Remote start-stop control system of pneumatic diaphragm pump

Abstract

The invention relates to a remote start-stop control system of a pneumatic diaphragm pump, and belongs to the technical field of pneumatic equipment control. According to the system, a remote instruction interaction module receives a start-stop instruction and authority verification information, and generates a control instruction packet with a timestamp through priority marking and encryption processing; the state sensing feedback module obtains multi-dimensional data and generates a data set through multi-source fusion and signal filtering processing; the local core control module completes instruction verification decryption and priority scheduling, generates a pneumatic driving signal, dynamically adjusts an air pressure safety threshold in combination with historical data and real-time working conditions, triggers an emergency stop instruction through data comparison and generates a fault code; the pneumatic execution driving module switches on and off of an air channel based on the pneumatic driving signal and adjusts pressure according to a preset stepless pressure adjusting mechanism. Safe remote start-stop control over the pneumatic diaphragm pump is achieved, the control accuracy, the working condition adaptability and the operation stability are improved, and the equipment damage risk is reduced.

Description

Technical Field

[0001] This invention belongs to the field of pneumatic equipment control technology, specifically relating to a remote start-stop control system for a pneumatic diaphragm pump. Background Technology

[0002] Pneumatic diaphragm pumps are widely used in media conveying applications across various industries, including chemical, environmental protection, and mining, due to their simple structure, strong corrosion resistance, and ability to transport media containing particulate matter (or fibers). With the increasing level of industrial automation, the demand for remote control of pneumatic diaphragm pumps is growing, aiming to achieve intelligent management goals such as unattended operation and remote maintenance.

[0003] Existing remote start / stop control systems for pneumatic diaphragm pumps suffer from several technical defects: The lack of effective authorization verification and encryption protection mechanisms during remote command transmission makes them susceptible to tampering, misexecution, or illegal operation, leading to potential safety hazards; the single dimension of operational data acquisition, coupled with the absence of effective data fusion and filtering, results in poor data reliability and difficulty in supporting accurate operational condition assessments; fixed pressure safety thresholds fail to adapt to dynamically changing conditions such as different conveying media, operating pressures, and ambient temperatures, potentially causing equipment overload or excessive protection due to threshold mismatch; pressure regulation often employs step-like adjustments, which can easily generate pressure shocks, damaging pump components and affecting equipment lifespan; the lack of a priority scheduling mechanism for command execution prevents priority response to emergency start / stop commands, resulting in insufficient emergency response capabilities; and the lack of standardized classification, storage, and updating of historical operating data hinders effective data support for operational condition adaptation, limiting system adaptability and iterative capabilities.

[0004] To address the shortcomings of the existing technologies, a remote start-stop control system for pneumatic diaphragm pumps is needed, which features secure encrypted transmission, accurate data sensing, dynamic operating condition adaptation, stable pressure regulation, and efficient emergency response capabilities, in order to solve the problems of poor operational safety, reliability, and adaptability of the existing systems. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, this invention provides a remote start / stop control system for a pneumatic diaphragm pump. The objective of this invention can be achieved through the following technical solutions: A remote start / stop control system for a pneumatic diaphragm pump includes: a remote command interaction module, a local core control module, a pneumatic execution drive module, and a status perception feedback module. The remote command interaction module receives start / stop commands and permission verification information, marks the start / stop commands with priority, encrypts them, and generates a control command packet with a timestamp. The state perception feedback module acquires multi-dimensional data, performs multi-source fusion processing, and then performs signal filtering processing to generate a multi-dimensional dataset. The local core control module completes key verification and decryption based on the control command package, parses out the command priority, schedules execution according to priority order, and generates pneumatic drive signals; based on the multi-dimensional dataset, combined with pre-stored historical operating data and real-time operating conditions, the initial air pressure safety threshold is dynamically adjusted, and then the multi-dimensional dataset is compared with the dynamically adjusted air pressure safety threshold to trigger a local emergency stop command and generate a fault code. The pneumatic actuator drive module switches the on / off state of the pneumatic circuit based on the pneumatic drive signal, and adjusts the pneumatic circuit pressure based on a preset stepless pressure regulation mechanism.

[0006] Specifically, the permission verification information includes identity information and operation permission information. The parsed identity information is matched and compared with a pre-stored database of legitimate identity information. If the match is successful, the operation permission information is then checked to see if it covers the operation permission range corresponding to the current start / stop command. When both identity matching and permission verification are successful, the permission verification information is deemed valid.

[0007] Specifically, the encryption process includes: first, integrating the start / stop instructions with priority markers, sorting out the start / stop type and target execution parameter information in the start / stop instructions and forming a standardized data format, then using asymmetric encryption to encrypt the integrated standardized instruction information, and adding a timestamp to generate the control instruction package.

[0008] Specifically, the multi-dimensional data includes air pressure data of the pneumatic circuit, vibration data of the pneumatic diaphragm pump, flow rate data of the conveying medium, flow velocity of the conveying medium, and temperature data of the pump body. When the state perception feedback module performs multi-source fusion processing, it performs data format regularization on the acquired multi-dimensional data, converts the heterogeneous data output by different acquisition components into a unified data format, removes invalid data through preset anomaly judgment rules, removes duplicate data by comparing similar data at different acquisition times, assigns corresponding weight coefficients to various types of valid data based on data type, and fuses and splices the regularized valid data to generate a preliminary fused dataset.

