Filter intelligent backwashing control system based on pressure sensor

Abstract

The invention discloses a filter intelligent backwashing control system based on a pressure sensor, and relates to the technical field of intelligent equipment, and the filter intelligent backwashing control system comprises the following steps: S1, the system firstly executes pressure difference reference initialization, and determines a pressure difference reference value; s2, constructing a corrected differential pressure offset model, and determining the current differential pressure offset of the system; s3, constructing a blockage risk assessment model, and pre-judging whether the system needs to start backwashing or not; s4, carrying out segmented pulse backwashing control; and S5, constructing a closed-loop judgment model, and evaluating the actual backwashing effect of each stage. According to the method, a high-credibility differential pressure reference is obtained through a reference calculation model; the blocking trend is tracked in real time through the moving average pressure difference offset; constructing a blockage risk coefficient fusing the offset, the offset rate and the duration, and judging the flushing opportunity in multiple dimensions; a three-section flow is adopted to distribute flushing strength and duration according to needs; and after each section of pulse is finished, the backwashing effect is evaluated in a closed-loop manner through the closed-loop judgment model.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent equipment, and in particular to an intelligent backwashing control system for a filter based on a pressure sensor. Background Art

[0002] Filter backwash control is widely used in water supply and drainage, circulating cooling, chemical industry, and converter station cooling. Traditional devices usually use two types of triggering methods: one is the fixed pressure difference threshold method, when the inlet and outlet pressure difference exceeds the preset value (such as 0.05MPa), a constant intensity and constant duration backwash is performed; the other is the timed flushing method, which performs backwashing indiscriminately after a fixed operating time interval (such as 6h). To improve accuracy, some systems superimpose the influent turbidity or instantaneous flow signal, but the overall situation still remains at the "single indicator, single point judgment" level. This type of solution has the following major shortcomings: Benchmark error and false triggering: The system often directly uses the pressure difference obtained from the first sampling after the device is powered on as the benchmark, neither eliminating startup disturbances nor verifying the flow field steady state, resulting in long-term drift in subsequent judgments. In addition, short-term pressure difference fluctuations caused by water hammer in the water pump, valve switching, or temperature-viscosity changes are often misjudged as blockage, causing meaningless flushing.

[0003] Flushing energy level rigidity and resource waste: Most controllers only support single-stage, single-intensity flushing, and cannot automatically match the power and duration according to the degree of blockage. The phenomenon of over-washing of light blockage and incomplete cleaning of heavy blockage is common, which not only wastes backwash water and electricity, but also increases filter element wear.

[0004] Lack of in-process assessment and early warning: Existing systems usually use "pressure differential reset" or "visually clear wastewater" for post-confirmation after flushing is completed, and cannot dynamically determine whether the flushing process is in place. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a filter intelligent backwashing control system based on a pressure sensor to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a filter intelligent backwash control system based on a pressure sensor, comprising the following steps: S1. After the filter is initially installed or restarted for routine maintenance, the system first performs pressure differential reference initialization to determine the pressure differential reference value; S2. After determining the pressure differential baseline value, a pressure differential offset correction model is constructed. The first-order sliding average of the collected real-time pressure differential sequence is compared with the baseline value to determine the current pressure differential offset of the system. S3, based on the current differential pressure offset, build a blockage risk assessment model, make a pre-judgment on whether the system needs to start backwashing; S4, after entering the backwashing process, segmented pulse backwashing control is performed, and the backwashing process is divided into three stages; S5, after backwashing is completed, a closed-loop judgment model is built to evaluate the actual effect of each stage of backwashing.

[0007] Further optimize the technical solution, in step S1, a reference calculation model is built, the sampling window performs fluctuation analysis on the collected differential pressure in a specified time period, judges whether the current system is in hydraulic steady state, and extracts a representative initial differential pressure value as a differential pressure reference value in the steady state window; The reference calculation model is as follows: Among them, : the average differential pressure in the initial stable window of system operation; : the import and export differential pressure value recorded in each k sampling period; : the set number of samplings; is the standard deviation of differential pressure fluctuation, which is used to measure the sampling fluctuation; At the same time, the system presets a steady state threshold When , the system considers that the sampling window is in steady state, becomes the differential pressure reference value.

