A refrigeration and heating system

Abstract

The present invention provides a refrigeration and heating system. The multi-channel measuring point information adopted by the sensor module is sampled and sent to the control module by a data selector. The switch of the data selector is determined according to the preset, the abnormality and the change trend of the sensor information, and then transmitted to the control module after the determination. The cause-effect analysis module in the control module predicts the information trend and the consequences produced. The source tracing module tracks and analyzes and determines the source measuring point causing the abnormality. The fault warning module determines the reliability of the warning of the source measuring point of the abnormality and issues a warning. The fault code acquisition module acquires the code information recognized by the electric control module and converts it into fault information. The fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the fault information matches, it combines multiple measuring point information sequences to check whether there is any abnormal condition, locates the location and cause of the fault, and issues an alarm. The module can perform refined alarm and display, which is convenient for the safe and stable operation and maintenance of the unit / refrigeration system.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated cooling and heating equipment, and in particular relates to a cooling and heating system. Background Art

[0002] The main components of the integrated water-cooled box-type unit / single-stage and cascade refrigeration system usually include a compressor, a condenser, an expansion valve and an evaporator, etc., which can circulate the refrigerant, absorb and release heat, thereby realizing the process of cooling or heating. The existing technology sets sensors at the four key components to detect the working temperature, working parameters, etc., detect the power supply current parameters of the unit, and transmit them to the electronic control module of the unit / refrigeration system. The electronic control module realizes the regulation of cooling and heating, fault diagnosis and code indication. On the one hand, the acquisition and call of sensor information are not flexible enough, and are usually carried out in serial or parallel transmission according to the sampling cycle, and cannot be dynamically and quickly addressed and collected according to the importance of information. On the other hand, although fault diagnosis and code indication can be performed according to the operating parameters, fault warning and fine fault positioning cannot be performed, which is not conducive to the safe and stable operation and subsequent maintenance of the unit / refrigeration system. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a refrigeration and heating system, which effectively solves the problems existing in the prior art.

[0004] The technical scheme is that it includes a unit, a sensor module, and a control module. The sensor module is equipped with temperature, current, and pressure sensors at multiple measuring points. The information of the measuring points of the multiple temperature, current, and pressure sensors forms an array and is sent to the control module through a data selector.

[0005] The control module includes a cause-effect analysis module, a traceability module, a regulation module, a fault warning module, a fault code acquisition module, and a fault location alarm module;

[0006] The cause-effect analysis module receives the sensor measurement point information, uses the trend analysis method to make predictions, weights the prediction information and the measurement information, and sends them to the control module, which drives the power of the unit to perform cooling and heating control;

[0007] The traceability module determines the local abnormal situation based on the measurement point information, combines the information of multiple measurement points, tracks and analyzes, and determines the source measurement point that causes the abnormality;

[0008] The fault warning module performs reliability evaluation on the measurement information and prediction information, uses the evaluation value to correct the reliability of the abnormal source measurement point warning, and performs a time series stationary analysis on the abnormal source measurement point information to determine the reliability of the abnormal source measurement point warning. When the reliability reaches 95%, a warning is issued;

[0009] The fault code acquisition module acquires code information recognized by the electronic control module, and the code information is converted into fault information;

[0010] The fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning source measurement point, it further combines multiple measurement point information sequences to check whether there are any abnormal conditions, locate the location and cause of the fault, and issue an alarm.

[0011] Preferably, the fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning source measurement point, it further combines multiple measurement point information sequences to check whether there is any abnormality, locates the location and cause of the fault, and issues an alarm, specifically: Step 1, matching the representation type with the knowledge base using a matching method;

[0012] Step 2: Filter the successfully matched knowledge base data to obtain the reverse data set;

[0013] Step 3: Match the reverse data set with the source measurement point information and perform similarity analysis;

[0014] Step 4: When the data match, combine the information sequences of multiple measurement points to check whether there is any abnormality;

[0015] Step 5: locate the fault location and determine the cause of the fault;

[0016] Step 6: Perform refined alarm and display according to the location and cause of the fault.

