Intelligent dispensing data acquisition system based on wireless radio frequency and control method

Abstract

The invention discloses an intelligent separate injection data acquisition system based on wireless radio frequency and a control method, the system comprises a power supply unit, a communication unit, a parameter acquisition unit, a flow regulation and control unit and a main control unit, the power supply unit comprises a non-contact power supply conversion module, a power supply management system and a high-energy rechargeable battery; the communication unit comprises a remote two-way communication module, an antenna, a power line carrier module and a wireless communication module; the parameter acquisition unit comprises a temperature sensor, a pressure sensor, a vibration sensor, a sound sensor and a flow sensor; the flow regulation and control unit comprises an intelligent water distributor, a flow control valve and a motor; the main control unit comprises a controller, a communication processor and a wireless communication base station; establishing a radio frequency identification tag which is installed on a key part of the dispensing equipment and is used for storing information of the dispensing equipment; and establishing a wireless radio frequency reader-writer which is used for reading and writing the information in the RFID tag and sending the read information to the main control unit.

Description

Technical Field

[0001] The present invention relates to the technical field of dispensing data collection, and in particular to an intelligent dispensing data collection system and a control method based on wireless radio frequency. Background Art

[0002] Most of the main domestic oil fields are disturbed by interlayer and intralayer factors, which seriously affects the effect of water drive development and reduces the oil field recovery rate. The development of stratified water injection technology has improved the above problems. However, there are long measurement and adjustment cycles and multiple versions of control software. To solve the above problems, the software that can monitor the production parameters such as injection volume and injection pressure of the injection well layer segment has the functions of automatic measurement and adjustment, sealing verification, pressure recovery test and water absorption index test. It promotes the precise development of oil reservoirs and has a high promotion and application value. Wireless data collection of injection pipes through wireless radio frequency technology is the pain point of data collection at present, and data cannot be effectively collected for different injection points. At the same time, the flow rate of injection through online control cannot be intelligently adjusted and controlled. The concentric injection technology has a large workload and a low success rate, which affects the water injection effect and limits the further promotion of stratified water injection technology.

[0003] Therefore, it is necessary to provide an intelligent dispensing data acquisition system and control method based on wireless radio frequency to solve the above technical problems. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract of the specification and the title of the invention of this application to avoid blurring the purpose of this section, the abstract of the specification and the title of the invention, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an intelligent dispensing data acquisition system based on wireless radio frequency, comprising a power supply unit, a communication unit, a parameter acquisition unit, a flow control unit and a main control unit, wherein the power supply unit comprises a contactless power conversion module, a power management system and a high-energy rechargeable battery, and the communication unit comprises a remote two-way communication module, an antenna, a power carrier module and a wireless communication module; the parameter acquisition unit comprises a temperature sensor, a pressure sensor, a vibration sensor, a sound sensor and a flow sensor; the flow control unit comprises an intelligent water distributor, a flow control valve and a motor; the main control unit comprises a controller, a communication processor and a wireless communication base station;

[0006] Establishing a radio frequency identification (RFID) tag, which is installed on a key component of the dispensing device and is used to store information of the dispensing device;

[0007] Establishing a wireless radio frequency reader / writer for reading and writing information in the RFID tag and sending the read information to the main control unit;

[0008] The data transmission module is used to wirelessly transmit the data processed by the main control unit to a remote monitoring center; the data storage module is used to store the data processed by the main control unit.

[0009] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system described in the present invention, wireless radio frequency is divided into low frequency, high frequency and ultra-high frequency. The low frequency and the high frequency use wireless radio frequency identification technology in the form of electromagnetic induction. The ultra-high frequency uses electromagnetic propagation or electromagnetic backscatter coupling. The wireless communication module includes ZigBee terminal nodes and ZigBee coordinators that communicate with each other. The ZigBee terminal nodes are connected to the reader / writer via an RS232 to TTL module, and the ZigBee coordinator is connected to the data acquisition management module via an RS232 to TTL module.