[0009] Specifically, the signal filtering process includes: using a multi-stage filtering method, first filtering to remove high-frequency interference signals from the initially fused dataset, then using secondary filtering to correct the deviation of the dataset after primary filtering, calibrating it in conjunction with a standard data model under similar working conditions, and then performing smoothing processing after calibration to generate the multi-dimensional dataset.

[0010] Specifically, the scheduling and execution process includes: dividing the start and stop instructions into two levels, emergency instructions and regular instructions, according to a preset priority determination rule; suspending the execution of regular instructions; prioritizing the execution of emergency instructions; and resuming the execution of suspended regular instructions after the emergency instructions have been fully executed and the execution result has been confirmed to be valid. Subsequent regular instructions are scheduled and executed in sequence based on the order of the start and stop instructions.

[0011] Specifically, the process by which the local core control module dynamically adjusts the initial air pressure safety threshold includes: extracting the core features of the multi-dimensional dataset under real-time operating conditions, including the current average air pressure, fluctuation range, and pump body temperature trend; retrieving similar operating condition data from the pre-stored historical operating data that have a similarity to the core features of the current operating condition that meets a preset standard; extracting the corresponding optimal air pressure threshold; and calculating the threshold adjustment range of the current operating condition by combining the difference parameters between the current operating condition and similar operating conditions.

[0012] Specifically, the local emergency stop command triggering process includes: setting a fixed detection cycle, sampling and detecting air pressure data within the detection cycle, and triggering the local emergency stop command if air pressure data exceeds the preset warning standard in consecutive detection cycles; determining the fault category based on the data type, quantifying the fault level based on the data exceeding the standard, and generating the fault code based on the combination relationship between the fault category and the fault level.

[0013] Specifically, the pressure regulation process of the preset stepless pressure regulation mechanism includes: based on the pneumatic drive signal, extracting the rated working pressure parameter of the pneumatic diaphragm pump, calculating the real-time pressure deviation between the rated working pressure and the current actual air pressure; starting the stepless pressure regulation action, calculating the adjustment parameter of the air intake or exhaust flow rate of the pneumatic circuit, continuously adjusting the opening of the flow control valve, and adjusting the pneumatic circuit pressure to stabilize within the preset working range.

[0014] Specifically, the process of switching the on / off state of the pneumatic circuit by the pneumatic actuator drive module includes: detecting the actual on / off state of the current air path and feeding back the detection result to the local core control module for confirmation; performing the air path switching action according to the pneumatic drive signal; monitoring the switching speed and air path pressure changes in real time during the switching process; and after the switching is completed, detecting the on / off state of the air path, detecting the switching abnormality, and feeding back fault information.

[0015] Specifically, the pre-stored historical operating data includes: categorizing and storing data based on operating condition type, with categorization dimensions including the type of conveying medium, operating pressure range, and ambient temperature range, while adding corresponding time tags to each type of operating condition data; periodically organizing and updating the historical operating data, during which the frequency of occurrence of each type of operating condition is first counted, and operating data for newly added operating conditions is supplemented.

[0016] Specifically, the control instruction packet sending process includes: when sending the control instruction packet, simultaneously generating an instruction verification identifier and embedding it into the control instruction packet; after completing key verification and decryption, extracting the instruction verification identifier, generating verification response information in combination with pre-stored verification rules, encrypting the verification response information, and feeding it back to the remote instruction interaction module.

[0017] The beneficial effects of this invention are as follows: The remote command interaction module uses permission verification and asymmetric encryption, combined with a two-way verification mechanism of command verification identifier, to avoid the risks of illegal operation, command tampering and misexecution, thus ensuring the security and reliability of the remote control link.

[0018] The status awareness feedback module collects multi-dimensional data such as pneumatic circuit air pressure, pump body vibration, medium flow rate, and pump body temperature. After data format normalization, invalid / duplicate data removal, weight allocation fusion, and multi-level filtering, a highly reliable multi-dimensional dataset is generated to provide data support for the decision-making of the local core control module.

[0019] The local core control module combines pre-stored classified historical operating data and real-time multi-dimensional datasets to dynamically adjust the air pressure safety threshold. This allows it to adapt to dynamically changing operating conditions such as different conveying media, operating pressures, and ambient temperatures, avoiding the problems of insufficient or excessive protection caused by fixed thresholds and improving the system's environmental adaptability.

[0020] The pneumatic actuator drive module adopts a stepless pressure regulation mechanism. Based on the real-time deviation between the rated pressure and the actual air pressure, it adjusts the opening of the flow control valve in real time to achieve continuous and smooth adjustment of the pneumatic circuit pressure. This avoids the impact damage caused by sudden pressure rises and falls and extends the service life of the pump body. At the same time, combined with the emergency stop triggering mechanism of continuous periodic data detection, it can respond to abnormal operating conditions in a timely manner and ensure the safe operation of the equipment.

[0021] The instruction priority scheduling mechanism of the local core control module can prioritize the execution of critical instructions such as emergency start and stop, and suspend the execution of current routine instructions, ensuring the timeliness of emergency response and improving the system's emergency response capability and operational reliability.

[0022] By classifying and storing historical operating data according to operating condition type (including dimensions such as conveying medium, pressure range, and ambient temperature) and regularly organizing and updating it, and supplementing it with new operating condition data, continuous and effective data support is provided for dynamic threshold adjustment and operating condition adaptation, thereby improving the long-term operational stability and iterative capability of the system.