[0008] Further optimize the technical solution, in step S2, the correction differential pressure offset model is as follows: Among them, : represents the differential pressure offset at the current time t; : represents the width of the sliding window, representing the number of samplings backtracking from the current time t; : represents the filter import and export differential pressure value collected at the i-th time; : the differential pressure reference value output by step S1, representing the normal differential pressure level of the system under the condition of no blockage and no disturbance; : represents the starting index position of the current sliding time window; The model outputs the offset representing the blockage trend, which is used for subsequent judgment of whether to start backwashing operation.

[0009] Further optimize the technical solution, in step S3, the blockage risk assessment model calculates the blockage risk coefficient at the current time as the criterion for whether to enter the backwashing preparation state; The blockage risk assessment model is as follows: Among them, : indicates the blockage risk coefficient; : the differential pressure offset output by step S2; : indicates the differential pressure offset rate, reflecting the blockage aggravation speed; : indicates the duration of the current differential pressure offset; : indicates the weight coefficient of the differential pressure offset; : indicates the weight coefficient of the offset rate; : indicates the weight coefficient of the duration; At the same time, the system presets a safety threshold Once the output of the model Exceeds the preset safety threshold , the system enters the backwashing preparation state.

[0010] Further optimize the technical solution, when the blockage risk coefficient output by the blockage risk assessment model exceeds the safety threshold, do not immediately execute the flushing action, enter a 30-second critical observation interval, and collect continuous multiple groups of sensing data in the interval; If the differential pressure continues to rise or the water quality deterioration trend is obvious, immediately execute backwashing; If the trend tends to ease or reverse, delay the flushing start, which is used to avoid false triggering caused by process disturbance.

[0011] Further optimize the technical solution, in step S4, the three stages of the backwashing process are high-intensity pulse segment, medium-intensity stripping segment and low-pressure tailing segment, and the duration and power of each pulse are dynamically adjusted according to the size of By constructing a pulse scheduling model.

[0012] Further optimize the technical solution, the pulse scheduling model is as follows: Among them, : indicates the flushing power factor used by the jth backwashing at time t; : represents the duration of the jth backflushing; : represents the power coefficient of the jth backflushing; : represents the time coefficient of the jth backflushing; : the clogging risk coefficient output in step S3; The model dynamically determines the power and duration of each backflushing according to the clogging risk coefficient .

[0013] Further optimization of the technical solution, in step S5, the closed-loop judgment model is as follows: wherein, : represents the cleaning effect factor; : represents the pressure difference offset after backflushing; : represents the concentration of particulate matter in the water flowing out of the blowdown outlet during backflushing; : represents the weight coefficient of the pressure difference offset after backflushing; : represents the weight coefficient of the concentration of particulate matter in the water; At the same time, the system sets a judgment threshold , when , it indicates that the cleaning is up to standard, and the subsequent flushing segment that has not been executed is ended in advance; otherwise, the current segment time is automatically extended, or the segment is re-executed.

[0014] Further optimization of the technical solution, the system is also provided with an acoustic deviation alarm mechanism, which can predict potential clogging events in advance and enter a gentle pre-flushing mode in advance when the pressure difference signal has not been triggered.

[0015] Further optimization of the technical solution, the acoustic deviation alarm mechanism is provided with a micro-acceleration sensor array on the outer wall of part of the key pipe segments including the filter inlet and outlet nipples, which is used to collect the weak acoustic vibration characteristic changes generated when the water flow passes through the filter when the deposition layer is initially formed on the surface of the filter.

[0016] In a second aspect, the embodiments of the present application provide a computer device comprising a memory and a processor, and the memory stores a computer program, wherein the computer program instructions are executed by the processor to realize the steps of the filter intelligent backflushing control system based on the pressure sensor according to the first aspect of the present application.

[0017] In a third aspect, an embodiment of the present application provides a computer readable storage medium, having stored thereon a computer program, wherein the computer program instructs a processor to implement the steps of the filter intelligent backwash control system based on a pressure sensor according to the first aspect of the present application.

