Vacuum sewage discharge equipment intelligent monitoring system based on multi-data fusion

Abstract

The invention discloses a vacuum sewage discharge equipment intelligent monitoring system based on multi-data fusion. The vacuum sewage discharge equipment intelligent monitoring system comprises an installation sensor module, a data acquisition module, a data analysis module, an intelligent control module and an intelligent monitoring module. The invention belongs to the technical field of data processing, and particularly relates to a vacuum sewage discharge equipment intelligent monitoring system based on multi-data fusion. According to the scheme, Kalman filtering noise reduction is performed on collected data through a data analysis module, and a correlation trend is mined through multi-parameter fusion; traditional post-event fault processing is converted into beforehand early warning and graded intervention through a three-level early warning mechanism, so that the driving risk is reduced; the intelligent control module is adopted for three-level early warning judgment, an improved vacuum pump cooperative control algorithm is adopted for optimizing multi-pump-set starting and stopping logic, loads are dynamically distributed, predictive starting and stopping are carried out in combination with demand fluctuation, and the real-time operation state of the system is monitored through the intelligent monitoring module. The hose recovery state monitoring function of the sewage discharge unit and the low-power-consumption operation function of the system are achieved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of data processing, and particularly relates to an intelligent monitoring system for vacuum sewage unloading equipment based on multi-data fusion. BACKGROUND

[0002] The intelligent monitoring system for vacuum sewage unloading equipment is a system that, taking multi-data fusion as the core, combining sensor perception, intelligent communication, data analysis and remote management and control technology, and monitoring the running state of railway vacuum sewage unloading equipment in real time, intelligently controlling and fault warning, aiming to comprehensively improve the fine management level of station and section equipment, and realizing the functions of intelligent control, remote intervention, energy saving and consumption reduction, safe communication and fault auxiliary processing of the vacuum sewage unloading system.

[0003] The traditional railway vacuum sewage unloading operation has the technical problems that the running data of the sewage unloading system is not collected and analyzed, potential faults cannot be identified through data trends, and only post-fault processing is available, resulting in resource waste and increased driving risk; the system does not have a logic of running on demand, needs to be turned on all the time, has high energy consumption, and has no unified monitoring system, relies on manual inspection, has no special technical monitoring of the hose recovery state, has the driving risk of driving with the hose, and cannot realize real-time perception and fine management and control of the equipment state. SUMMARY

[0004] In view of the above problems, in order to overcome the defects of the prior art, the intelligent monitoring system for vacuum sewage unloading equipment based on multi-data fusion provided by the application comprehensively collects the key running data of the sewage unloading unit and the vacuum unit through a data collection module, ensures real-time and accurate uploading of the data through multi-interface compatibility and safe transmission technology, performs Kalman filter noise reduction on the collected data through a data analysis module, mines the correlation trends through multi-parameter fusion, changes the traditional post-fault processing into pre-warning and graded intervention through a three-level early warning mechanism, and reduces the driving risk; in view of the technical problems that the system does not have a logic of running on demand, needs to be turned on all the time, has high energy consumption, has no unified monitoring system, relies on manual inspection, has no special technical monitoring of the hose recovery state, has the driving risk of driving with the hose, and cannot realize real-time perception and fine management and control of the equipment state, the intelligent control module is used to make a three-level early warning judgment, an improved vacuum pump cooperative control algorithm is used to optimize the start-stop logic of the multi-pump group, dynamically allocate the load, implement predictive start-stop in combination with demand fluctuation, link the soft start current and the load distribution coefficient to ensure state synchronization, and the intelligent monitoring module is used to monitor the real-time running state of the system, realize the hose recovery state monitoring function of the sewage unloading unit, and realize the low-power running function of the system.

[0005] The intelligent monitoring system for vacuum waste removal equipment based on multi-data fusion provided by this invention includes a sensor installation module, a data acquisition module, a data analysis module, an intelligent control module, and an intelligent monitoring module.

[0006] The sensor installation module specifically refers to the installation of sensors in preparation for the data acquisition module.

[0007] The data acquisition module specifically acquires sensor signals from the unloading unit and the vacuum unit through the unloading unit data acquisition sub-module and the vacuum unit data acquisition sub-module, and then transmits the vacuum unloading data securely to the data analysis module through the unloading unit data communication sub-module.