[0010] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system of the present invention, the main control unit includes an alarm module for triggering an alarm signal when an abnormal situation is detected.

[0011] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system described in the present invention, the RFID smart tag includes a data acquisition module, a microprocessor, a data output module, a power management module and a power supply; the data acquisition module includes an excitation circuit and a monitoring circuit, the excitation circuit provides an excitation voltage to excite the sensor to sense the status information of the monitored object, and the monitoring circuit is used to pick up the information sensed by the sensor; the microprocessor is connected to the data acquisition module, and sends monitoring commands to the data acquisition module at regular intervals, and at the same time receives and processes the status information uploaded by the data acquisition module; the microprocessor is connected to the data output module, receives the data acquisition command transmitted from the data output module, and transmits the collected status information to the data output module; the power management module effectively distributes the electric energy provided by the power supply to the various component modules of the RFID smart tag; the power supply is a storage battery, a dry cell battery or a button cell battery.

[0012] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system described in the present invention, the wireless communication base station includes a data acquisition device, a radio frequency communication device, a server and a management terminal.

[0013] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system described in the present invention, the parameter acquisition unit uploads data to the control unit via 5G radio frequency.

[0014] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system described in the present invention, the main control unit includes an information portal layer, a function layer, a public service software layer, a support software layer, a data layer and a hardware infrastructure layer.

[0015] As a preferred solution of the wireless radio frequency-based intelligent dispensing data acquisition system of the present invention, the communication unit includes a preamplifier, a filter and a multi-way analog switch.

[0016] As a preferred solution of the control method of the present invention, a parameter acquisition unit performs multi-modal data acquisition to obtain multi-modal data; a multi-sensor data fusion algorithm is used to fuse the multi-modal data into a unified data set; the method includes the following steps:

[0017] Establish a basic information database;

[0018] Make RFID tags;

[0019] Multi-target recognition and localization framework;

[0020] Build a data management system;

[0021] Build a data analysis platform;

[0022] The intelligent data collection system monitors data missing from the perception layer to the application layer. Data missing will affect the data processing results and cause deviations in the data mean. In response to data missing, this application uses weighted methods, interpolation methods, or estimation methods to handle data missing.

[0023] As a preferred solution of the control method described in the present invention, the multi-sensor fusion algorithm includes data-level fusion, feature-level fusion and judgment-level fusion. When realizing multi-sensor information fusion, there are two commonly used fusion processing structures, namely centralized fusion structure and distributed fusion structure.

[0024] The beneficial effects of the present invention are as follows: by using wireless radio frequency technology, the remote control platform is linked to the ground controller at two levels through optical fiber or mobile communication network, and the ground controller and the underground intelligent water distributor communicate in two directions through pressure waves. The entire intelligent injection data acquisition system and control method are centrally controlled by combining wireless radio frequency technology with computer information systems, forming a management mode of integrated regulation, equipment automation, information integration, and process visualization for the entire system, thereby effectively ensuring the accuracy, timeliness, and storage security of the data. The data acquisition system using wireless radio frequency technology and FPGA technology collects and organizes different data by connecting different sensors, thereby realizing a portable connection between originally unrelated systems and data, so that the connection system between the two can be applied to a wider range, making data acquisition more accurate, faster, and safer. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0026] in:

[0027] Figure 1 A data verification flow chart of an intelligent dispensing data acquisition system and control method based on wireless radio frequency according to an embodiment of the present invention;

[0028] Figure 2 A flow chart of a data acquisition device of an intelligent dispensing data acquisition system and control method based on wireless radio frequency according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0030] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1

[0033] Reference Figure 1 The first embodiment of the present invention is an intelligent dispensing data acquisition system based on wireless radio frequency, characterized in that it includes a power supply unit, a communication unit, a parameter acquisition unit, a flow control unit and a main control unit, the power supply unit includes a contactless power conversion module, a power management system and a high-energy rechargeable battery, the communication unit includes a remote two-way communication module, an antenna, a power carrier module and a wireless communication module; the parameter acquisition unit includes a temperature sensor, a pressure sensor, a vibration sensor, a sound sensor and a flow sensor; the flow control unit includes an intelligent water distributor, a flow control valve and a motor; the main control unit includes a controller, a communication processor, and a wireless communication base station;