[0018] Compared with the prior art, the present application provides a filter intelligent backwash control system based on a pressure sensor, which has the following beneficial effects: The filter intelligent backwash control system based on a pressure sensor obtains a high-credibility differential pressure reference through a reference calculation model, tracks the blocking trend in real time by using a sliding average differential pressure offset, constructs a blocking risk coefficient that fuses the offset, offset rate and duration to determine the washing opportunity in multiple dimensions, and adopts a three-section process to allocate the washing intensity and duration on demand, and evaluates the effect of backwash through a closed-loop judgment model after each pulse ends. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a filter intelligent backwash control system based on a pressure sensor is provided. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0022] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from the description, and those skilled in the art can make similar generalizations without departing from the scope of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. In this specification, "in one embodiment" does not mean the same embodiment, nor does it mean an embodiment that is separate or selectively excluded from other embodiments.

[0024] Embodiment one: Reference Figure 1For the first embodiment of the present application, the embodiment provides a pressure sensor-based filter intelligent backwashing control system, comprising the following steps: S1, after initial installation or routine maintenance restart of the filter, the system first performs differential pressure reference initialization to determine the differential pressure reference value.

[0025] Most filter backwashing control systems currently use fixed differential pressure threshold method or timing trigger method as the backwashing start condition. However, due to the short-term pressure fluctuation caused by installation angle, transient disturbance in water pump starting process or residual water film of filter element during actual installation of the filter system, the initial differential pressure cannot be directly used as the system operation reference value.

[0026] A reference calculation model is constructed, and the collected differential pressure is analyzed for fluctuation in a specified time period in the sampling window to determine whether the current system is in hydraulic steady state, and then a representative initial differential pressure value is extracted in the steady state window as the differential pressure reference value; The reference calculation model is as follows: Among them, : average differential pressure in the initial stable window of system operation.

[0027] : inlet and outlet differential pressure value recorded in each k sampling period, the instantaneous differential pressure value obtained by the pressure sensor, indicating the pressure drop of the current water flow before and after the filter screen.

[0028] : set sampling number, in the initial steady state sampling window, the total number of data points collected, for example, 20 times.

[0029] is the differential pressure fluctuation standard deviation, used to measure the sampling fluctuation, the smaller the value, the more stable the hydraulic system.

[0030] At the same time, the system presets a steady state threshold When , the system considers that the sampling window is in steady state, becomes the differential pressure reference value.

[0031] In use: After the controller is used in the system, the sampling window (for example, 20 times in 10 seconds) is automatically started; the sampling value is accumulated in real time and the mean and standard deviation are calculated; if continuously below the steady state threshold , the current is automatically locked; if the fluctuation exceeds the threshold, the sampling window is automatically restarted to avoid misjudgment due to external disturbance.

[0032] It can not only effectively eliminate the interference of transient disturbances on the benchmark value, but also clearly determine whether the system is in a hydraulically stable operating state, ensuring the final determination of This approach offers statistical stability and engineering credibility. It significantly improves the accuracy of subsequent backwash triggering logic, avoiding issues such as premature flushing of the filter element and waste of backwash water due to initial misjudgment.

[0033] S2. After determining the pressure difference baseline value, analyze the pressure difference deviation trend during operation in real time.

[0034] Most current automatic backwash systems monitor clogging trends using only instantaneous pressure differential comparisons or fixed-time weighted averages during operation, often overlooking the effects of long-term zero-point drift and temperature drift in pressure sensors. Once this drift is combined with the actual pressure differential, the system can easily misjudge. While some improved solutions incorporate sliding averages, these still retain a fixed baseline value, preventing dynamic self-calibration. This results in decreasing accuracy over extended periods of operation.

[0035] In this invention, due to the long-term small fluctuations of the pressure sensor, such as temperature drift and zero drift, which may lead to judgment deviation, a pressure differential offset correction model is constructed. The first-order sliding average of the collected real-time pressure differential sequence is compared with the reference value to determine the current pressure differential offset of the system.

[0036] The corrected differential pressure offset model is shown below: in, : Indicates the pressure difference offset at the current time t.

[0037] : represents the width of the sliding window, which represents the number of samples to be taken at the current time t. It controls the time range used to smooth fluctuations and calculate the average value. For example, if the sample is taken once per second, Indicates that the average pressure difference within the last 10 seconds is used for analysis.

[0038] : represents the filter inlet and outlet pressure difference value collected at the i-th moment, which is the raw data collected in real time by the pressure sensor.

[0039] The pressure differential baseline value output in step S1 represents the normal pressure differential level of the system under unobstructed and undisturbed conditions. It serves as a reference value for comparison with the current average pressure differential to determine whether it deviates from the normal state.