[0008] The data analysis module specifically stores vacuum unloading data, performs fault early warning analysis based on the data, performs single-parameter noise reduction and multi-parameter fusion, and performs three-level early warning judgment on abnormal parameters.

[0009] The intelligent control module specifically optimizes the start-stop logic of multiple pump groups based on the three-level early warning judgment, adopts an improved vacuum pump collaborative control algorithm, dynamically allocates the load, implements predictive start-stop of pump groups in combination with demand fluctuations, and combines the soft start current with the load allocation coefficient to ensure state synchronization.

[0010] The intelligent monitoring module specifically monitors the real-time operating status of the system, enabling it to monitor the hose recovery status of the wastewater unloading unit and perform low-power operation of the system.

[0011] Furthermore, the sensor installation module specifically includes an installation position detection sensor, a vacuum sensor, a temperature sensor, a current sensor, a voltage sensor, and a vibration sensor.

[0012] Furthermore, the data acquisition module includes a wastewater unloading unit data acquisition submodule, a wastewater unloading unit data communication submodule, and a vacuum unit data acquisition submodule;

[0013] The data acquisition submodule of the unloading unit is installed in the on-site unloading unit electrical control box. It is connected to the data output terminal of the position detection sensor through a data transmission signal line, and to the signal input terminal of the unloading unit data communication submodule through an RS485 industrial communication network.

[0014] The data communication submodule of the unloading unit is installed at the end of the track of each unloading line. Its signal input end is connected to the data acquisition module of the unloading unit, and its signal output end is connected to the input end of the data analysis module through the fiber optic industrial network. Secure communication of key data of vacuum unloading is achieved through data encryption technology, identity authentication technology, access control technology, secure communication protocol, data integrity verification and other technologies.

[0015] The vacuum unit data acquisition submodule is installed inside the vacuum unit electrical control cabinet in the vacuum pump room and is connected to the input terminal of the data analysis module via an industrial Ethernet.

[0016] The data acquisition submodules of the wastewater unloading unit and the vacuum unit are designed with various data interfaces, including RS232, RS485, fiber optic, Ethernet, and LoRa, for compatibility design with various interface devices.

[0017] Furthermore, the data analysis module includes a data storage and analysis server, a switch, and a firewall;

[0018] The data storage and analysis server is installed in the information computer room and is used to store all the operating data collected by the system, including basic data and equipment operating data uploaded by sub-servers, as well as to provide background data analysis for the system. The background data analysis mainly includes fault early warning analysis, energy consumption analysis, etc.

[0019] The fault early warning analysis involves real-time acquisition of system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current. A Kalman filter algorithm is used to process and fuse these data points. Through comparison of multiple data sets, early warning of abnormal equipment conditions is achieved, including the following:

[0020] Parameter acquisition involves real-time acquisition of four types of core data: system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current.

[0021] Single-parameter Kalman filter noise reduction eliminates sensor noise and outputs purified parameters;

[0022] Multi-parameter fusion, through extended Kalman filtering, mines parameter correlations and outputs a fused state vector, capturing the implicit correlations between parameters. It combines static values ​​and dynamic change rates, including both the current absolute state of the device and the trend of its change, breaking through the limitations of single parameters and improving data reliability.

[0023] The three-level early warning system is as follows: when the deviation of the calculated parameters from the rated values ​​exceeds 5%, a level 1 early warning is triggered, indicating a minor anomaly; when the deviation between the actual correlation parameters and the normal model exceeds 10%, a level 2 early warning is triggered, indicating a correlation anomaly; and when the monitored parameters change drastically and exceed the normal range, a level 3 early warning is triggered, indicating an emergency fault.

[0024] Level 1 warning: The system records the anomaly and reminds maintenance personnel to conduct inspections; Level 2 warning: The system automatically adjusts the load and triggers audible and visual alarms; Level 3 warning: The system immediately cuts off the power to some pump units to prevent equipment damage and sends an emergency fault signal to the staff.

[0025] The switch is installed in the information room and is a network device used for forwarding electrical signals, providing a dedicated electrical signal path for any two network nodes connected to the switch.

[0026] The firewall is installed in the information computer room to protect computer systems and networks from external threats.