[0023] The system can automatically generate fault codes containing fault type, level, location and time, and also has an anomaly information feedback function, which makes it easy for maintenance personnel to quickly locate faults and carry out repairs, reducing maintenance costs and management difficulty. Attached Figure Description

[0024] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0025] Figure 1 This is a schematic diagram of the overall architecture of a remote start / stop control system for a pneumatic diaphragm pump according to the present invention. Figure 2 This is a sequence diagram of instruction processing in this invention. Detailed Implementation

[0026] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0027] Please see Figures 1-2 A remote start / stop control system for a pneumatic diaphragm pump includes: a remote command interaction module, a local core control module, a pneumatic execution drive module, and a status perception feedback module. The remote command interaction module receives start / stop commands and permission verification information, marks the start / stop commands with priority, encrypts them, and generates a control command packet with a timestamp. The state perception feedback module acquires multi-dimensional data, performs multi-source fusion processing, and then performs signal filtering processing to generate a multi-dimensional dataset. The local core control module completes key verification and decryption based on the control command package, parses out the command priority, schedules execution according to priority order, and generates pneumatic drive signals; based on the multi-dimensional dataset, combined with pre-stored historical operating data and real-time operating conditions, the initial air pressure safety threshold is dynamically adjusted, and then the multi-dimensional dataset is compared with the dynamically adjusted air pressure safety threshold to trigger a local emergency stop command and generate a fault code. The pneumatic actuator drive module switches the on / off state of the pneumatic circuit based on the pneumatic drive signal, and adjusts the pneumatic circuit pressure based on a preset stepless pressure regulation mechanism.

[0028] Specifically, the permission verification information includes identity information and operation permission information. Upon receipt, the two types of information are first separated and parsed. Then, the parsed identity information is matched and compared with a pre-stored database of legitimate identity information. If the match is successful, the operation permission information is then checked to see if it covers the operation permission range corresponding to the current start / stop command. Only when both identity matching and permission verification are successful is the permission verification deemed valid, and the start / stop command is then subject to subsequent priority marking and encryption processing. If any step fails verification, the start / stop command is directly rejected, and the reason for the permission verification failure is reported to the user.

[0029] Specifically, the encryption process includes: first, integrating the start and stop instructions with priority markers, sorting out the core information such as start / stop type and target execution parameters in the instructions and forming a standardized data format; then, using asymmetric encryption to encrypt the integrated standardized instruction information; after encryption, verifying the integrity of the encryption result; after confirming that there is no missing or incorrect information, adding a unique timestamp to generate a complete control instruction package; the timestamp information precisely corresponds to the instruction sending time and is used for subsequent instruction execution timing traceability.

[0030] Specifically, the multi-dimensional data includes air pressure data from the pneumatic circuit, vibration data from the pneumatic diaphragm pump, flow rate data of the conveyed medium, and temperature data of the pump body. Air pressure data is acquired synchronously by acquisition components deployed in the inlet and outlet pipes of the pneumatic circuit; vibration data is acquired by acquisition components deployed at key stress points on the pump body shell; flow rate data is acquired by acquisition components deployed in the medium conveying pipeline; and temperature data is acquired by acquisition components deployed at the pump drive and conveying ends. All acquisition components are synchronously initiated according to a unified time sequence to ensure consistency in the temporal dimensions of the various data types. When the state perception feedback module performs multi-source fusion processing, it standardizes the data format of the acquired multi-dimensional data, converts heterogeneous data output from different acquisition components into a unified data format, removes invalid data using preset anomaly judgment rules, and eliminates duplicate data by comparing similar data at different acquisition times. Based on data type, it assigns corresponding weight coefficients to various types of valid data, and then fuses and splices the standardized valid data to generate a preliminary fused dataset.

[0031] Specifically, the signal filtering process includes: firstly, filtering out high-frequency interference signals in the data through primary filtering, which is achieved by filtering out signal components that exceed the frequency range of normal operating conditions; then, correcting the deviation of the dataset after primary filtering through secondary filtering; calibrating the data with slight deviations by combining it with a standard data model under similar operating conditions; and finally, smoothing the data after calibration to generate a highly reliable multi-dimensional dataset with small fluctuations and high data accuracy.

[0032] Specifically, the scheduling and execution process includes: first, dividing the instructions into two levels, emergency instructions and regular instructions, according to a preset priority determination rule. Emergency instructions include instructions related to equipment safety and emergency response, such as emergency shutdown and emergency start. If there is a regular instruction currently being executed, the execution of the regular instruction is paused first, and the emergency instruction is scheduled for execution first. After the emergency instruction is fully executed and the execution result is confirmed to be valid, the execution of the paused regular instruction is resumed. Subsequent regular instructions are queued and scheduled for execution in the order they are received.

[0033] Specifically, the process of dynamically adjusting the initial air pressure safety threshold by the local core control module includes: first, extracting the core features of the multi-dimensional dataset under real-time operating conditions, including the current average air pressure, fluctuation range, pump body temperature trend, etc.; then, retrieving similar operating condition data with a similarity to the core features of the current operating condition that meets the preset standard from the pre-stored historical operating data; extracting the optimal air pressure threshold corresponding to the similar operating conditions; calculating the threshold adjustment range adapted to the current operating conditions by combining the difference parameters between the current operating conditions and similar operating conditions; after correcting the initial air pressure safety threshold based on the adjustment range, first conducting a short-term trial operation to verify the adjusted threshold; and then using it as the formal safety threshold after confirming that there are no abnormalities.