[0017] Preferably, the reliability assessment of the measurement information and prediction information includes the assessment of the closeness between the prediction information and the measurement information of the next period, the consistency assessment between different measurement information, and the data volume and integrity assessment. The reliability assessment result is obtained by using a weighted fusion algorithm for each assessment result.

[0018] Preferably, the knowledge base is a knowledge base summarized from historical faults and causes, the results and causes confirmed during maintenance are stored in the knowledge base, and the degree of proximity between the refined diagnosis results and the results and causes confirmed during maintenance is compared. When the degree of proximity is high, the refined diagnosis results are stored in the knowledge base, and when the degree of proximity is low, the refined diagnosis is optimized and adjusted.

[0019] The beneficial effects of the present invention are as follows: 1. The measurement point information of the multi-channel temperature, current and pressure sensors used constitutes an array, which is sampled and sent to the control module by the data selector. The switch of the data selector is first determined by the priority encoder according to the position and type coding of each sensor of the preset sensor module, as well as the abnormality of the sensor information and the change trend of the abnormal value. After the determination, it is synchronously transmitted to the control module, which can dynamically and quickly address and collect according to the importance of the information, and the acquisition and calling of the sensor information are flexible;

[0020] 2. The sensor measurement point information is predicted by trend analysis, and then the prediction information and measurement information are weighted and fused. The result of causal analysis prediction is compared with the existing drive signal of the output drive signal unit, and the compensation is regulated. The local abnormal situation is judged according to the information of a single measurement point, and the information of multiple measurement points is combined for tracking and analysis. Specifically, the historical data of different periods of the single measurement point information can be analyzed, and the information of multiple measurement points around the single measurement point can be compared to determine the source measurement point causing the abnormality. The reliability of the measurement information and prediction information is evaluated, and the reliability of the warning of the abnormal source measurement point is corrected by the evaluation value, and the abnormal source measurement point information is taken in time. The stability analysis of the time sequence is carried out to determine the reliability of the abnormal source measurement point warning. When the reliability reaches 95%, a warning is issued, and the code information recognized by the electronic control module is obtained. According to the corresponding relationship stored in the control module, the code information is converted into fault information. Fault diagnosis is performed according to the representation type and knowledge base of the fault information. When the diagnosis result is consistent with the warning source measurement point, multiple measurement point information sequences are further combined to check whether there is any abnormal condition, locate the location and cause of the fault, and issue an alarm. Compared with the electronic control module that only displays the fault code, the location and cause of the fault can be finely alarmed and displayed, which is convenient for the safe and stable operation and maintenance of the unit / refrigeration system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is an overall block diagram of the present invention.

[0022] Figure 2 Schematic diagram of the sensor module array of the present invention.

[0023] Figure 3 It is a flow chart of the steps of the fault location alarm module of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions of various embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0025] The following is in conjunction with the instruction manual Figures 1 to 3 , the specific implementation modes of the present invention are further described in detail.

[0026] A refrigeration and heating system includes a unit, a sensor module, and a control module. The sensor module is configured at key components and the power supply of the unit (cold and hot integrated water-cooled box unit / single-stage and self-cascade refrigeration system), including temperature, pressure, flow, such as Figure 2As shown, the measurement point information of multiple temperature, current and pressure sensors constitutes an array, which is sampled by the data selector and sent to the control module. The switch of the data selector is first determined by the priority encoder according to the position and type code of each sensor of the preset sensor module and the abnormality of the sensor information;

[0027] After confirmation, it is transmitted to the control module (the control module is a chip with processing function, such as general-purpose single-chip microcomputer, DSP, etc.). The control module verifies according to the preset logic level and abnormal information, and outputs the logic level to the decoder. The decoder output level triggers the row switch K1-A1...K1-An and the column switch K1-1...Kn-n in the data selector respectively, which can dynamically and quickly address and collect according to the importance of information, and the acquisition and call of sensor information are flexible;

[0028] The control module includes a cause-effect analysis module, a traceability module, a regulation module, a fault warning module, a fault code acquisition module, and a fault location alarm module;