[0027] Furthermore, the intelligent control module, based on a three-level early warning system, optimizes the start-stop logic of multiple pump groups using an improved vacuum pump collaborative control algorithm, dynamically allocating the load to ensure precise matching between the load of the operating pump groups and actual needs. This prevents full-load operation during off-peak hours, reducing ineffective energy consumption and avoiding single-pump overload. This reduces equipment wear and failure risks caused by prolonged single-pump overload, extending the overall service life of the pump groups. A soft-start strategy is also introduced to avoid current surge issues, including the following:

[0028] Dynamic load distribution across multiple pump groups: Based on the real-time operating parameters of each vacuum pump, the optimal load distribution coefficient for each pump is calculated to avoid overloading of a single pump.

[0029] Predictive start-stop of pump sets, combined with demand fluctuations, dynamically adjusts the number of pneumatic pumps to reduce frequent start-stops;

[0030] Calculate the soft-start current and link it with the load distribution factor to ensure that the current rises synchronously with the load during startup, thereby improving the safety of the startup process.

[0031] Furthermore, the intelligent monitoring module includes an information management computer, a video camera, and a video recorder;

[0032] The information management computer is installed in the control room monitoring room and is connected to the data analysis module via an industrial Ethernet. The video camera is installed in the vacuum pump room and is connected to the video recorder via an Ethernet.

[0033] The information management computer is used to display the real-time operating status of the intelligent monitoring system for vacuum unloading equipment; to realize the remote monitoring function of the hose recovery status of the unloading unit and the function of low-power operation of the system.

[0034] The beneficial effects achieved by the present invention using the above solution are as follows:

[0035] (1) To address the technical problem that there is no data collection and analysis of the operation data of the sewage unloading system, and the inability to identify potential faults through data trends, and the only way to deal with faults after the fact, which leads to resource waste and increased driving risks, a data acquisition module is adopted to comprehensively collect key operation data of the sewage unloading unit and vacuum unit. Multi-interface compatibility and secure transmission technology are used to ensure that the data is uploaded in real time and accurately. The data analysis module performs Kalman filtering to reduce noise in the collected data, multi-parameter fusion to mine correlation trends, and a three-level early warning mechanism to transform the traditional post-fault handling into pre-warning and graded intervention, thereby reducing driving risks;

[0036] (2) In response to the technical problems that the system does not operate on demand, requires continuous operation, has high energy consumption, lacks a unified monitoring system, relies on manual inspection, lacks specialized technology to monitor hose recovery status, poses driving risks with hose in operation, and cannot achieve real-time perception and refined management of equipment status, an intelligent control module is adopted to make judgments through three-level early warning, optimize the start-stop logic of multiple pump groups using an improved vacuum pump collaborative control algorithm, dynamically allocate the load, implement predictive start-stop based on demand fluctuations, and link the soft start current and load allocation coefficient to ensure status synchronization. Furthermore, the intelligent monitoring module monitors the real-time operating status of the system, realizing the hose recovery status monitoring function of the sewage unloading unit and the low-power operation function of the system. Attached Figure Description

[0037] Figure 1 A schematic diagram of the intelligent monitoring system for vacuum waste removal equipment based on multi-data fusion provided by the present invention;

[0038] Figure 2 This is a schematic diagram of the intelligent control module.

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

[0040] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0041] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0042] Example 1, see Figure 1 The intelligent monitoring system for vacuum waste removal equipment based on multi-data fusion provided by the present invention includes a sensor installation module, a data acquisition module, a data analysis module, an intelligent control module, and an intelligent monitoring module.

[0043] The sensor installation module specifically refers to the installation of sensors in preparation for the data acquisition module.

[0044] The data acquisition module specifically acquires sensor signals from the unloading unit and the vacuum unit through the unloading unit data acquisition sub-module and the vacuum unit data acquisition sub-module, and then transmits the vacuum unloading data securely to the data analysis module through the unloading unit data communication sub-module.

[0045] The data analysis module specifically stores vacuum unloading data, performs fault early warning analysis based on the data, performs single-parameter noise reduction and multi-parameter fusion, and performs three-level early warning judgment on abnormal parameters.

[0046] The intelligent control module specifically optimizes the start-stop logic of multiple pump groups based on the three-level early warning judgment, adopts an improved vacuum pump collaborative control algorithm, dynamically allocates the load, implements predictive start-stop of pump groups in combination with demand fluctuations, and combines the soft start current with the load allocation coefficient to ensure state synchronization.

[0047] The intelligent monitoring module specifically monitors the real-time operating status of the system, enabling it to monitor the hose recovery status of the wastewater unloading unit and perform low-power operation of the system.