[0034] Specifically, the local emergency stop command triggering process includes: setting a fixed detection cycle, sampling and detecting air pressure data multiple times within each detection cycle, and triggering a local emergency stop command only if the air pressure data exceeds the dynamically adjusted threshold range in multiple consecutive detection cycles and the exceedance reaches a preset warning standard; simultaneously, determining the fault category based on the data type exceeding the threshold, quantifying the fault level based on the data exceedance range, and generating a corresponding fault code based on the combination relationship between the fault category and the fault level. The fault code contains key information such as the fault occurrence time and fault location.

[0035] Specifically, the pressure regulation process of the preset stepless pressure regulation mechanism includes: first, receiving the pneumatic drive signal sent by the local core control module, extracting the rated working pressure parameter of the pneumatic diaphragm pump carried in the signal, and collecting the current actual air pressure of the pneumatic circuit in real time through the built-in pressure acquisition component, calculating the real-time pressure deviation between the rated working pressure and the current actual air pressure; based on the real-time pressure deviation, starting the stepless pressure regulation action, using a preset regulation algorithm to calculate the adjustment parameters of the air inlet / outlet flow rate of the pneumatic circuit in real time, and achieving continuous and smooth adjustment of the pneumatic circuit pressure by continuously adjusting the opening of the flow control valve, without fixed adjustment stage divisions; during the adjustment process, synchronously receiving the pump body operating status data transmitted by the status perception feedback module, and verifying in real time whether the pump body vibration, temperature and other parameters are within the preset normal range; if an abnormal pump body operating status is detected, immediately adjusting the adjustment rate to reduce the pressure change amplitude, and simultaneously feeding back the abnormal status signal to the local core control module; after the pump body operating status returns to normal, continuing to perform stepless pressure regulation based on the real-time pressure deviation until the pneumatic circuit pressure stabilizes in the preset working range, thus completing the pressure regulation.

[0036] Specifically, the process of switching the on / off state of the pneumatic circuit by the pneumatic actuator drive module includes: detecting the actual on / off state of the current air path through the built-in detection component and feeding back the detection result to the local core control module for confirmation; executing the air path switching action according to the instruction requirements of the pneumatic drive signal; monitoring the switching speed and air path pressure changes in real time during the switching process to avoid excessive pressure fluctuations caused by switching too quickly; after the switching is completed, detecting whether the air path is unobstructed through the detection component, and verifying whether the air path on / off state is consistent with the instruction requirements to ensure that the switching action is effectively executed; if an abnormal switching is detected, immediately feeding back fault information to the local core control module.

[0037] Specifically, the pre-stored historical operating data includes: categorizing and storing data according to operating condition type, with categorization dimensions including the type of transport medium, operating pressure range, and ambient temperature range, while adding corresponding time tags to each type of operating condition data; periodically organizing and updating the historical operating data, during which the recent frequency of occurrence of various operating conditions is first counted, and old data corresponding to operating conditions that have not occurred for a long time and have exceeded the preset retention period are marked as data to be removed. After a second confirmation that there is no value in retaining them, the data to be removed is deleted, and at the same time, the operating data of recently added operating conditions is supplemented to ensure that the historical data effectively supports the adjustment of dynamic thresholds.

[0038] Specifically, the control command packet sending process includes: when the remote command interaction module sends an encrypted control command packet, it simultaneously generates a unique command verification identifier and embeds it into the command packet; after the local core control module completes key verification and decryption, it extracts the verification identifier from the command packet, generates verification response information by combining it with its own pre-stored verification rules, encrypts the verification response information, and feeds it back to the remote command interaction module; the remote command interaction module receives and decrypts the encrypted response information, compares the parsed verification identifier with the original identifier sent, and if the two match, it determines that the command reception is valid and records the command reception status; if the comparison is inconsistent or no valid verification identifier is parsed, it determines that there is a risk of tampering in the command transmission, immediately sends a command invalidation signal to the local core control module, and terminates the subsequent execution process of the current command; if no verification response information is received from the local core control module, it first starts a local encryption link self-check, eliminates its own encryption module failure, resends an encrypted control command packet carrying a new verification identifier, and resets the response waiting time; if multiple resends still fail to pass two-way verification, it locks the current user's command sending permission and sends a verification failure warning.

[0039] This embodiment discloses a specific implementation of a remote start-stop control system for a pneumatic diaphragm pump. In this embodiment, the permission matching threshold is set to A, the asymmetric encryption algorithm parameters are set to B, the data acquisition synchronization timing is set to C, the instruction priority judgment threshold is set to D, the operating condition similarity matching threshold is set to F, the air pressure threshold adjustment coefficient is set to G, the emergency stop detection cycle is set to T, the number of consecutive over-threshold judgments is set to M, the pressure over-threshold warning standard is set to H, the number of pressure adjustment stages is set to N, the stabilization time of each adjustment stage is set to S, and the weight coefficients of various data types are set to D1, D2, D3, and D4, respectively. The specific implementation process is as follows: Overall system deployment The remote start / stop control system for the pneumatic diaphragm pump in this embodiment includes a remote command interaction module, a local core control module, a pneumatic actuator drive module, and a status perception feedback module. Each module establishes a signal connection via an industrial communication link, forming a closed-loop control link. The remote command interaction module is deployed in a remote monitoring center and establishes a bidirectional communication connection with the user's operating terminal via wired Ethernet, enabling real-time transmission of command reception and status feedback. The local core control module is integrated into the field control box of the pneumatic diaphragm pump and constructs a pre-stored historical operation database using a built-in storage chip. The database adopts a partitioned storage architecture, with independent storage partitions divided according to operating conditions. The pneumatic actuator drive module establishes a sealed connection with the pneumatic main circuit and branch circuits of the pneumatic diaphragm pump through flange interfaces and is equipped with an independent air circuit control unit to achieve on / off switching and pressure regulation. The data acquisition components of the status perception feedback module are deployed by bolts at the inlet and outlet sections of the pneumatic circuit, key stress positions on the pump body shell, the flange of the medium delivery pipeline, and the bearing position at the pump drive end. All data acquisition components aggregate data through a bus connection, achieving comprehensive and synchronous acquisition of multi-dimensional data.