[0040] : Indicates the starting index position of the current sliding time window. This smooths out short-term fluctuations and produces a more stable pressure difference trend. This sliding average is the most commonly used dynamic denoising method for time series data.

[0041] At the same time, the window width Adaptive adjustment can be made according to the flow fluctuation amplitude and the start and stop frequency of the pump: the system automatically shortens the time when it detects that the flow is stable. , improve response speed; extend the time under maintenance or flow rate fluctuation conditions , enhancing anti-interference capabilities. This not only retains the advantage of sliding average in suppressing high-frequency noise, but also offsets the cumulative error caused by sensor slow drift through the reference differential mechanism.

[0042] The model output represents the offset of the blocking trend , which is used to determine whether to start the backwash operation.

[0043] S3. Based on the current differential pressure offset, a blockage risk assessment model is constructed to predict whether the system needs to start backwashing.

[0044] The traditional method only relies on setting the static pressure difference threshold to determine whether to trigger backwash, which is prone to "false washing" or "delayed washing" phenomena.

[0045] The clogging risk assessment model calculates the current clogging risk coefficient as the criterion for whether to enter the backwash preparation state; The congestion risk assessment model is as follows: in, : Indicates the congestion risk coefficient.

[0046] : The pressure difference offset output in step S2.

[0047] : Indicates the offset rate of the pressure difference, which is calculated by comparing two consecutive frames. The value is obtained by numerical differentiation, which reflects the speed of congestion aggravation.

[0048] : Indicates the duration of the current pressure difference deviation, which is counted by the logic counter. The cumulative time that has elapsed since the value of zero was first exceeded, a measure of the persistence of the potential blockage.

[0049] : Represents the weight coefficient of the pressure difference offset. It is initially set between 0.4 and 0.6 and adjusted according to the filter element's sensitivity to the pressure difference. If the filter medium has a fine structure and is prone to clogging, its weight can be appropriately increased.

[0050] : weight coefficient representing the rate of change, suitable to be set in the interval of 0.2-0.4, if the water quality fluctuates greatly or the sediment load may increase suddenly, the value should be increased to enhance the forward-looking response ability of the system.

[0051] : weight coefficient representing the duration, generally set in the range of 0.1-0.3, suitable for the situation where the filter resistance increases slowly but the cumulative effect is obvious, such as industrial reuse water or circulating cooling water system.

[0052] The system presets a safety threshold , once the output of the model exceeds the preset safety threshold , the system enters the backwashing preparation state.

[0053] In use: The controller calculates at each sampling period and compares it with the safety threshold ; If , the system immediately enters the backwashing preparation process; If , it continues to monitor until the trigger condition is met.

[0054] At the same time, according to the historical operation data, when the water quality deterioration leads to the increase of the clogging rate, rapidly increases, so that breaks through the safety threshold quickly, thus realizing "early intervention"; on the contrary, under the condition of low load at night or clean water, the increases slowly, but gradually accumulates, and the system will trigger gently after a long time to prevent excessive washing.

[0055] The model makes the trigger decision focus on the clogging degree, and also takes into account the clogging development trend and the cumulative influence time. In this way, it can intervene in advance before real harm is formed, while avoiding false washing caused by short-term fluctuations or process disturbance; compared with the prior art, it significantly improves the accuracy, forward-looking and resource utilization efficiency of the judgment.

[0056] In the embodiment, when the clogging risk coefficient output by the clogging risk assessment model exceeds the safety threshold, a 30-second critical observation interval is entered without immediately performing the washing action, and a plurality of groups of continuous sensing data are collected in the interval. If the pressure difference continues to rise or the water quality deterioration trend is obvious, backwashing is immediately performed; if the trend tends to be mild or reverses, the washing start is delayed, which is used to avoid false triggering caused by process disturbance.

[0057] The buffer mechanism is equivalent to adding a "logical safety valve" to the system, which can make more prudent decisions based on multi-frame trends and cross signals: it reduces invalid water consumption caused by transient fluctuations, avoids the risk of high load of the filter screen caused by excessive waiting, prolongs the operation and maintenance cycle of the equipment, and significantly saves energy consumption and manual intervention costs.

[0058] S4, after determining that the backwashing process is entered, segmented pulse backwashing control is performed. In order to balance the filter protection, backwashing efficiency and water resource saving, the backwashing process is divided into three stages.

