Base station mop cleanliness detection system and method and storage medium

Abstract

The invention discloses a base station mop cleanliness detection system and method and a computer storage medium, and the method comprises the steps: measuring and calculating the relative conductivity in real time through two detection points arranged at the upstream and downstream of a sewage pipeline, monitoring the sewage temperature at the same time, and dynamically calculating the cleanliness index in combination with a built-in cleanliness evaluation model. According to the system, a double-point-position ratio measurement principle is adopted, the influence of environmental interference and sensor drift is effectively eliminated, and judgment consistency under different water temperature conditions is ensured through a temperature compensation mechanism. The system intelligently judges whether cleaning is stopped or not according to the dual conditions of the cleanliness index and the relative conductivity, overcomes the defects that a traditional turbidity detection method is prone to bubble interference, large in response delay and limited in application scene, has the advantages of being accurate in detection, high in stability and capable of saving water and energy, and is particularly suitable for the sewage environment with complex components.

Description

Technical Field

[0001] This invention relates to the field of intelligent cleaning technology, specifically to a base station mop cleanliness detection system, method, and storage medium. Background Technology

[0002] With the popularization of smart homes, robotic vacuum cleaners with automatic cleaning functions have become mainstream in the market. One of their core functions is that the base station automatically cleans the robot's mop, and the decision to terminate the cleaning process directly affects the cleaning effect and resource efficiency. Currently, the cleanliness detection technology in this field mainly includes the following solutions: Timer-based control scheme: This is the most basic scheme, where the base station controls the cleaning process according to a preset fixed duration. This scheme completely ignores the actual degree of dirtiness of the mop, which can easily lead to two situations: excessive consumption of water and electricity when the mop is relatively clean; and poor cleaning due to insufficient cleaning time when the mop is particularly dirty, resulting in "secondary pollution".

[0003] Turbidity sensor-based detection scheme: This is a relatively common improvement scheme. Its principle is to determine the cleanliness level of wastewater by detecting its turbidity using an optical sensor. However, this scheme has inherent technical limitations: Susceptibility to environmental interference: air bubbles in wastewater scatter light, and large suspended solids block the light path, both leading to inaccurate readings and a high false alarm rate. Response delay: Optical detection requires a certain settling time, typically with a delay of 5-10 seconds, making real-time accurate control impossible. Poor applicability: Turbidity detection relies on the light transmittance of the liquid and is only suitable for relatively transparent liquids. For complex wastewater rich in organic matter, dark in color, or completely opaque, the detection effect drops sharply or even fails. Additional power consumption: A continuous illumination source is required, increasing the overall power consumption of the system.

[0004] Single-point conductivity detection-based solutions: Some technologies attempt to utilize the positive correlation between ion concentration and conductivity in wastewater for detection. However, single-point absolute conductivity values ​​are easily affected by environmental factors such as water temperature, pipe material aging, and electrode scaling, resulting in poor stability and requiring frequent calibration. In complex real-world application scenarios, their reliability is insufficient.

[0005] Therefore, providing a base station mop cleanliness detection system, method, and storage medium for solving the above problems is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide a base station mop cleanliness detection system, method, and computer storage medium. This system has a simple structure and safe operation. It employs a dual-point conductivity detection method combined with temperature compensation to solve the technical problems of existing technologies, such as susceptibility to the influence of sewage transparency and bubbles, slow response, and low resistance to environmental interference, resulting in low cleaning effectiveness.

[0007] The technical solution provided by this invention is as follows: A base station mop cleanliness detection system includes: Two conductivity detection electrodes are respectively installed at the upstream and downstream detection points in the sewage pipeline; Temperature sensor for real-time detection of wastewater temperature; The data acquisition module is used to acquire the resistance value of the upstream detection point, the resistance value of the downstream detection point, and the wastewater temperature in real time. The calculation module is used to calculate the relative conductivity based on the resistance value of the upstream detection point and the resistance value of the downstream detection point. The calculation module is also used to calculate and obtain a cleanliness index based on the relative conductivity and the wastewater temperature using a cleanliness assessment model. The judgment control module is used to determine whether the cleanliness index and the relative conductivity both meet the termination conditions. If they do, the cleaning process is terminated.