[0029] The causal analysis module receives the sensor measurement point information and uses the trend analysis method to make predictions. Specifically, a simple exponential smoothing method Yt+1 = αYt + (1-α)Yt-1 can be used. After that, the prediction information and the measurement information are weighted and fused to reduce the measurement error and then go to the control module. The control module performs causal analysis (you can enter the knowledge base, input matching, and obtain the results), predict the results, and output the drive signal. The unit's existing drive signal is compared and compensated, such as the drive power, the opening of the expansion valve, etc., for cooling / heating control. It can make timely compensation and control according to the predicted information and the results, and the control is more accurate and constant temperature;

[0030] The traceability module determines the local abnormal situation based on the information of a single measuring point, and combines the information of multiple measuring points to track and analyze. Specifically, it can analyze the historical data of different periods of the information of a single measuring point, take the single measuring point information as the center, compare the information of multiple measuring points around it, and determine the source measuring point that caused the abnormality;

[0031] The fault warning module performs reliability evaluation on the measurement information and prediction information, uses the evaluation value to correct the reliability of the abnormal source measurement point warning, and performs a time series stationary analysis on the abnormal source measurement point information to determine the reliability of the abnormal source measurement point warning. When the reliability reaches 95%, a warning is issued;

[0032] The fault code acquisition module acquires the code information recognized by the electronic control module, and converts the code information into fault information according to the corresponding relationship stored in the control module;

[0033] The fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning source measurement point, it further combines multiple measurement point information sequences to check whether there are any abnormal conditions, locates the location and cause of the fault, and issues an alarm. Compared with the electronic control module that only displays the fault code, the location and cause of the fault can be finely alarmed and displayed, which is convenient for the safe and stable operation and maintenance of the unit / refrigeration system.

[0034] On the basis of the above technical solution, the fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning source measurement point, it further combines multiple measurement point information sequences to check whether there is any abnormal condition, locate the location and cause of the fault, and issue an alarm. Specifically, step 1, using a matching method to match the representation type with the knowledge base (the existing unit expert knowledge base or the self-summarized fault diagnosis instance knowledge base);

[0035] Step 2: Since one cause may correspond to multiple results and multiple causes may correspond to one result, the successfully matched knowledge base is further screened to obtain the reverse data set;

[0036] Step 3: Match the reverse data set with the source measurement point information and perform similarity analysis;

[0037] Step 4: When the data match, combine the information sequences of multiple measurement points to check whether there is any abnormality;

[0038] Step 5: When an abnormality occurs, locate the source measuring point as the location where the fault occurs, and determine the component corresponding to the source measuring point information as the cause of the fault;

[0039] Step 6, according to the location and cause of the fault, detailed alarm and display are performed, which can be on-site sound and light alarm, display, or remote alarm and monitoring via communication.

[0040] On the basis of the above technical solution, the reliability assessment of the measurement information and prediction information includes the assessment of the closeness between the prediction information and the measurement information of the next period, the consistency assessment between different measurement information, and the assessment of data volume and integrity. The results of each assessment are combined by a weighted fusion algorithm to obtain the result of the reliability assessment.

[0041] On the basis of the above technical solution, the knowledge base is a knowledge base summarized from historical faults and causes. The results and causes confirmed during maintenance are stored in the knowledge base, and the degree of proximity between the refined diagnosis results and the results and causes confirmed during maintenance is compared. When the degree of proximity is high, the refined diagnosis results are stored in the knowledge base. When the degree of proximity is low, the refined diagnosis is optimized and adjusted.

[0042] On the basis of the above technical scheme, the sensor module information is preprocessed with data extraction, outlier elimination and data compensation, the sampling order of abnormal value compensation is further compensated according to the changing trend of the abnormal value, so as to obtain information in time. The measuring point information enters the control module after synchronization processing at the acquisition time, such as a synchronous data acquisition card, so as to accurately obtain the detection parameter information at the same time and reduce the time lag error of the later quantification.