[0048] Example 2, see Figure 1 This embodiment is based on the above embodiment, and the sensor installation module specifically includes an installation position detection sensor, a vacuum sensor, a temperature sensor, a current sensor, a voltage sensor, and a vibration sensor.

[0049] The positioning detection sensor is installed at the hose retraction point of the unloading unit to detect whether the unloading hose of the unloading unit has been retracted into place after the unloading operation is completed; the data output terminal of the positioning detection sensor is connected to the data input terminal of the unloading unit data acquisition submodule in the data acquisition module through a data transmission signal line.

[0050] The vacuum sensor is installed on the vacuum unit pipeline and is used to detect the real-time vacuum pressure of the vacuum sewage unloading system. Its signal output terminal is connected to the vacuum pressure detection port of the vacuum unit data acquisition submodule in the data acquisition module through a data transmission signal line.

[0051] The temperature sensor is installed on the housing of the cam pump, vacuum pump, and unit motor to detect the real-time operating temperature of the vacuum sewage unloading equipment. Its signal output terminal is connected to the temperature detection port of the vacuum unit data acquisition submodule through a data transmission signal line.

[0052] The current sensor is installed on the main cable inside the vacuum unit's electrical control cabinet and is used to detect the real-time operating current of the vacuum waste removal system. Its signal output terminal is connected to the current detection port of the vacuum unit's data acquisition submodule through a data transmission signal line.

[0053] The voltage sensor is installed on the main circuit breaker in the vacuum unit's electrical control cabinet and is used to detect the real-time power supply voltage of the vacuum sewage unloading system. Its signal output terminal is connected to the voltage detection port of the vacuum unit's data acquisition submodule through a data transmission signal line.

[0054] The vibration sensor is installed on the housing of the cam pump of the vacuum unit and is used to detect the real-time vibration of the cam pump of the vacuum unit. Its signal output terminal is connected to the vibration detection port of the data acquisition submodule of the vacuum unit through a data transmission signal line.

[0055] Example 3, see Figure 1 This embodiment is based on the above embodiment, and the data acquisition module includes a wastewater unloading unit data acquisition submodule, a wastewater unloading unit data communication submodule, and a vacuum unit data acquisition submodule;

[0056] The data acquisition submodule of the unloading unit is installed in the on-site unloading unit electrical control box. It is connected to the data output terminal of the position detection sensor through a data transmission signal line and to the signal input terminal of the unloading unit data communication submodule through an RS485 industrial communication network. It is used to collect signals such as the hose recovery status of the unloading unit, the vacuum pressure at the unloading unit, and the manual confirmation button, as well as output the hose recovery status signal of the unloading unit.

[0057] The data communication submodule of the unloading unit is installed at the end of each unloading line. Its signal input end is connected to the unloading unit data acquisition module, and its signal output end is connected to the input end of the data analysis module through an optical fiber industrial network. It is used to receive the unloading unit operating status signals monitored by the unloading unit data acquisition submodule in real time, convert the acquired signals, and communicate with the data analysis module through optical fiber to realize real-time monitoring of the safe operating status of the unloading unit. Through data encryption technology, identity authentication technology, access control technology, secure communication protocol, data integrity verification and other technologies, secure communication of key data of vacuum unloading is realized.

[0058] The vacuum unit data acquisition submodule is installed inside the vacuum unit electrical control cabinet in the vacuum pump room and is connected to the input terminal of the data analysis module via an industrial Ethernet. It is used to receive signals from vacuum sensors, temperature sensors, current sensors, voltage sensors, and vibration sensors installed in the vacuum unit in real time, and is also connected to the control system PLC via an industrial Ethernet to realize communication and reading of real-time operating data of the vacuum unit.

[0059] The data acquisition submodules of the wastewater unloading unit and the vacuum unit are designed with various data interfaces, including RS232, RS485, fiber optic, Ethernet, and LoRa, for compatibility design with various interface devices.

[0060] Example 4, see Figure 1 This embodiment is based on the above embodiment, and the data analysis module includes a data storage and analysis server, a switch, and a firewall;

[0061] The data storage and analysis server is installed in the information computer room and is used to store all the operating data collected by the system, including basic data and equipment operating data uploaded by sub-servers, as well as to provide background data analysis for the system. The background data analysis mainly includes fault early warning analysis, energy consumption analysis, etc.