[0008] Preferably, the conductivity detection electrode is a ring-shaped coaxial electrode made of 316L stainless steel, with an electrode spacing of 5mm, an inner diameter matching the pipe diameter, and a tolerance of ±0.5mm.

[0009] A method for detecting the cleanliness of a base station mop includes the following steps: Upstream and downstream monitoring points are installed in the sewage pipeline; The resistance values ​​of the upstream detection point, the resistance values ​​of the downstream detection point, and the wastewater temperature are acquired in real time. The relative conductivity is calculated based on the resistance values ​​of the upstream detection point and the downstream detection point. Based on the relative conductivity and the wastewater temperature, a cleanliness index is calculated using a cleanliness assessment model. Determine whether both the cleanliness index and the relative conductivity meet the termination conditions. If they do, then terminate the cleaning process.

[0010] Preferably, the formula for calculating the relative conductivity is: ; in, Relative conductivity The resistance value at the upstream detection point. This represents the resistance value at the downstream detection point.

[0011] Preferably, the formula for calculating the cleanliness index is: ; in, Cleanliness index and The first and second feature parameters are obtained through training with historical cleaned data. Relative conductivity Wastewater temperature is used as a direct input variable in the model to achieve compensation.

[0012] Preferably, the step of determining whether both the cleanliness index and the relative conductivity meet the termination condition, and terminating the cleaning process if they do, includes the following steps: Determine whether the cleanliness index is greater than the first cleanliness threshold, and record it as the first determination result; Determine whether the relative conductivity remains within the first conductivity threshold range within the first time window, and record this as the second determination result; If both the first and second judgment results are yes, then the termination condition is met, and the cleaning operation is terminated.

[0013] Preferably, after calculating the cleanliness index based on the relative conductivity and the wastewater temperature using a cleanliness assessment model, the method further includes: If the cleanliness index is less than the second cleanliness threshold after three consecutive washes, the deep cleaning mode is triggered, extending the cleaning time by 30%.

[0014] Preferably, after calculating and obtaining the relative conductivity, the method further includes: Determine whether the relative conductivity is continuously less than the second conductivity threshold within the second time window, and record it as the third determination result; If the third judgment result is yes, it is defined as sensor contamination, and a self-cleaning operation is performed.

[0015] Preferably, the process of acquiring the wastewater temperature in real time further includes: When the temperature of the wastewater is detected to be higher than the preset temperature threshold, the backup electrode group is switched to continue the detection.

[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the base station mop cleanliness detection method described in any of the preceding claims.

[0017] This invention discloses a base station mop cleanliness detection system, comprising: two conductivity detection electrodes respectively installed at upstream and downstream detection points in a sewage pipe, a temperature sensor, a data acquisition module, a calculation module, and a judgment and control module; the data acquisition module acquires real-time data from the two conductivity detection electrodes to obtain the resistance values ​​of the upstream and downstream detection points, and acquires real-time data from the temperature sensor to obtain the water temperature; the calculation module calculates the relative conductivity between the resistance values ​​of the upstream and downstream detection points, and calculates a cleanliness index based on the water temperature and a cleanliness assessment model; the judgment and control module determines whether both the cleanliness index and the relative conductivity meet the stop condition, and terminates the cleaning process when the condition is met.

[0018] This invention obtains "relative conductivity" by calculating the resistance ratio of upstream and downstream electrodes, avoiding misjudgments caused by sensor drift, greatly reducing maintenance frequency and misjudgment rate, and making the system more stable in long-term operation. By introducing a temperature sensor and monitoring wastewater temperature in real time, and by using temperature as a direct input variable in the cleanliness assessment model, the influence of water temperature changes on conductivity measurement is effectively compensated. The detection principle of this invention is based on the ion concentration generated by the dissolution or suspension of stains, rather than optical properties, completely solving the problem of "turbidity sensors" failing in non-transparent liquids in the prior art. By setting dual termination conditions, premature termination or invalid extension caused by instantaneous fluctuations is avoided, making control decisions more cautious and intelligent.