[0059] In the existing industrial filtration system, backwashing usually adopts a fixed intensity + fixed time strategy: once the controller detects that the pressure difference exceeds the threshold, it continuously flushes at a single power for a preset time (such as 60 seconds), or simply divides it into "flushing - sewage" two sections, and the time length and flow rate are unchanged all year round. This "one-size-fits-all" approach cannot adjust the flushing power level flexibly according to the degree of blockage, and often appears the contradiction of over-flushing for light blockage and not cleaning for heavy blockage, which wastes backwashing water and shortens the service life of the filter.

[0060] The three stages of the backwashing process are high-intensity pulse stage, medium-intensity stripping stage and low-pressure tailing stage, which realize "block hard, wash hard, block light, stop at the right moment". This hierarchical response mechanism not only protects the filter screen, but also significantly reduces invalid water consumption; compared with the prior art, it saves more than 30% of water on average and prolongs the service life of the filter by about 20% under the same cleanliness. The duration and power of each pulse are dynamically adjusted according to the size of the pressure difference through the construction of a pulse scheduling model.

[0061] The pulse scheduling model is as follows: wherein, : represents the flushing power factor of the jth backwashing at time t; : represents the duration of the jth backwashing; : represents the power coefficient of the jth backwashing, the initial value is determined by the test curve in the debugging stage and stored in the controller parameter table; : represents the time coefficient of the jth backwashing, the initial value is determined by the test curve in the debugging stage and stored in the controller parameter table. For example, can be set to a small value to avoid filter screen wear caused by long duration of high-intensity stage, can be set to be slightly larger to ensure complete removal of residual particles.

[0062] : the blockage risk coefficient output in step S3;​ The model dynamically determines the power and duration of each backwash segment according to the risk coefficient of blockage.

[0063] The system directly multiplies the power coefficient of each segment to obtain the intensity of the three segments — high intensity, — medium intensity, — low intensity. When the risk is higher, the generated high-intensity pulse can instantly tear through thick layers of scale; the medium-intensity pulse of continuously peels off residual particles; and the low-pressure tailing pulse of clears away fine residues and inhibits redeposition.

[0064] Synchronization with power, the time length of each segment is mapped to . This means that the higher the risk, the longer the total time of the three segments, and the proportional relationship remains consistent; when the risk is low, the total time is automatically shortened to avoid over-washing.

[0065] The three sets of parameters calculated are immediately issued to the execution unit: the variable frequency pump adjusts the speed according to the power instruction, and the electromagnetic valve opens and closes according to the time length instruction; at the same time, these actual execution values are written back to the monitoring log to provide a reference for step S5.

[0066] S5, to avoid the problems of "not washed clean" or "over-washing" during backwashing, after backwashing is completed, a closed-loop judgment model is constructed to evaluate the actual effect of each stage of backwashing.

[0067] The current backwashing system of the filter often relies on a single pressure difference rebound or visual observation of the clarity of the sewage after backwashing: as long as the controller detects that the pressure difference resets to a certain static value, or the water sample is not obviously turbid when the sewage is finished, it is considered that the backwashing is successful. However, this kind of "single indicator-post-determination" method cannot judge whether each pulse segment is truly clean in real time, and it is also difficult to correct the under-washing or over-washing phenomenon in a timely manner, causing water consumption, energy consumption and filter wear to be difficult to optimize.

[0068] In the step S5, the closed-loop judgment model is as follows: wherein, : represents the cleaning effect factor.

[0069] ​​: represents the pressure difference offset after backwashing. The difference between the pressure difference measured at the moment when the system is re-stabilized after the end of the current backwashing stage and the reference pressure difference. It reflects whether the filter screen is effectively cleaned. If the pressure difference drops close to the initial value after backwashing, it means that the blockage has been largely removed. It is calculated by the controller through real-time acquisition of pressure sensor data at the inlet and outlet of the filter.

[0070] : represents the concentration of particulate matter in the water flowing out of the blowdown outlet during backwashing. It is sampled by an online optical turbidimeter or laser particle size meter installed in the backwashing blowdown channel.

[0071] : represents the weight coefficient of the pressure difference offset after backwashing. It is set by test curve or empirical value during the commissioning period, and the typical value is between 0.4 and 0.7.