[0040] Implementation process of remote command interaction module Users input start / stop commands and permission verification information through a remote operation terminal. The permission verification information includes user identity information (such as the identity identifier associated with the operation account) and operation permission information (such as whether they have start / stop operation permissions). After receiving the above information, the remote command interaction module first performs preliminary parsing of the information to separate the start / stop commands and permission verification information.

[0041] Permission verification execution: The parsed identity information is matched against a locally pre-stored database of legitimate identity information. The identity information contains K1 core features (such as account identifier, operation permission level, device association code, etc.). The similarity is calculated using a weighted average of feature matching degree. The specific calculation process is as follows: Let the matching identifier of the i-th feature be α_i (α_i=1 indicates a successful match, α_i=0 indicates a failed match), and the corresponding weight be ω_i (Σω_i=1, i=1 to K1). Then the identity matching similarity S_id=Σ(α_i×ω_i). The identity matching is considered successful when S_id≥threshold A. After the identity matching is successful, the operation permission information is further verified to see if it covers the operation permission range corresponding to the current start / stop command. When both identity matching and permission verification are successful, the permission verification information is deemed valid, and the process proceeds to the next step. If either verification fails, a permission verification failure message (including information about the failed step) is sent to the user, and the current command processing is terminated.

[0042] Priority Marking and Encryption Processing: Based on preset priority determination rules (e.g., start / stop commands involving emergency equipment protection are judged as emergency commands, and regular start / stop operations are judged as regular commands), start / stop commands that have passed authorization verification are marked with priority. The marker for emergency commands is C1, and the marker for regular commands is C2. Then, the start / stop commands with priority marking are integrated to sort out the start / stop type (start / stop) and target execution parameter information (such as the associated parameters corresponding to the target running status) in the command. The data is then converted into standardized data in JSON format using a data format conversion algorithm. During the conversion process, the data is organized according to the field order of "command type-priority-target parameter-initiation time". The integrated standardized command information is encrypted using an asymmetric encryption method with parameter B. Specifically, the standardized data is first encrypted in segments using the public key. After each segment is encrypted, a checksum is added. After all segments are encrypted, they are concatenated. Then, an encrypted digest is generated using the private key. Finally, the encrypted data and the encrypted digest are combined, and a unique timestamp is added (the timestamp is accurate to the millisecond level and the format is "year-month-day-hour-minute-second-millisecond") to generate a complete control command package.

[0043] Command transmission and verification identifier embedding: The remote command interaction module sends the above control command package through the industrial communication link. At the same time, it generates a unique command verification identifier (the identifier format is a preset character combination) and embeds it in the control command package, and simultaneously starts command transmission status monitoring.

[0044] Implementation process of the state-aware feedback module Multi-dimensional data acquisition: The state perception feedback module synchronously starts each acquisition component according to the timing C, and collects the air pressure data of the pneumatic circuit (air pressure of the inlet pipe section and air pressure of the outlet pipe section), the vibration data of the pneumatic diaphragm pump (vibration signal of key positions of the pump body shell), the flow data of the conveyed medium (flow signal in the medium conveying pipeline), and the temperature data of the pump body (temperature of the pump body drive end and temperature of the conveying end), forming a multi-dimensional raw data set.

[0045] Multi-source fusion processing: The acquired multi-dimensional raw data undergoes data format standardization. Heterogeneous data output from different acquisition components (such as voltage signal format for air pressure data and frequency signal format for vibration data) are uniformly converted into 32-bit floating-point numerical data via a data conversion interface. Invalid data is removed using preset anomaly judgment rules (e.g., data exceeding the reasonable fluctuation range ±K is considered invalid, where K is a preset fluctuation coefficient). Simultaneously, a data deduplication algorithm compares similar data from different acquisition times. Let the data from the previous acquisition time be X_prev and the similar data from the next acquisition time be X_curr. The data difference ΔX = |X_curr - X_prev| is calculated. When ΔX < When a preset threshold P is set, data from the previous moment is retained and duplicate data from the next moment is removed. Based on data type, corresponding weight coefficients are assigned to each type of valid data, with air pressure data having a weight coefficient of D1, vibration data D2, flow rate data D3, and temperature data D4, and D1+D2+D3+D4=1. Data concatenation uses a quadruple format of "time sequence marker + data type marker + value + weight," concatenating the quadruples of each valid data in the order of collection. The specific concatenation logic is as follows: first, extract the collection timestamp of each data point, sort them in ascending order of timestamp, then connect the sorted quadruples of data using a string concatenation function, adding a separator "|" between adjacent quadruples to generate a preliminary fused dataset.

[0046] Signal filtering: A multi-stage filtering method is used to process the initially fused dataset. First, a low-pass filter circuit is used for primary filtering, with a cutoff frequency of Q, to filter out high-frequency interference signals with frequencies higher than Q (such as signal components whose frequencies exceed a preset reasonable range). Then, a least squares method is used for secondary filtering to correct the deviation of the dataset after primary filtering. Specifically, a standard data model E under similar operating conditions is used as a benchmark, and the deviation value of each data point from the corresponding data point in the standard data model is calculated. The data is calibrated using a deviation compensation algorithm. After calibration, a moving average algorithm is used for smoothing. The moving window size is set to R, and the average value is calculated according to the window range of "current data point + previous R-1 data points". The average value is used to replace the current data point, and finally a highly reliable multi-dimensional dataset is generated. This dataset is then transmitted to the local core control module in real time via an industrial bus.