[0019] The present invention also discloses a method for detecting the cleanliness of a base station mop and a computer-readable storage medium. Since they belong to the same technical concept as the system and solve the same technical problem, they should have the same beneficial effects, and will not be described in detail here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of a base station mop cleanliness detection system provided in an embodiment of the present invention; Figure 2 This is a flowchart of a base station mop cleanliness detection method provided in an embodiment of the present invention; Figure 3 This is a flowchart of step S5 provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] The embodiments of this invention are written in a progressive manner.

[0024] This invention provides a method, system, and computer-readable storage medium for monitoring battery leakage. It primarily addresses the technical problems of delayed leakage alarms and inaccurate location in existing technologies.

[0025] like Figure 1 As shown, a base station mop cleanliness detection system includes: Two conductivity detection electrodes are respectively installed at the upstream and downstream detection points in the sewage pipeline; Temperature sensor for real-time detection of wastewater temperature; The data acquisition module is used to acquire the resistance values ​​of upstream and downstream detection points and the wastewater temperature in real time. The calculation module is used to calculate the relative conductivity based on the resistance values ​​of the upstream detection point and the downstream detection point. The calculation module is also used to calculate and obtain the cleanliness index based on the cleanliness assessment model according to the relative conductivity and wastewater temperature; The judgment control module is used to determine whether the cleanliness index and relative conductivity both meet the termination conditions. If they do, the cleaning process is terminated.

[0026] In practical applications, stains (such as dust and food residue) in wastewater release ions when dissolved or suspended, leading to an increase in conductivity. During the cleaning process, as stains are washed away, the conductivity of the wastewater decreases exponentially (experimental data shows that for every 10% increase in cleanliness, conductivity decreases by approximately 15-25 μS / cm). Based on this, two conductivity detection electrodes were installed at upstream and downstream monitoring points in the wastewater pipeline, respectively, along with a temperature sensor in the wastewater within the pipeline. The resistance values ​​at the upstream and downstream monitoring points were collected using a data acquisition module. , and sewage temperature The resistance value is then calculated using the calculation module. , The ratio between the two values ​​is defined as relative conductivity. The calculation module takes relative conductivity and wastewater temperature as inputs and feeds them into the cleanliness assessment model to calculate the cleanliness index. The cleanliness index is determined by the control module. Check whether both the relative conductivity and the termination condition are met. If the termination condition is met, perform the termination cleaning.

[0027] Through the synergistic effect of its unique hardware architecture (dual electrodes + temperature sensing) and intelligent algorithms (ratio calculation + temperature compensation model), it comprehensively solves the technical problems of high misjudgment rate, response delay, narrow applicable scenarios and large resource waste in existing technologies, and realizes intelligent and precise cleaning control, achieving the dual purpose of improving cleaning effect and saving energy and reducing consumption.

[0028] Preferably, the conductivity detection electrode is a ring-shaped coaxial electrode made of 316L stainless steel, with an electrode spacing of 5mm, an inner diameter matching the pipe diameter, and a tolerance of ±0.5mm.

[0029] In practical applications, the coaxial design ensures a more uniform distribution of electric field lines between the electrodes, allowing the measured resistance value to more accurately reflect the overall conductivity of the solution rather than local fluctuations. The annular structure allows wastewater to flow through the center, ensuring full contact with the electrodes. When bubbles or tiny particles flow through, the electric field is distributed around the perimeter, preventing drastic changes in the resistance of the entire measurement circuit even if some areas are momentarily blocked. This effectively suppresses sudden changes in readings and ensures signal stability. The 316L stainless steel material significantly extends the electrode's lifespan in harsh wastewater environments, guaranteeing long-term accuracy of measurement data. 5mm is an optimized empirical value that strikes a good balance between acquiring a sufficiently strong signal (high signal-to-noise ratio) and reducing transient interference. The inner wall of the electrode is flush with the inner wall of the pipe, forming a smooth channel. If the inner diameter of the electrode is too small, it will create throttling, generating turbulence and bubbles. If the inner diameter is too large, it will create steps and dead angles, leading to dirt accumulation and bacterial growth, which will eventually contaminate the electrode surface and affect the measurement. The strict ±0.5mm tolerance ensures the smoothness of this flow channel during manufacturing.