[0034] Establishing a radio frequency identification (RFID) tag, which is installed on a key component of the dispensing device and is used to store information of the dispensing device;

[0035] Establishing a wireless radio frequency reader / writer for reading and writing information in the RFID tag and sending the read information to the main control unit;

[0036] The data transmission module is used to wirelessly transmit the data processed by the main control unit to the remote monitoring center; the data storage module is used to store the data processed by the main control unit. Since the signal backscattered by the RFID tag cannot be transmitted to the RFID antenna, and the laser sensor can scan the object but cannot realize the positioning function, part of the information received by the laser sensor is missing. Therefore, if multi-target recognition and positioning are to be realized, the received RFID and laser information must be processed separately. When the multi-targets to be identified and located are located in the detection interval and the RFID tag signal of the target is weak, or there is no detection, the target position detected at the last moment and the laser clustering must be used to achieve the positioning purpose. The dynamic framework for continuous positioning that integrates RFID and laser information is mainly composed of an RFID reader and a laser sensor. The two are responsible for obtaining dynamic multi-target related information. After calculating the RFID phase information, phase difference estimation, laser clustering and laser clustering estimation rate, speed matching is implemented, and multi-target recognition and positioning are completed based on particle filtering according to the matching results. Wireless radio frequency is divided into low frequency, high frequency and ultra-high frequency. The low frequency and high frequency adopt electromagnetic induction wireless radio frequency identification technology. The ultra-high frequency adopts electromagnetic propagation or electromagnetic backscatter coupling. The wireless communication module includes a ZigBee terminal node and a ZigBee coordinator that communicate with each other. The ZigBee terminal node is connected to the reader through an RS232 to TTL module, and the ZigBee coordinator is connected to the data acquisition management module through an RS232 to TTL module. The main control unit includes an alarm module for triggering an alarm signal when an abnormal situation is detected. The alarm module will be triggered when a system or sensor fails. The RFID smart tag includes a data acquisition module, a microprocessor, a data output module, a power management module and a power supply; the data acquisition module includes an excitation circuit and a monitoring circuit, the excitation circuit provides an excitation voltage to excite the sensor to sense the state information of the monitored object, and the monitoring circuit is used to pick up the information sensed by the sensor; the microprocessor is connected to the data acquisition module, regularly sends monitoring commands to the data acquisition module, and receives and processes the state information uploaded by the data acquisition module; the microprocessor is connected to the data output module, receives the data acquisition command transmitted by the data output module, and transmits the collected state information to the data output module; the power management module effectively distributes the electric energy provided by the power supply to each component module of the RFID smart tag; the power supply is a storage battery, a dry cell battery or a button cell; the data acquisition system is formed by combining wireless radio frequency technology with FPGA technology. The wireless communication base station includes a data acquisition device, a radio frequency communication device, a server and a management terminal. The main control unit includes an information portal layer, a function layer, a public service software layer, a support software layer, a data layer and a hardware infrastructure layer.Most traditional wireless data acquisition devices use a single sensor to perform single variable analysis on the injection, and the collection and analysis of injection parameters are relatively one-sided. Moreover, the device installation environment is harsh, and single data upload is easily disturbed, resulting in unsuccessful data upload. In addition, the sensor may have data anomalies or missing when collecting data, and there is no preliminary judgment on the correctness of the original data, which affects to a certain extent.