[0072] : represents the weight coefficient of the concentration of particulate matter in the water. It is set by test curve or empirical value during the commissioning period, and the common setting range is 0.3-0.6.

[0073] At the same time, the system sets a judgment threshold , when , it means that the cleaning is up to standard, and the subsequent backwashing stage that has not been executed is ended in advance; otherwise, the current stage time is automatically extended by 20-40%, or the stage is re-executed. If the pressure difference drops significantly and the pressure difference is still high, the system prompts "possible filter core damage"; if the concentration of particulate matter in the water quickly drops to zero and the concentration of particulate matter in the water remains high, it prompts "possible existence of local hard scale", and manual maintenance is recommended.

[0074] The system calculates the factor immediately after the end of each pulse, and if the cleaning degree has reached the standard, the remaining high-energy pulses are immediately stopped, and if the deviation is still large, the current stage or the stage is repeated as needed. Compared with the prior art, this step realizes in-process evaluation + dynamic correction: not only avoids simple "one hammer" with static threshold, but also adjusts the washing intensity and time length adaptively according to different blockage types, greatly saves backwashing water volume and prevents the filter screen from being overwashed, while ensuring complete cleaning and better system running continuity.

[0075] Embodiment two: The filter intelligent backwashing control system based on pressure sensor described in embodiment one is also provided with an acoustic deviation alarm mechanism in this embodiment, which can predict potential blockage events in advance and enter a gentle pre-washing mode in advance when the pressure difference signal has not been triggered.

[0076] The acoustic deviation alarm mechanism is provided with a micro acceleration sensor array on the outer wall of a partial key pipe section including a filter inlet and outlet short section, and is used to collect weak acoustic vibration characteristic changes generated when water flow passes through the filter. Although the pressure difference has not significantly increased, the fluid disturbance pattern has changed, and characteristic peak value displacement and spectral density decline in the medium and high frequency band spectrum are exhibited.

[0077] Compared with the traditional method of judging whether the filter is blocked by pressure difference or flow, the system is provided with a micro acceleration sensor on the outer wall of a key pipe section to capture medium and high frequency acoustic vibration characteristics generated by the water flow due to micro blockage. When the characteristic peak value migration or energy density attenuation occurs in the acoustic spectrum, even if the pressure difference has not increased, the system will enter the "gentle pre-flushing" or issue a maintenance prompt. In this way, the blockage is identified and disposed in advance: avoiding hasty flushing after the pressure difference increases dramatically and reducing the filter life. Meanwhile, the acoustic vibration signal acquisition is a non-contact monitoring method, which does not increase the additional resistance of the flow channel and does not interfere with the existing pressure and water quality sensors, so that the intelligent level and safety margin of the whole machine are significantly improved.

[0078] Embodiment three The embodiment also provides a computer device suitable for the case of the filter intelligent backwashing control system based on a pressure sensor, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the filter intelligent backwashing control system based on a pressure sensor as proposed in the above embodiment.

[0079] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the filter intelligent backwashing control system based on a pressure sensor as proposed in the above embodiment.

[0080] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the computer device shell, or an external keyboard, touchpad or mouse, etc.

[0081] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0082] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0083] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0084] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, for example, by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technology, known in the art, or combinations thereof, can be used: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0085] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A filter intelligent backwash control system based on pressure sensor, characterized in that: The following steps are involved: S1. After the filter is initially installed or restarted for routine maintenance, the system first performs pressure differential reference initialization to determine the pressure differential reference value; S2. After determining the pressure differential reference value, construct a corrected pressure differential offset model, perform a first-order sliding average on the collected real-time pressure differential sequence, compare it with the reference value, and determine the current pressure differential offset of the system; S3. Based on the current differential pressure offset, a blockage risk assessment model is constructed to predict whether the system needs to start backwashing. S4: After entering the backwash process, segmented pulse backwash control is performed, and the backwash process is divided into three stages; S5. After backwashing is completed, a closed-loop judgment model is constructed to evaluate the actual effect of backwashing at each stage.