[0030] Through a quadruple design of "ring coaxial structure + 316L material + 5mm spacing + matching inner diameter", the components work together to achieve stable, accurate and long-term reliable conductivity measurement in complex, multiphase and corrosive wastewater environments.

[0031] like Figure 2 As shown, a method for detecting the cleanliness of a base station mop includes the following steps: S1. Install upstream and downstream monitoring points in the sewage pipeline; S2. Real-time acquisition of resistance values ​​at upstream and downstream detection points and wastewater temperature; S3. Calculate the relative conductivity based on the resistance values ​​of the upstream and downstream detection points; S4. Based on the relative conductivity and wastewater temperature, calculate the cleanliness index using the cleanliness assessment model; S5. Determine whether both the cleanliness index and relative conductivity meet the termination conditions. If they do, terminate the cleaning process.

[0032] Steps S1 to S5 detail the specific implementation of the base station mop cleanliness detection method, including setting up two monitoring points (upstream and downstream) in step S1 and collecting resistance values ​​in step S2. , and sewage temperature When step S3 calculates the ratio At this time, most of the common-mode interference mentioned above is canceled out, allowing the system to focus on detecting changes caused by the core variable of the degree of mop stain cleaning. The measurement results are stable and reliable, greatly reducing the false judgment rate; through step S4, the wastewater temperature is... As a direct input variable for the cleanliness assessment model, it eliminates misjudgments of cleanliness caused by changes in water temperature; the dual termination conditions set in step S5—the cleanliness index-related termination condition—ensure that the mop has met the macroscopic cleanliness standard; the relative conductivity-related termination condition ensures that the ion concentration in the wastewater no longer undergoes a trend change, indicating that the cleaning process has reached equilibrium and will not become cleaner.

[0033] The above solution constitutes a complete intelligent closed loop of "perception-computation-decision-execution". It overcomes hardware and environmental interference through relative measurement, ensures environmental adaptability through temperature compensation, achieves accurate status assessment through intelligent models, and finally realizes reliable control through dual-condition execution, thereby achieving intelligent and precise cleaning control and achieving the dual goals of improving cleaning effect and saving energy.

[0034] Preferably, the formula for calculating the relative conductivity is: ; in, Relative conductivity The resistance value at the upstream detection point. This represents the resistance value at the downstream detection point.

[0035] In practical applications, factors such as changes in pipe material, slow surface contamination or aging of electrodes due to long-term use, and slight fluctuations in water flow speed will simultaneously and proportionally affect the absolute resistance values ​​of the two upstream and downstream detection points. By calculating the ratio between the two resistance values, these interferences are filtered out, and only the useful signal caused by the core variable of the change in mop stain concentration is extracted. During the cleaning process, as the clean water rinses, the overall concentration of sewage ions decreases from upstream to downstream. and The absolute values ​​of both will change accordingly, but the trend of the ratio ∆R can more clearly and stably reflect the decreasing trend of wastewater ion concentration during the cleaning process.

[0036] Preferably, the formula for calculating the cleanliness index is: ; in, Cleanliness index and The first and second feature parameters are obtained through training with historical cleaned data. Relative conductivity Wastewater temperature is used as a direct input variable in the model to achieve compensation.

[0037] In practical application, this formula is essentially a logistic regression model, a classic classification algorithm in machine learning. It accurately fits complex, non-linear real-world relationships (the relationship between stains, temperature, and cleanliness) using a mathematical model; it also incorporates wastewater temperature... These are the direct input variables to the model. The parameters are obtained through model training. It will automatically learn and correct the impact of temperature on the final cleanliness judgment; in the dynamic model and The parameters were obtained through training on historical cleaning data, and the training set contained at least 1,000 samples of different stain types.