[0037] Affects the validity of data. Compared with traditional data acquisition devices, the acquisition device of the present application can use multiple sensors to comprehensively obtain intelligent dispensing data parameters, provide richer data for dispensing prediction and analysis, and improve the accuracy of prediction to a certain extent; in addition, abnormal data is processed and corrected from the source to improve data reliability; in response to the problem of unreliable data transmission, a multi-path data redundant upload method is adopted. On the basis of ensuring that data can be uploaded reliably, the reliability of data is also ensured to a certain extent. The main idea this time is multi-sensor parameter acquisition, preliminary processing and multi-path data upload to ensure the comprehensiveness, accuracy and stability of the data as much as possible. In addition, considering the actual application scenario of the device, an independent field power supply device for the device is also designed.

[0038] Example 2

[0039] Reference Figure 2 The present embodiment is different from the previous embodiment in that the present embodiment provides a control method, including the wireless radio frequency based intelligent dispensing data acquisition system, and performing multi-modal data acquisition in a parameter acquisition unit to obtain multi-modal data; using a multi-sensor data fusion algorithm to fuse the multi-modal data into a unified data set; having the following advantages:

[0040] (1) Improve the credibility of the system. The measurement results of a single sensor can be verified and inferred through the detection data of other related sensors. Moreover, the data measured by different sensors are more comprehensive and can detect the target status information more comprehensively.

[0041] (2) Improved system reliability and robustness. When a single sensor fails, it will cause data loss or errors, affecting the normal operation of the entire system. Multi-sensor data fusion increases the system's fault tolerance due to data redundancy between sensors.

[0042] (3) The accuracy of measurement data is improved. The fusion of multi-sensor measurement information can achieve a comprehensive effect on abnormal data and improve data accuracy and precision.

[0043] Multi-sensor data fusion includes data-level fusion, feature-level fusion and decision-level fusion.

[0044] Data-level fusion is a method of directly fusing raw data from similar sensors and then performing feature extraction and state estimation on the data. It is a lower-level fusion method that can retain the original information of the sensor data to the greatest extent, with high precision and accuracy.

[0045] Feature-level fusion is that after each sensor collects the observed target data, it extracts features to obtain feature vectors, and then fuses the feature vectors obtained by each sensor to obtain joint features to estimate the target state.

[0046] In decision-level fusion, each sensor extracts individual features of the target data and performs state estimation, and then the estimation results of the sensors are fused together to obtain a decision-level fusion result.

[0047] Because the multi-sensor fusion algorithm includes data-level fusion, feature-level fusion and judgment-level fusion, there are two commonly used fusion processing structures when realizing multi-sensor information fusion, namely centralized fusion structure and distributed fusion structure. The intelligent dispensing data acquisition device relies on the intelligent dispensing data system and is the collection and transmission part of the system. The device mainly has the following functions: collection of dispensing parameters, data preprocessing and preliminary fusion, data transmission and power supply. The device connects multiple sensors to collect multiple parameters of multiple dispensing holes, then preprocesses the data, judges abnormal data and fills in missing data, and then extracts features and preliminarily fuses the data; after the data processing is completed, the data is finally uploaded to the concentrator through the radio frequency 433MHz, and the verification mechanism, retransmission mechanism and multi-channel redundancy mechanism are used to ensure the reliability of communication. Generally, data preprocessing mainly includes the following steps. The first is to calibrate the sensors and related equipment and optimize the sensor installation location reasonably, so as to eliminate instrument and equipment errors as well as human errors as much as possible to improve the accuracy of the collected data and ensure the accuracy of the data. In addition, for the collected data, it is necessary to judge and locate the abnormalities of the data, and eliminate the abnormal data or replace it according to the time series of the data, determine the appropriate time interval, compensate for the missing data that may occur in the collection, and ensure the continuity of the data.

[0048] Data anomalies are generally divided into two situations. One is that data is collected, but the data shows obvious anomalies. The other is that data is not collected and there is data missing.

[0049] 1) Save the outliers in the sampled data and participate in the later data analysis and statistics;

[0050] 2) Remove outliers from the sampled data and ignore this group of data;

[0051] 3) It allows the removal of outliers, and according to the analysis of the characteristics of the data time series, appropriate sensor data is supplemented to replace the outliers, and the supplementary data is used to realize the subsequent data analysis;

[0052] 4) Find the cause of data anomalies, correct the abnormal values, and obtain correct data.