[0062] The fault early warning analysis involves real-time acquisition of system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current. A Kalman filter algorithm is used to process and fuse these data points. Through comparison of multiple data sets, early warning of abnormal equipment conditions is achieved, including the following:

[0063] Parameter acquisition involves real-time collection of four core data types: system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current. The sampling frequency is set to 10Hz to ensure the capture of instantaneous anomalies.

[0064] Single-parameter Kalman filter noise reduction eliminates sensor noise and outputs purified parameters;

[0065] Multi-parameter fusion, through extended Kalman filtering, uncovers parameter correlations and outputs a fused state vector, capturing implicit relationships between parameters. It combines static values ​​and dynamic change rates, encompassing both the device's current absolute state and its changing trends. This overcomes the limitations of single parameters and improves data reliability. The formula used is as follows:

[0066] ;

[0067] In the formula, This represents the fused state vector at time t. This indicates the vacuum level of the purified system. This indicates the liquid level in the vacuum tank after purification. This indicates the temperature of the cam pump after purification. This indicates the current of the cam pump after purification. Indicates the rate of change of vacuum degree. The current represents the rate of change, and T represents the vector transpose.

[0068] The three-level early warning system is as follows: when the deviation of the calculated parameters from the rated values ​​exceeds 5%, a level 1 early warning is triggered, indicating a minor anomaly; when the deviation between the actual correlation parameters and the normal model exceeds 10%, a level 2 early warning is triggered, indicating a correlation anomaly; when the parameters change drastically and exceed the normal range, a level 3 early warning is triggered, indicating an emergency fault.

[0069] The calculated parameters represent the actual values ​​of the core operating parameters of the equipment obtained after data acquisition and Kalman filtering noise reduction.

[0070] The associated parameter relationship represents the actual collaborative relationship between multiple parameters;

[0071] Level 1 warning: The system records the anomaly and reminds maintenance personnel to conduct inspections; Level 2 warning: The system automatically adjusts the load and triggers audible and visual alarms; Level 3 warning: The system immediately cuts off the power to some pump units to prevent equipment damage and sends an emergency fault signal to the staff.

[0072] The switch is installed in the information room and is a network device used for forwarding electrical signals, providing a dedicated electrical signal path for any two network nodes connected to the switch.

[0073] The firewall is installed in the information computer room to protect computer systems and networks from external threats.

[0074] By performing the above operations, the data acquisition module comprehensively collects key operating data from the unloading unit and vacuum unit. Multi-interface compatibility and secure transmission technology ensure real-time and accurate data upload. The data analysis module performs Kalman filtering for noise reduction on the collected data, integrates multiple parameters to uncover correlation trends, and employs a three-level early warning mechanism to transform traditional post-fault handling into pre-fault warning and tiered intervention, reducing operational risks. This solves the technical problem of wasting resources and increasing operational risks due to the lack of data collection and analysis of the unloading system's operating data, the inability to identify potential faults through data trends, and the reliance on post-fault handling.

[0075] Example 5, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. The intelligent control module uses an improved vacuum pump collaborative control algorithm to optimize the start-stop logic of multiple pump groups based on the three-level early warning judgment, dynamically allocates the load, and makes the load of the operating pump group accurately match the actual demand. It operates at full load during non-full-time periods, reduces ineffective energy consumption, and avoids single pump overload, reducing equipment wear and failure risks caused by long-term single pump overload, extending the overall service life of the pump group. A soft-start strategy is introduced to avoid current surge problems, including the following:

[0076] Dynamic load distribution across multiple pump groups is achieved by calculating the optimal load distribution coefficient for each pump based on its real-time operating parameters, thus avoiding single-pump overload. The formula used is as follows:

[0077] ;

[0078] In the formula, Let represent the optimal load distribution coefficient of the i-th pump at time t. Let represent the real-time load rate of the i-th pump at time t. This represents the health status coefficient of the i-th pump at time t. This represents the set of pump units that are in operation at time t. This represents the soft-start correction factor for the i-th pump. This represents the rated load reference coefficient of the i-th pump at time t, and j is used to iterate through all pumps currently in operation.