[0038] The above formula, through a data-driven model and a built-in temperature compensation mechanism, robustly maps the raw, easily disturbed sensor signals into a reliable and intuitive cleanliness assessment, resulting in high cleaning efficiency and low false alarm rate.

[0039] like Figure 3 Preferably, the process involves determining whether both the cleanliness index and relative conductivity meet the termination conditions. If they do, the cleaning process is terminated, including the following steps: A1. Determine whether the cleanliness index is greater than the first cleanliness threshold, and record it as the first judgment result; A2. Determine whether the relative conductivity remains within the first conductivity threshold range within the first time window, and record this as the second determination result; A3. If both the first and second judgment results are yes, then the termination condition is met, and the cleaning operation is terminated.

[0040] Steps A1 to A3 are the specific implementation details of step S5. The first judgment result is determined by whether the cleanliness index is greater than the first cleanliness threshold; the second judgment result is determined by whether the relative conductivity is continuously within the range of the first conductivity threshold for a period of time. Only when both the first judgment result and the second judgment result are yes, the termination condition is defined as being met, and the cleaning operation is terminated.

[0041] In one embodiment, a 5 g / L NaCl solution was used to simulate wastewater for cleaning, and the initial conductivity was stabilized at 12000 μS / cm.

[0042] The first cleanliness threshold is set to 0.9; the first conductivity threshold range is... 5%, with a first time window of 10 seconds; The specific implementation process of steps A1 to A3 is as follows: determine whether the cleanliness index under the current cleaning cycle is greater than 0.9; determine whether the conductivity is stable within 5% for 10 seconds; as shown in the table above, the cleanliness index under the third cleaning cycle is greater than 0.9 and the relative conductivity under the third cleaning cycle is 1.02. Both of these conditions are met, so the termination condition is met and the cleaning operation is terminated.

[0043] Preferably, after calculating the cleanliness index based on the cleanliness assessment model according to the relative conductivity and wastewater temperature, the method further includes: If the cleanliness index is less than the second cleanliness threshold after three consecutive washes, the deep cleaning mode is triggered, extending the cleaning time by 30%.

[0044] In practical application, after step S4, the cleaning time is extended by 30% if the cleanliness index after three consecutive cleanings is less than the second cleanliness threshold.

[0045] In one embodiment, the second cleanliness threshold is 0.7. Since the cleanliness index increases after each cleaning, if the cleanliness index is still less than 0.7 after three consecutive cleanings, it indicates that the cleaning effect is poor and the cleanliness is low. By extending the cleaning time and reducing the number of cleanings, the cleaning efficiency and cleaning effect can be improved.

[0046] Preferably, after calculating and obtaining the relative conductivity, the method further includes: Determine whether the relative conductivity is continuously less than the second conductivity threshold within the second time window, and record this as the third determination result; If the third judgment result is yes, it is defined as sensor contamination, and a self-cleaning operation is performed.

[0047] In practical applications, after step S3, it also includes determining whether the relative conductivity is continuously less than the second conductivity threshold in the second time window, as a third determination result.

[0048] In one embodiment, the second conductivity threshold is 0.3, and the second time window is 15 seconds. If the relative conductivity is consistently less than 0.3 within 15 seconds, sensor contamination is defined, and the corresponding self-cleaning procedure is initiated.

[0049] Preferably, the process of acquiring wastewater temperature in real time also includes: When the detected wastewater temperature exceeds the preset temperature threshold, switch to the backup electrode group to continue detection.

[0050] In practical applications, when the detected wastewater temperature exceeds a preset temperature threshold, the system switches to a backup electrode group for continued monitoring. The preset temperature threshold is 50 degrees Celsius, meaning a redundant electrode group is set up. When the wastewater temperature exceeds 50 degrees Celsius, the system switches to the backup electrode group. The backup electrode group can also be made of heat-resistant materials to prevent temperature-related interference with the resistance value.

[0051] A computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the base station mop cleanliness detection method described above.