2. The filter intelligent backwashing control system based on pressure sensor according to claim 1 is characterized in that: In step S1, a benchmark calculation model is constructed, and a fluctuation analysis is performed on the collected pressure difference within a specified time period in a sampling window to determine whether the current system is in a hydraulic steady state, and then a representative initial pressure difference value is extracted within the steady state window as a pressure difference benchmark value; The benchmark calculation model is as follows: in, : Average pressure difference within the initial stable window of system operation; : The inlet and outlet pressure difference value recorded in each k sampling period; : The set number of sampling times; is the standard deviation of pressure difference fluctuation, which is used to measure sampling volatility; At the same time, the system presets a steady-state threshold ,when When , the system considers that the sampling window is in steady state. This becomes the pressure difference reference value.

3. The filter intelligent backwashing control system based on pressure sensor according to claim 1 is characterized in that: In step S2, the pressure difference offset model is corrected as follows: in, : Indicates the pressure difference offset at the current time t; : represents the width of the sliding window, which represents the number of samples traced back at the current time t; : represents the filter inlet and outlet pressure difference value collected at the i-th moment; : The pressure difference reference value output in step S1 represents the normal pressure difference level of the system under no blockage and no disturbance conditions; : Indicates the starting index position of the current sliding time window; The model output represents the offset of the blocking trend , which is used to determine whether to start the backwash operation.

4. The filter intelligent backwashing control system based on pressure sensor according to claim 1 is characterized in that: In step S3, the blockage risk assessment model calculates the blockage risk coefficient at the current moment as a criterion for whether to enter the backwash preparation state; The congestion risk assessment model is as follows: in, : represents the congestion risk coefficient; : The pressure difference offset output in step S2; : Indicates the deviation rate of pressure difference, reflecting the speed of blockage aggravation; : Indicates the duration of the current pressure difference deviation; : represents the weight coefficient of the pressure difference offset; : represents the weight coefficient of the offset rate; : represents the weight coefficient of duration; At the same time, the system presets a safety threshold Once the model outputs Exceeding the preset safety threshold , the system enters the backwash preparation state.

5. The filter intelligent backwashing control system based on pressure sensor according to claim 4 is characterized in that: When the clogging risk coefficient output by the clogging risk assessment model exceeds the safety threshold, the flushing action is not immediately performed, and a critical observation interval of 30 seconds is entered, during which multiple sets of continuous sensor data are collected; If the pressure difference continues to rise or the water quality deteriorates significantly, backwash immediately; If the trend tends to ease or reverse, the flush start is delayed to avoid false triggering due to process disturbances.

6. The filter intelligent backwashing control system based on pressure sensor according to claim 1 is characterized in that: In step S4, the three stages of the backwash process are high-intensity pulse section, medium-intensity stripping section and low-pressure sweeping section. The duration and power of each pulse are calculated by constructing a pulse scheduling model based on the The size is adjusted dynamically.

7. The filter intelligent backwashing control system based on pressure sensor according to claim 6, characterized in that: The pulse scheduling model is as follows: in, : represents the flushing power factor used in the j-th backwash at time t; : represents the duration of backwashing in the jth section; : represents the power coefficient of the j-th backwash; : represents the time coefficient of backwashing in the jth section; : The congestion risk coefficient output in step S3; The model is based on the congestion risk coefficient To dynamically determine the power and duration of each backwash section.

8. The filter intelligent backwashing control system based on pressure sensor according to claim 1 is characterized in that: In step S5, the closed-loop judgment model is as follows: in, : Indicates the cleaning effect factor; : Indicates the pressure difference offset after backwashing; : Indicates the concentration of particulate matter in the water flowing out of the sewage outlet during backwashing; : represents the weight coefficient of the pressure difference offset after backwashing; : represents the weight coefficient of the concentration of particulate matter in water; At the same time, the system sets the judgment threshold ,when When , it means that the cleaning standard is met and the subsequent flushing segments that have not been executed are terminated in advance; otherwise, the current segment time is automatically extended or the segment is re-executed.

9. The filter intelligent backwashing control system based on pressure sensor according to claim 1, characterized in that: The system is also equipped with an acoustic deviation alarm mechanism, which can predict potential blockage events in advance and enter the gentle pre-flush mode in advance before the pressure difference signal is triggered.

10. The filter intelligent backwashing control system based on pressure sensor according to claim 9, characterized in that: The acoustic deviation alarm mechanism is equipped with a micro-acceleration sensor array on the outer wall of some key pipe sections including the filter inlet and outlet short sections. When a sediment layer is initially formed on the filter surface, it is used to collect the weak acoustic vibration characteristic changes generated when water in the pipeline flows through the filter.

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