[0079] Predictive start-stop of pump units, combined with demand fluctuations, dynamically adjusts the number of pneumatic pumps to reduce frequent start-stops. The formula used is as follows:

[0080] ;

[0081] In the formula, This indicates the number of pumps that should be started at time t. This indicates the rounding up sign, ensuring that load requirements are met. Let t represent the total load demand of the system at time t, and k represent the demand change sensitivity coefficient. This represents the rate of change in total load demand. This represents the maximum load distribution factor in the pump set at time t. This indicates the rated load capacity of a single pump. Indicates the redundancy coefficient;

[0082] The soft-start current is calculated and linked to the load distribution factor to ensure that the current rises synchronously with the load during startup, thereby improving the safety of the startup process. The formula used is as follows:

[0083] ;

[0084] In the formula, This represents the real-time current during the soft-start process at time t. T1 represents the rated current of the motor, and T2 represents the time constant for soft starting. This indicates the pump's base no-load current.

[0085] Example 6, see Figure 1 This embodiment is based on the above embodiment, and the intelligent monitoring module includes an information management computer, a video camera, and a video recorder;

[0086] The information management computer is installed in the control room monitoring room and is connected to the data analysis module via an industrial Ethernet. The video camera is installed in the vacuum pump room and is connected to the video recorder via an Ethernet.

[0087] The information management computer includes a system display computer, an operating console, a power supply, etc., used to display the real-time operating status of the intelligent monitoring system for vacuum unloading equipment; and to realize the remote monitoring function of the hose recovery status of the unloading unit and the function of low-power system operation.

[0088] The system remotely monitors the hose recovery status of the sewage unloading unit. The information management computer is connected to the data analysis module via Ethernet. The data analysis module receives the hose recovery status signal collected by the sewage unloading unit data acquisition submodule in the data acquisition module. After obtaining the signal from the data analysis module, the information management computer displays the hose recovery status in real time through the system display interface. The hose recovery status includes recovered, not recovered, and being recovered, thereby realizing remote monitoring of the hose recovery status of the sewage unloading unit, effectively avoiding train accidents caused by running with hoses on, and ensuring the safety of train operation and personnel.

[0089] In the low-power operating system, the information management computer acts as a monitoring terminal. During system idle periods, it automatically reduces the brightness of the display screen and enters sleep mode, maintaining only the core data receiving function. At the same time, the video cameras are remotely controlled to start and stop and adjust the shooting frame rate through the information management computer. The frame rate is reduced during unnecessary periods to reduce the energy consumption of video data transmission and processing, indirectly supporting the overall low-power operation of the system.

[0090] By performing the above operations, the intelligent control module uses a three-level early warning judgment, an improved vacuum pump collaborative control algorithm to optimize the start-stop logic of multiple pump groups, dynamically allocate the load, implement predictive start-stop based on demand fluctuations, and link the soft start current and load allocation coefficient to ensure state synchronization. Furthermore, the intelligent monitoring module monitors the real-time operating status of the system, realizing the monitoring function of hose recovery status of the sewage unloading unit and the low-power operation function of the system. This solves the technical problems of the system not having logic for on-demand operation, requiring continuous operation, high energy consumption, lack of a unified monitoring system, reliance on manual inspection, lack of specialized technology to monitor hose recovery status, the risk of driving with hoses on, and the inability to achieve real-time perception and refined management of equipment status.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0093] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for vacuum waste disposal equipment based on multi-data fusion, characterized in that: The system includes a sensor module, a data acquisition module, a data analysis module, an intelligent control module, and a smart monitoring module; The sensor installation module specifically refers to the installation of sensors in preparation for the data acquisition module. The data acquisition module specifically acquires sensor signals from the unloading unit and the vacuum unit through the unloading unit data acquisition sub-module and the vacuum unit data acquisition sub-module, and then transmits the vacuum unloading data securely to the data analysis module through the unloading unit data communication sub-module. The data analysis module specifically stores vacuum unloading data, performs fault early warning analysis based on the data, performs single-parameter noise reduction and multi-parameter fusion, and performs three-level early warning judgment on abnormal parameters. The intelligent control module specifically optimizes the start-stop logic of multiple pump groups based on the three-level early warning judgment, adopts an improved vacuum pump collaborative control algorithm, dynamically allocates the load, implements predictive start-stop of pump groups in combination with demand fluctuations, and combines the soft start current with the load allocation coefficient to ensure state synchronization. The intelligent monitoring module specifically monitors the real-time operating status of the entire system, enabling it to monitor the hose recovery status of the wastewater unloading unit and perform low-power operation of the system.