[0053] In the actual processing process, it is difficult to distinguish the outliers caused by errors or interference in the data. Therefore, in the process of data statistics, we must make full use of mathematical statistics methods to analyze the errors, so as to obtain accurate data as correctly as possible and improve the accuracy of data analysis.

[0054] According to different types of sensors, according to the time of data collection, the time series of sensor data of this type is established, and the average value of the series is obtained by calculation and standard deviation X S , if the newly collected data S j The difference from the mean is greater than the standard deviation X S Three times of The data is considered abnormal and is removed;

[0055] in,

[0056] is the mean value of the sequence samples,

[0057]

[0058] Represents the standard deviation of the series.

[0059] Multi-sensor data fusion and SSA-LSTM-Transformer algorithm

[0060] The improved sparrow search algorithm (SSA) improves the algorithm's global exploration and local optimization capabilities and reduces the risk of falling into local optimality by introducing heterogeneous individual theoretical memory mechanisms and uncertainty strategies. The improved SSA includes the following four roles:

[0061] 1) Detector: Responsible for global search, exploring the vast area of ​​the search space, the detector introduces an adaptive particle swarm optimization (PSO) mechanism, which improves the global exploration capability through dynamic adjustment of speed and position. The inertia weight and learning factor of PSO enable the detector to flexibly adjust the search direction, increase the diversity of the search, and avoid falling into the local optimum too early.

[0062] 2) Developers: Focus on local optimization, and refine the explored excellent areas by combining adaptive gradient descent (ADAM) and Newton's method. ADAM dynamically adjusts the learning rate to speed up the convergence, while Newton's method uses second-order derivative information to improve the accuracy and stability of local optimization, and can more accurately approach the local optimal solution.

[0063] 3) Hybrid individuals: Flexibly switch between detectors and developers, dynamically select the most suitable search strategy based on the current optimization stage and population status. Hybrid individuals adjust the balance between global search and local optimization by evaluating population diversity and fitness changes to ensure that the algorithm can effectively adapt to different optimization needs.

[0064] 4) Alarm: Responsible for monitoring the search status of the group and identifying potential local optimal traps. By recording and analyzing the behavior of the population, the alarm can sound an alarm and adjust the individual search path to prevent falling into the local optimal state.

[0065] The improved SSA also introduces a memory mechanism and uncertainty strategy. The memory mechanism allows individuals to record the best historical solutions to guide the current search process: the detector uses historical information to expand the search range, the developer improves the local optimization accuracy, and the hybrid individual adjusts the strategy through comprehensive memory information. The uncertainty strategy increases the diversity and breadth of the search. The detector and the developer introduce random perturbations to prevent falling into the local optimum. The hybrid individual adjusts the uncertainty strategy according to the optimization stage, and the alarm flexibly intervenes in the search path by monitoring the individual status. These improvements enable the new SSA to show better performance in complex search spaces, effectively balance global search and local optimization, and enhance the adaptability and robustness of the algorithm.

[0066] Sparrow Search Algorithm (SSA) optimizes data fusion weights

[0067] When processing multimodal data, traditional methods often simply concatenate the data and fail to fully utilize the complementarity of each data modality. This study proposes a weighted fusion strategy and combines it with the improved SSA-LSTM-Transfommer model to fully tap the potential of each modality data. Specifically, the formula for feature fusion is as follows:

[0068] X fusion =[w mpu ·X mpu , w phone ·X phone , w f ·X f ]

[0069] The weighted fused feature vector is input into the LSTM-Transformer model for processing. By optimizing the data fusion weights, the model achieves the best balance in the contribution of different sensor data, thereby improving the overall detection performance.

