task monitoring parameters according to the operating status and sampling data, 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.

[0067] Furthermore, the sampling device information acquisition module 10 also includes: The device connection network construction unit is used to construct a device connection network by performing correlation analysis based on the device layout location and groundwater sampling tasks when the sampling device information includes multiple sampling devices.

[0068] A collaborative control relationship establishing unit, wherein the collaborative control relationship establishing unit is used to establish a collaborative control relationship according to the network connection of the device.

[0069] A constraint relationship parsing unit, wherein the constraint relationship parsing unit is used to parse the constraint relationship of the device control parameters according to the collaborative control relationship.

[0070] 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 equipment positioning control operations using the collaborative control strategy.

[0071] Furthermore, the device connection network construction unit also includes: A sampling task execution unit is used to extract sampling range, sampling period and sampling target parameters according to the groundwater sampling task.

[0072] The device location distribution network establishment unit is used to locate the sampling water source according to the device layout position, and establish the device location distribution network according to the interval distance of the layout position, wherein each sampling device serves as a network node of the device location distribution network.

[0073] 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.

[0074] Furthermore, the collaborative control relationship establishing unit also includes: 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.

[0075] 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.

[0076] A multi-cooperative relationship integration unit, wherein the multi-cooperative relationship integration 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.

[0077] Furthermore, the device control operation module 40 also includes: 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.

[0078] A sampling timing constraint feature acquisition unit is used to acquire sampling timing constraint features of the weather data and the groundwater sampling task.

[0079] The device forced sleep timing node identification unit is used to perform timing alignment on the weather impact timing characteristics and sampling timing constraint characteristics, and identify the device forced sleep timing node, wherein the device forced sleep timing node is a timing period node where the weather impact exceeds the sampling constraint impact.

[0080] Furthermore, the collaborative control strategy acquisition unit further includes: 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 impact of time series loss.

[0081] A constraint timing relationship establishing unit is used to establish a constraint timing relationship of each collaborative sampling device according to the constraint relationship.

[0082] 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.

[0083] A balancing processing unit is used to balance the conflict of interest timing to obtain the collaborative control strategy.

[0084] Furthermore, the benefit loss evaluation relationship fitting unit further includes: ;

[0085] 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 The energy consumption per unit time, is the energy-saving efficiency factor, which indicates the energy saving ratio of the device when entering the standby / sleep state. is the weight factor of the equipment operation state deviation, which indicates the proportion of the loss caused by the deviation in the total loss when the equipment is running in a non-energy-saving state. 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

[0086] Figure 3 The schematic diagram of the structure of the electronic device provided in the third embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode 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 example of connecting through bus is taken in the following.

[0087] The memory 22 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to a groundwater quality monitoring device control method in an embodiment of the present application. The processor 21 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 22, that is, realizing the above-mentioned groundwater quality monitoring device control method.

[0088] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0090] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for controlling groundwater quality monitoring equipment, characterized in that: The method comprises: Connecting a sampling device to obtain 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; According to the sampling task monitoring parameters, a task interval time span analysis is performed to establish an identification control relationship between the device control parameters and the sampling task monitoring parameters, wherein 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; According to the operating status and sampling data, a matching analysis is performed with the sampling task monitoring parameters, 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.

2. The groundwater quality monitoring equipment control method according to claim 1, characterized in that: The method further comprises: When the sampling equipment information includes multiple sampling equipment, a correlation analysis is performed according to the equipment layout location and the groundwater sampling task, and an equipment connection network is constructed; Establishing a collaborative control relationship according to the device connection network; According to the collaborative control relationship, analyzing the constraint relationship of the device control parameters; Interactive control identification is performed based on the constraint relationship and the identification control relationship to obtain a collaborative control strategy, and the collaborative control strategy is used to perform collaborative sampling equipment positioning control operations.

3. The groundwater quality monitoring equipment control method according to claim 2, characterized in that: The step of constructing 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 location, and establishing an equipment location distribution network according to the spacing distance of the layout locations, wherein each sampling device serves as a network node of the equipment location distribution network; The sampling device nodes in the device location distribution network are associated and configured according to the sampling range, and the sampling task parameters of the sampling device nodes are marked according to the sampling period and sampling target parameters to build the device connection network.

4. The groundwater quality monitoring equipment control method according to claim 3, characterized in that: According to the device connection network, a collaborative control relationship is established, including: 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 sampling interaction influence relationship of multiple groundwater sampling tasks and extract the coordination node relationship of interactive tasks; The same-task collaborative node relationship and the interactive task coordination node relationship are labeled, and multiple collaborative relationships are integrated based on the relationship labels to establish the collaborative control relationship.

5. The groundwater quality monitoring equipment control method according to claim 1, characterized in that: Locating the equipment deployment position to perform 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 the 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.

6. The groundwater quality monitoring equipment control method according to claim 2, characterized in that: Performing interactive control identification based on the constraint relationship and the identified control relationship to obtain a collaborative control strategy includes: Acquire 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; According to the constraint relationship, establishing a constraint timing relationship of each collaborative sampling device; Using the constraint time sequence relationship to perform interest conflict analysis on the interest loss evaluation relationship to obtain the interest conflict time sequence; The conflict of interest time sequence is balanced to obtain the collaborative control strategy.

7. The groundwater quality monitoring equipment control method according to claim 6, 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 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 The energy consumption per unit time, is the energy-saving efficiency factor, which indicates the energy saving ratio of the device when entering the standby / sleep state. is the weight factor of the equipment operation state deviation, which indicates the proportion of the loss caused by the deviation in the total loss when the equipment is running in a non-energy-saving state. 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.

8. 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 7, and the system comprises: A sampling device information acquisition module, which 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; A groundwater sampling task acquisition module, which 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; A span analysis module, the span analysis module is used to perform task interval time span analysis according to 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 with the sampling task monitoring parameters according to the operating status and sampling data, 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.

9. 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 7.

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

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