2. The intelligent monitoring system for vacuum waste discharge equipment based on multi-data fusion according to claim 1, characterized in that: The data analysis module includes a data storage and analysis server, a switch, and a firewall; The data storage and analysis server is installed in the information computer room and is used to store all the operating data collected by the system, including basic data and equipment operating data uploaded by sub-servers, as well as to provide background data analysis for the system. The background data analysis mainly includes fault early warning analysis. The fault early warning analysis involves real-time acquisition of system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current. A Kalman filter algorithm is used to process and fuse these data points. Through comparison of multiple data sets, early warning of abnormal equipment conditions is achieved, including the following: Parameter acquisition involves real-time acquisition of four types of core data: system vacuum level, vacuum tank liquid level, cam pump temperature, and cam pump current. Single-parameter Kalman filter noise reduction, outputting purified parameters; Multi-parameter fusion, through extended Kalman filtering, mines parameter correlations, outputs a fused state vector, captures the implicit correlations between parameters, and combines static values ​​and dynamic rates of change; The Level 3 early warning system determines whether a minor anomaly is triggered when the calculated parameter deviates from the rated value by more than 5%. If the deviation between the actual correlation parameters and the normal model exceeds 10%, a level 2 warning is triggered, indicating an abnormal correlation. The monitoring parameters changed drastically, exceeding the normal range and triggering a level three warning, which is an emergency fault. Level 1 warning: The system records the anomaly and reminds maintenance personnel to conduct inspections. Level 2 warning: The system automatically adjusts the load and triggers an audible and visual alarm. A Level 3 warning will immediately cut off the power supply to some pump units to prevent equipment damage and send an emergency fault signal to the staff. The switch is installed in the information room and is a network device used for forwarding electrical signals, providing a dedicated electrical signal path for any two network nodes connected to the switch. The firewall is installed in the information computer room to protect computer systems and networks from external threats.

3. The intelligent monitoring system for vacuum waste discharge equipment based on multi-data fusion according to claim 1, characterized in that: The intelligent control module uses a three-level early warning judgment, an improved vacuum pump collaborative control algorithm to optimize the start-stop logic of multiple pump groups, dynamically allocate the load, and introduce a soft-start strategy. Dynamic load distribution for multiple pump groups: Based on the real-time operating parameters of each vacuum pump, the optimal load distribution coefficient for each pump is calculated. Predictive start-up and shutdown of pump sets, dynamically adjusting the number of pneumatic pumps based on demand fluctuations; Calculate the soft-start current and link it with the load distribution factor to ensure that the current rises synchronously with the load during startup.

4. The intelligent monitoring system for vacuum waste discharge equipment based on multi-data fusion according to claim 1, characterized in that: The intelligent monitoring module includes an information management computer, a video camera, and a video recorder; The information management computer is installed in the control room monitoring room and is connected to the data analysis module via an industrial Ethernet. The video camera is installed in the vacuum pump room and is connected to the video recorder via an Ethernet. The information management computer is used to display the real-time operating status of the intelligent monitoring system for vacuum unloading equipment; to realize the remote monitoring function of the hose recovery status of the unloading unit and the function of low-power operation of the system.

5. The intelligent monitoring system for vacuum waste discharge equipment based on multi-data fusion according to claim 1, characterized in that: The data acquisition module includes a wastewater unloading unit data acquisition submodule, a wastewater unloading unit data communication submodule, and a vacuum unit data acquisition submodule; The data acquisition submodule of the unloading unit is installed in the on-site unloading unit electrical control box. It is connected to the data output terminal of the position detection sensor through a data transmission signal line, and to the signal input terminal of the unloading unit data communication submodule through an RS485 industrial communication network. The data communication submodule of the unloading unit is installed at the end of the track of each unloading line. Its signal input end is connected to the data acquisition module of the unloading unit, and its signal output end is connected to the input end of the data analysis module through the fiber optic industrial network. Secure communication of key data of vacuum unloading is achieved through data encryption technology, identity authentication technology, access control technology, secure communication protocol, and data integrity verification technology. The vacuum unit data acquisition submodule is installed inside the vacuum unit electrical control cabinet in the vacuum pump room and is connected to the input terminal of the data analysis module via an industrial Ethernet. The data acquisition submodules of the sewage unloading unit and the vacuum unit are designed with various data interfaces to ensure compatibility with various interface devices.

6. The intelligent monitoring system for vacuum waste disposal equipment based on multi-data fusion according to claim 1, characterized in that: The sensor installation module specifically includes a positioning detection sensor, a vacuum sensor, a temperature sensor, a current sensor, a voltage sensor, and a vibration sensor.

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