[0070] Sparrow Search Algorithm (SSA) Optimizing LSTM-Transformer

[0071] The LSTM-Transformer model combines the encoder layers in the LSTM and Transformer models. The model can not only make full use of the LSTM's sensitivity to short-term changes, but also use the self-attention mechanism of the Transformer encoder layer to capture the long-term dependency characteristics of multimodal data. And enhance information interaction. This combination method effectively integrates information at different time scales and significantly improves the accuracy and real-time performance of fall detection. The introduction of the sparrow search algorithm (SSA) makes it more efficient to optimize the hyperparameters of the LSTM-Transformer model in a complex high-dimensional parameter space. The hyperparameters optimized by SSA include the hidden layer dimensions of the LSTM and Transformer models, the number of heads of the multi-head self-attention mechanism in the Transformer encoder, the number of layers of the Transformer encoder, the dropout rate, the learning rate, the batch size, the weight of sensor 1, the weight of sensor 2, and the weight of sensor 3. The upper bounds are 265, 8.6.0.5.0.001.74, 1.0, 1.0, 1.0, and the lower bounds are 10, 1, 1, 0.1.0.00001.2.0.0.0.0.0.0. The best configuration is found within the defined range of these hyperparameters through SSA.

[0072] During the optimization process, SSA first generates the initial population through a heterogeneous individual strategy, and each individual corresponds to a different set of hyperparameter configurations and data fusion weights. The heterogeneous individual strategy introduces different search algorithms, increases the diversity of the population, and avoids the search from falling into the local optimum. Subsequently, SSA uses a memory mechanism to save the best performing individuals in the current iteration in each iteration, and refers to the information of these individuals in subsequent iterations to guide other individuals to evolve in a better direction. This mechanism effectively utilizes the information of the historical best solution and accelerates the convergence speed. At the same time, SSA also adopts an uncertainty processing strategy, which increases the randomness and diversity in the search process by introducing a random perturbation adjustment strategy. This processing method helps individuals get rid of the local optimum and explore a wider solution space. Through these improved strategies, SSA can find the parameter configuration that makes the LSTM-Transformer model perform best within the defined hyperparameter range. The optimization of these hyperparameters not only improves the accuracy and real-time performance of the model, but also enhances the data fusion capability, effectively improving the performance of the model in the detection task.

[0073] In the actual application process, due to various unstable factors, missing data is inevitable. In order to provide a complete data basis for the host computer to perform data analysis and processing, missing data filling is also an indispensable technical issue. When missing data is found in the sampling data time series, we can analyze the possible mathematical or logical relationship between the data based on the reliable data collected, and use the relationship between the data to fill the missing data.

[0074] For the stationary detection of data, time series can be divided into stationary and non-stationary. Stationary time series usually show continuous fluctuations around the mean of the sequence in the graph; while non-stationary series show different means in different time periods, and finally present a divergent state, such as continuous rise or continuous decline. The judgment of data stationarity is based on discrete sequences, and the data collected by the data acquisition device is also an equally spaced time series, avoiding discrete processing. The way of stationarity detection is to judge according to the data scatter plot, and the arithmetic method can be used to judge the stationarity according to the autocorrelation function, unit root test method and autoregression method of the data sequence. Considering the computing power and calculation amount of the device, the autocorrelation function method is used to determine the stationarity of the injection data. If the autocorrelation function is in a decreasing state and close to 0, the stationary sequence autocorrelation function will decrease much faster than the non-stationary sequence. At the same time, it is useful to determine whether the autocorrelation function in the sequence sample has a value very close to 0, which is useful for judging whether the sequence sample is in a stationary state, because this value can test whether the true value of the autocorrelation function is 0.

[0075]

[0076] If the calculated statistic Q is greater than the critical value of the confidence level a, it is considered that there is a 1-a confidence to reject all r when K>0. k The assumption is that both are 0.

[0077] In summary, through the interconnection and control method between the above power supply unit, communication unit, parameter acquisition unit, flow control unit and main control unit, the intelligent injection is more automated, which greatly saves the construction cost and provides practical experience for the intelligent transformation of stratified water injection technology.