[0047] Implementation process of the local core control module Command Verification and Decryption: After receiving the control command packet sent by the remote command interaction module through the communication interface, the local core control module first extracts the encrypted digest from the control command packet, decrypts the encrypted digest using the private key matched with the remote end, and obtains the digest verification value; then, it decrypts the encrypted data in segments using the public key, verifying the checksum after each segment decryption to ensure that the data is not corrupted, and concatenates all segments after decryption to obtain standardized data; it generates a local digest from the decrypted data and compares it with the digest verification value, and if they match, the key verification is completed; after successful verification, it parses the standardized data to extract the start / stop command, priority flag, timestamp, and command verification identifier; at the same time, it generates verification response information by combining the pre-stored verification rules (the verification rules are "identifier format verification + identifier uniqueness verification"), which includes a "command received successfully" identifier, the local core control module number, and the receiving timestamp. After encrypting the verification response information using the same asymmetric encryption method, it is fed back to the remote command interaction module through the original communication link to complete the two-way verification of the command.

[0048] Command priority scheduling and execution: Based on the preset priority determination rule (priority threshold is D), the decrypted start and stop commands are divided into two levels: emergency commands (marker C1) and regular commands (marker C2). If there is a regular command currently being executed, the execution of the regular command is immediately suspended, and the emergency command is scheduled for execution first. After the emergency command is fully executed and the execution result fed back by the pneumatic actuator module is valid, the execution of the suspended regular command is resumed. Subsequent regular commands are queued and scheduled for execution in the order they are received.

[0049] Pneumatic drive signal generation: The local core control module generates an appropriate pneumatic drive signal based on the start / stop command executed by the schedule and the basic operating parameters of the pneumatic diaphragm pump. This pneumatic drive signal includes air circuit on / off switching command and pressure regulation parameter command, and then sends the pneumatic drive signal to the pneumatic execution drive module.

[0050] Dynamic adjustment of initial air pressure safety threshold: Core features of the multi-dimensional dataset transmitted by the state perception feedback module under real-time operating conditions are extracted using a feature extraction algorithm. The specific calculation process is as follows: ① Calculation of current average air pressure: Set the most recent S1 air pressure data points as P1, P2, ..., P_S1 (S1 is the preset number of data points), and the current average air pressure P_avg = (P1 + P2 + ... + P_S1) / S1, which represents the stability level of the current air pressure; ② Calculation of air pressure fluctuation range: Select the maximum value P_max and the minimum value P_min from the above S1 air pressure data points. The air pressure fluctuation range ΔP = P_max - P_min; ③ Calculation of pump body temperature change trend: A linear fitting algorithm is used. Let the acquisition times corresponding to the most recent S2 temperature data points be t1, t2, ..., t_S2 (time series variable, unit is time unit), and the corresponding temperature values ​​be T1, T2, ..., T_S2. With the acquisition time as the independent variable x and the temperature value as the dependent variable y, a linear equation y=kx+b is constructed (k is the slope, representing the temperature change trend; b is the intercept). The slope k is calculated using the least squares method. The specific calculation formula is as follows: k=[S2×Σ(x_iy_i)-Σx_i×Σy_i] / [S2×Σ(x_i 2 )-(Σx_i) 2 ] Where i takes values ​​from 1 to S2, and Σ is the summation symbol; then, a similarity matching algorithm is used to retrieve similar operating condition data from the pre-stored historical operation database that have a similarity to the core features of the current operating condition that reaches a threshold F. The similarity is achieved by calculating the Euclidean distance between the current core features and the core features of the historical operating conditions. Let the current core feature vector be V_curr = (P_avg, ΔP, k), and the core feature vector of a certain historical operating condition be V_hist = (P_avg', ΔP', k'), then the Euclidean distance D_sim = √[(P_avg - P_avg')]. 2 +(ΔP-ΔP') 2 +(k-k') 2 When D_sim < F, it is determined to be a similar working condition; the optimal air pressure threshold P_opt corresponding to the similar working condition data is extracted, and combined with the difference parameters between the current working condition and the similar working condition (such as the difference in medium type and the difference in ambient temperature), the threshold adjustment range G of the current working condition is calculated by weighted calculation method. The specific calculation logic is: G = G1 × (μ_curr / μ_hist) + G2 × (T_env-curr-T_env-hist), where μ_curr is the viscosity of the current transported medium, μ_hist is the viscosity of the transported medium in the similar working condition, T_env-curr is the current ambient temperature, T_env-hist is the ambient temperature in the similar working condition, and G1 and G2 are preset weight coefficients (G1 + G2 = 1); the initial air pressure safety threshold is corrected based on the adjustment range G, and the correction formula is: dynamic threshold P_dyn = P_init × (1 + G), and finally the dynamic air pressure safety threshold adapted to the current working condition is obtained.