[0052] The present invention also discloses a computer-readable storage medium that transforms the aforementioned innovative and complex multi-source sensor fusion identification method from a technical concept into a standardized, replicable, and deployable industrial product.

[0053] One or more embodiments in this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments in this application should be included within the protection scope of this application.

[0054] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0055] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0056] The foregoing provides a detailed description of a base station mop cleanliness detection system, method, and storage medium provided in this application. The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A base station mop cleanliness detection system, characterized in that, include: Two conductivity detection electrodes are respectively installed at the upstream and downstream detection points in the sewage pipeline; Temperature sensor for real-time detection of wastewater temperature; The data acquisition module is used to acquire the resistance value of the upstream detection point, the resistance value of the downstream detection point, and the wastewater temperature in real time. The calculation module is used to calculate the relative conductivity based on the resistance value of the upstream detection point and the resistance value of the downstream detection point. The calculation module is also used to calculate and obtain a cleanliness index based on the relative conductivity and the wastewater temperature using a cleanliness assessment model. The judgment control module is used to determine whether the cleanliness index and the relative conductivity both meet the termination conditions. If they do, the cleaning process is terminated.

2. The base station mop cleanliness detection system as described in claim 1, characterized in that, The conductivity detection electrode is a ring-shaped coaxial electrode made of 316L stainless steel, with an electrode spacing of 5mm and an inner diameter that matches the pipe diameter with a tolerance of ±0.5mm.

3. A method for detecting the cleanliness of a base station mop, characterized in that, Includes the following steps: Upstream and downstream monitoring points are installed in the sewage pipeline; The resistance values ​​of the upstream detection point, the resistance values ​​of the downstream detection point, and the wastewater temperature are acquired in real time. The relative conductivity is calculated based on the resistance values ​​of the upstream detection point and the downstream detection point. Based on the relative conductivity and the wastewater temperature, a cleanliness index is calculated using a cleanliness assessment model. Determine whether both the cleanliness index and the relative conductivity meet the termination conditions. If they do, then terminate the cleaning process.

4. The base station mop cleanliness detection method as described in claim 3, characterized in that, The formula for calculating the relative conductivity is as follows: ； in, The relative conductivity is The resistance value at the upstream detection point. This represents the resistance value at the downstream detection point.

5. The base station mop cleanliness detection method as described in claim 3, characterized in that, The formula for calculating the cleanliness index is as follows: ； in, Cleanliness index and The first and second feature parameters are obtained through training with historical cleaned data. The relative conductivity is Wastewater temperature is used as a direct input variable in the model to achieve compensation.

6. The base station mop cleanliness detection method as described in claim 3, characterized in that, The step of determining whether both the cleanliness index and the relative conductivity meet the termination conditions, and terminating the cleaning process if they do, includes the following steps: Determine whether the cleanliness index is greater than the first cleanliness threshold, and record it as the first determination result; Determine whether the relative conductivity remains within the first conductivity threshold range within the first time window, and record this as the second determination result; If both the first and second judgment results are yes, then the termination condition is met, and the cleaning operation is terminated.

7. The base station mop cleanliness detection method as described in claim 3, characterized in that, After calculating the cleanliness index based on the relative conductivity and the wastewater temperature using a cleanliness assessment model, the method further includes: If the cleanliness index is less than the second cleanliness threshold after three consecutive washes, the deep cleaning mode is triggered, extending the cleaning time by 30%.

8. The method for detecting the cleanliness of a base station mop as described in claim 3, characterized in that, After calculating and obtaining the relative conductivity, the method further includes: Determine whether the relative conductivity is continuously less than the second conductivity threshold within the second time window, and record it as the third determination result; If the third judgment result is yes, it is defined as sensor contamination, and a self-cleaning operation is performed.

9. The method for detecting the cleanliness of a base station mop as described in claim 3, characterized in that, The process of acquiring wastewater temperature in real time also includes: When the temperature of the wastewater is detected to be higher than the preset temperature threshold, the backup electrode group is switched to continue the detection.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the base station mop cleanliness detection method as described in any one of claims 3 to 9.

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