[0078] It will be appreciated that in the development of any actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will be a routine task of design, fabrication, and production for those of ordinary skill having the benefit of this disclosure without undue experimentation.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent dispensing data acquisition system based on wireless radio frequency, characterized in that: It includes a power supply unit, a communication unit, a parameter acquisition unit, a flow control unit and a main control unit. The power supply unit includes a contactless power conversion module, a power management system and a high-energy rechargeable battery. The communication unit includes a remote two-way communication module, an antenna, a power carrier module and a wireless communication module; the parameter acquisition unit includes a temperature sensor, a pressure sensor, a vibration sensor, a sound sensor and a flow sensor; the flow control unit includes an intelligent water distributor, a flow control valve and a motor; the main control unit includes a controller, a communication processor and a wireless communication base station; Establishing a radio frequency identification (RFID) tag, which is installed on a key component of the dispensing device and is used to store information of the dispensing device; Establishing a wireless radio frequency reader / writer for reading and writing information in the RFID tag and sending the read information to the main control unit; A data transmission module, used for wirelessly transmitting the data processed by the main control unit to a remote monitoring center; The data storage module is used to store the data processed by the main control unit.

2. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: Wireless radio frequency is divided into low frequency, high frequency and ultra-high frequency. The low frequency and high frequency adopt electromagnetic induction wireless radio frequency identification technology. The ultra-high frequency adopts electromagnetic propagation or electromagnetic backscatter coupling. The wireless communication module includes ZigBee terminal nodes and ZigBee coordinators that communicate with each other. The ZigBee terminal node is connected to the reader through an RS232 to TTL module, and the ZigBee coordinator is connected to the data acquisition management module through an RS232 to TTL module.

3. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 2 is characterized in that: The main control unit includes an alarm module for triggering an alarm signal when an abnormal situation is detected.

4. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: The RFID smart tag includes a data acquisition module, a microprocessor, a data output module, a power management module and a power supply; the data acquisition module includes an excitation circuit and a monitoring circuit, the excitation circuit provides an excitation voltage to excite the sensor to sense the state information of the monitored object, and the monitoring circuit is used to pick up the information sensed by the sensor; the microprocessor is connected to the data acquisition module, sends monitoring commands to the data acquisition module at regular intervals, and receives and processes the state information uploaded by the data acquisition module; the microprocessor is connected to the data output module, receives the data acquisition command transmitted by the data output module, and transmits the collected state information to the data output module; The power management module effectively distributes the power provided by the power supply to each component module of the RFID smart tag; the power supply is a storage battery, a dry cell battery or a button cell; The data acquisition system is formed by combining wireless radio frequency technology with FPGA technology.

5. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: The wireless communication base station includes a data acquisition device, a radio frequency communication device, a server and a management terminal.

6. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: The parameter acquisition unit uploads the data to the control unit via 5G radio frequency.

7. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: The main control unit includes an information portal layer, a function layer, a public service software layer, a support software layer, a data layer and a hardware infrastructure layer.

8. The wireless radio frequency based intelligent dispensing data acquisition system according to claim 1 is characterized in that: The communication unit includes a preamplifier, a filter and a multi-way analog switch.

9. A control method, comprising the wireless radio frequency based intelligent dispensing data acquisition system according to any one of claims 1 to 8, and performing multimodal data acquisition in a parameter acquisition unit to obtain multimodal data; using a multi-sensor data fusion algorithm to fuse the multimodal data into a unified data set; comprising the following steps: (1) Establish a basic information database; (2) Making RFID tags; (3) Multi-target recognition and localization framework; (4) Build a data management system; (5) Build a data analysis platform.

10. The control method according to claim 9, characterized in that: The multi-sensor fusion algorithm includes data-level fusion, feature-level fusion and decision-level fusion. When realizing multi-sensor information fusion, there are two commonly used fusion processing structures, namely, centralized fusion structure and distributed fusion structure.

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