[0051] Data Comparison and Emergency Stop Trigger: A fixed detection period T is set. Within each detection period, the air pressure data in the multi-dimensional dataset is sampled multiple times by the sampling circuit, with a sampling interval of T1 (T1 < T). The number of samples in each period is K = T / T1 (K is an integer). The air pressure data of each sampling point is compared with the dynamic air pressure safety threshold by a comparator, and the number of sampling points exceeding the threshold K_out is recorded. The percentage of exceeding the threshold in a single detection period is calculated as η = K_out / K. If η ≥ 50%, the air pressure in that period is determined to be abnormal. If the air pressure is determined to be abnormal in M ​​consecutive detection periods, and the air pressure exceeding the threshold by ΔP_out in any period is ≥ the preset warning standard H (ΔP_out = |current sampled air pressure P_samp - dynamic air pressure safety threshold P_dyn|), then a local emergency stop command is immediately triggered. The emergency stop command is transmitted to the emergency stop of the pneumatic actuator drive module through a level signal. The control unit simultaneously determines the fault category based on the data type exceeding the threshold, and quantifies the fault level by combining the magnitude of the data exceeding the threshold. Specifically, the quantification calculation is as follows: when H≤ΔP_out<2H, the fault level L=1; when ΔP_out≥2H, the fault level L=2. A unique fault code is generated based on the combination relationship between the fault category and the fault level. The fault code uses an 8-bit character encoding: the first 2 bits are the fault category code, the middle 3 bits are the fault level code (level 1 corresponds to 001, level 2 corresponds to 002), and the last 3 bits are the minute code of the time the fault occurred. The minute code is calculated as follows: if the time of the fault occurrence is h hours and m minutes, the minute code = h×60+m. If the calculation result is less than 3 bits, leading zeros are added in front (for example, 3 hours and 5 minutes corresponds to 003×60+005=185, and the code is 185; 0 ​​hours and 8 minutes corresponds to 008, and the code is 008). This fault code is synchronously fed back to the remote command interaction module in an encrypted manner.

[0052] Implementation process of pneumatic actuator drive module Air circuit on / off state switching: After receiving the pneumatic drive signal sent by the local core control module through the signal receiving interface, the pneumatic actuator first detects the actual on / off state of the current pneumatic circuit through the built-in pressure sensor (pressure greater than 0 indicates a closed circuit, pressure equal to 0 indicates an open circuit). The detected pressure value is converted into a digital signal and fed back to the local core control module for confirmation. According to the air circuit on / off switching command in the pneumatic drive signal, the valve core of the solenoid control valve is controlled to move, realizing the air circuit switching action. During the switching process, the valve core switching speed is monitored in real time by the speed sensor, and the air circuit pressure change is monitored in real time by the pressure sensor. When the switching speed is greater than the preset speed threshold V or the pressure fluctuation is greater than the preset fluctuation threshold U, the valve core is controlled to decelerate to avoid pressure fluctuations exceeding the reasonable range. After the switching is completed, the air circuit on / off state is detected again by the pressure sensor to verify whether it is consistent with the drive command requirements. If an abnormal switching is detected (such as not switching to the target on / off state as instructed, or the pressure cannot be stabilized after switching), a fault signal is immediately generated. The fault signal includes the abnormality type (switching failure / pressure instability) and the time of the abnormality, and is fed back to the local core control module through the communication link.

[0053] The stepless pressure regulation mechanism adjusts pressure by acquiring the rated operating pressure P_rated of the pneumatic diaphragm pump and the current actual air pressure P_curr in real time through a pressure acquisition unit. The pressure difference ΔP_reg = |P_rated - P_curr| is calculated, and stepless continuous pressure regulation is achieved based on this pressure difference. A proportional-integral-derivative (PID) controller is used to execute the stepless pressure regulation action. Specifically, the real-time opening adjustment of the flow control valve is calculated using a PID algorithm. The control formula for the PID algorithm is as follows: u(t)=Kp×ΔP_reg(t)+Ki×∫ΔP_reg(t)dt+Kd×d[ΔP_reg(t)] / dt Where u(t) is the real-time opening adjustment, Kp is the proportional coefficient, Ki is the integral coefficient, Kd is the derivative coefficient, ∫ΔP_reg(t)dt is the integral term of the pressure deviation, and d[ΔP_reg(t)] / dt is the derivative term of the pressure deviation; based on the calculated opening adjustment, the opening of the flow control valve is controlled in real time to adjust the inlet / outlet air flow, achieving continuous and smooth adjustment of the pneumatic circuit pressure without fixed adjustment stages; during pressure adjustment, the pump operating status data (vibration data, temperature data) transmitted by the status perception feedback module is received synchronously through the communication interface, and the vibration data is judged by the data verification algorithm. If the vibration and temperature data are within the preset range [Vmin, Vmax] and the temperature data are within the preset range [Tmin, Tmax], and both are within a reasonable range, then stepless pressure regulation continues. If the data exceeds the reasonable range, the PID parameters are immediately adjusted (Kp, Ki, Kd are corrected according to preset coefficients) to slow down the pressure regulation speed. At the same time, feedback is sent to the local core control module. After the data returns to normal, stepless pressure regulation continues based on the pressure deviation until the pneumatic circuit pressure is accurately adjusted to the preset working range (pressure value within the range of rated working pressure ± ΔP) and the pressure is maintained stably, thus completing the smooth start and stop of the pneumatic diaphragm pump.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A remote start / stop control system for a pneumatic diaphragm pump, characterized in that, include: Remote command interaction module, local core control module, pneumatic actuation drive module, and status perception feedback module; The remote command interaction module receives start / stop commands and permission verification information, marks the start / stop commands with priority, encrypts them, and generates a control command packet with a timestamp. The state perception feedback module acquires multi-dimensional data, performs multi-source fusion processing, and then performs signal filtering processing to generate a multi-dimensional dataset. The local core control module completes key verification and decryption based on the control command package, parses out the command priority, schedules execution according to priority order, and generates pneumatic drive signals; based on the multi-dimensional dataset, combined with pre-stored historical operating data and real-time operating conditions, the initial air pressure safety threshold is dynamically adjusted, and then the multi-dimensional dataset is compared with the dynamically adjusted air pressure safety threshold to trigger a local emergency stop command and generate a fault code. The pneumatic actuator drive module switches the on / off state of the pneumatic circuit based on the pneumatic drive signal, and adjusts the pneumatic circuit pressure based on a preset stepless pressure regulation mechanism.

2. The system according to claim 1, characterized in that, The permission verification information includes identity information and operation permission information. The parsed identity information is matched and compared with a pre-stored database of legitimate identity information. If the match is successful, the operation permission information is then checked to see if it covers the operation permission range corresponding to the current start / stop command. When both identity matching and permission verification are successful, the permission verification information is deemed valid.

3. The system according to claim 1, characterized in that, The encryption process specifically includes: first, integrating the start / stop instructions with priority markers, sorting out the start / stop type and target execution parameter information in the start / stop instructions and forming a standardized data format, then using an asymmetric encryption method to encrypt the integrated standardized instruction information, and adding a timestamp to generate the control instruction package.

4. The system according to claim 1, characterized in that, The multi-dimensional data includes air pressure data of the pneumatic circuit, vibration data of the pneumatic diaphragm pump, flow rate data of the conveying medium, flow velocity of the conveying medium, and temperature data of the pump body. When the state perception feedback module performs multi-source fusion processing, it standardizes the data format of the acquired multi-dimensional data, converts the heterogeneous data output by different acquisition components into a unified data format, removes invalid data through preset anomaly judgment rules, and removes duplicate data by comparing the same type of data at different acquisition times. Based on the data type, it assigns corresponding weight coefficients to various types of valid data, and then fuses and splices the standardized valid data to generate a preliminary fused dataset.

5. The system according to claim 1, characterized in that, The specific process of signal filtering includes: using a multi-level filtering method, first filtering to remove high-frequency interference signals from the initially fused dataset, then using secondary filtering to correct the deviation of the dataset after primary filtering, calibrating it in conjunction with a standard data model under similar working conditions, and then performing smoothing processing after calibration to generate the multi-dimensional dataset.

6. The system according to claim 1, characterized in that, The scheduling and execution process includes: dividing the start and stop instructions into two levels, emergency instructions and regular instructions, according to a preset priority determination rule; suspending the execution of regular instructions; prioritizing the execution of emergency instructions; and resuming the execution of suspended regular instructions after the emergency instructions have been fully executed and the execution result has been confirmed to be valid. Subsequent regular instructions are scheduled and executed in sequence based on the order of the start and stop instructions.

7. The system according to claim 1, characterized in that, The process by which the local core control module dynamically adjusts the initial air pressure safety threshold includes: extracting the core features of the multi-dimensional dataset under real-time operating conditions, including the current average air pressure, fluctuation range, and pump body temperature trend; retrieving similar operating condition data from the pre-stored historical operating data that have a similarity to the core features of the current operating condition that meets a preset standard; extracting the corresponding optimal air pressure threshold; and calculating the threshold adjustment range of the current operating condition by combining the difference parameters between the current operating condition and similar operating conditions.

8. The system according to claim 1, characterized in that, The local emergency stop command triggering process includes: setting a fixed detection cycle, sampling and detecting air pressure data within the detection cycle, and triggering the local emergency stop command if air pressure data exceeds the preset warning standard in consecutive detection cycles; determining the fault category based on the data type, quantifying the fault level based on the data exceeding the standard, and generating the fault code based on the combination relationship between the fault category and the fault level.

9. The system according to claim 1, characterized in that, The pressure regulation process of the preset stepless pressure regulation mechanism includes: based on the pneumatic drive signal, extracting the rated working pressure parameter of the pneumatic diaphragm pump, calculating the real-time pressure deviation between the rated working pressure and the current actual air pressure; starting the stepless pressure regulation action, calculating the adjustment parameter of the air intake or exhaust flow rate of the pneumatic circuit, continuously adjusting the opening of the flow control valve, and adjusting the pneumatic circuit pressure to stabilize within the preset working range.

10. The system according to claim 1, characterized in that, The process of switching the on / off state of the pneumatic circuit by the pneumatic actuator drive module includes: detecting the actual on / off state of the current air path and feeding back the detection result to the local core control module for confirmation; executing the air path switching action according to the pneumatic drive signal; monitoring the switching speed and air path pressure changes in real time during the switching process; detecting the on / off state of the air path after the switching is completed; detecting the switching abnormality and feeding back fault information.

11. The system according to claim 1, characterized in that, The pre-stored historical operating data specifically includes: categorized storage based on operating condition type, with categorization dimensions including conveying medium type, operating pressure range, and ambient temperature range, while adding corresponding time tags to each type of operating condition data; and periodically organizing and updating the historical operating data, first counting the frequency of occurrence of each type of operating condition during the update process, and supplementing the operating data of newly added operating conditions.

12. The system according to claim 1, characterized in that, The control command packet sending process specifically includes: when sending the control command packet, simultaneously generating a command verification identifier and embedding it into the control command packet; after completing key verification and decryption, extracting the command verification identifier, generating verification response information in combination with pre-stored verification rules, encrypting the verification response information, and feeding it back to the remote command interaction module.

Images