[0043] The working principle of the present invention is as follows: the multi-channel temperature, current and pressure sensor measuring point information used by the sensing module constitutes an array, which is sampled and sent to the control module through the data selector. The switch of the data selector is first determined by the priority encoder according to the position and type coding of each sensor of the preset sensing module and the abnormality of the sensing information, and then transmitted to the control module after determination. The control module verifies according to the preset logic level and abnormal information, and outputs the logic level to the decoder. The output level of the decoder triggers the data selector respectively, and can dynamically and quickly address and collect according to the importance of information. The acquisition and call of the sensing information are flexible. The control module also adopts the pre-processing of data extraction, wild value elimination and data compensation, the sampling order of abnormal value compensation, and further compensates the sampling order according to the changing trend of the abnormal value, so as to facilitate the timely acquisition of information. The measuring point information enters the control module after the acquisition time synchronization processing, so as to accurately obtain the detection parameter information at the same time;

[0044] The control module includes a cause-effect analysis module, a traceability module, a control module, a fault warning module, a fault code acquisition module, and a fault location alarm module;

[0045] The causal analysis module receives the sensor measurement point information and uses the trend analysis method to make predictions. Specifically, it can use the simple exponential smoothing method Yt+1 = αYt + (1-α)Yt-1. Then, the prediction information and the measurement information are weighted and fused to reduce the measurement error and go to the control module. The control module causal analysis predicts the results and outputs the existing drive signal of the drive signal unit for comparison and compensation. For example, the drive power, the opening of the expansion valve, etc. are used for cooling / heating control. Compensation and control can be carried out in time according to the predicted information and the results, and the control is more accurate and constant temperature.

[0046] The traceability module determines the local abnormal situation based on the information of a single measuring point, and combines the information of multiple measuring points for tracking and analysis. Specifically, it can analyze the historical data of different periods of the information of a single measuring point, take the single measuring point information as the center, compare the information of multiple measuring points around it, and determine the source measuring point that caused the abnormality;

[0047] The fault warning module evaluates the reliability of the measurement information and prediction information, and uses the evaluation value to correct the reliability of the abnormal source measurement point warning. It also conducts a time series stationary analysis on the abnormal source measurement point information to determine the reliability of the abnormal source measurement point warning. When the reliability reaches 95%, a warning is issued.

[0048] The fault code acquisition module acquires the code information recognized by the electronic control module, and converts the code information into fault information according to the corresponding relationship stored in the control module;

[0049] The fault location alarm module performs fault diagnosis according to the representation type of fault information and the knowledge base (the knowledge base is a knowledge base summarized by historical faults and causes, the results and causes confirmed during maintenance are stored in the knowledge base, and the degree of proximity between the refined diagnosis result and the results and causes confirmed during maintenance is compared. When the degree of proximity is high, the refined diagnosis result is stored in the knowledge base, and when the degree of proximity is low, the refined diagnosis is optimized and adjusted). When the diagnosis result is consistent with the early warning source measurement point, it is further combined with multiple measurement point information sequences to check whether there is any abnormal condition, locate the location and cause of the fault, and issue an alarm. Compared with the electronic control module which only displays the fault code, the location and cause of the fault can be finely alarmed and displayed, which is convenient for the safe and stable operation and maintenance of the unit / refrigeration system.

Claims

1. A refrigeration and heating system, comprising a unit, a sensor module, and a control module, characterized in that: The sensor module is equipped with temperature, current and pressure sensors at multiple measuring points. The information of the measuring points of the multiple temperature, current and pressure sensors forms an array and is sent to the control module through the data selector. The switch of the data selector is first determined by the priority encoder according to the position and type code of the preset sensor module and the abnormality of the sensor information. After the determination, it is transmitted to the control module. The control module verifies and outputs the logic level to the decoder. After decoding, the decoder drives the row switch and column switch of the data selector to realize dynamic and fast addressing and collection according to the importance of information. The control module includes a cause-effect analysis module, a traceability module, a regulation module, a fault warning module, a fault code acquisition module, and a fault location alarm module; The cause-effect analysis module receives the sensor measurement point information, uses the trend analysis method to make predictions, and then performs weighted fusion of the prediction information and the measurement information to reduce the measurement error before sending it to the control module, which drives the power of the unit to perform cooling and heating control; The traceability module determines the local abnormal situation according to the measurement point information, combines the information of multiple measurement points, tracks and analyzes, determines the measurement point source causing the abnormality, analyzes the historical data of different periods of the single measurement point information, combines the single measurement point information as the center, and compares it with the information of multiple surrounding measurement points to determine the measurement point source causing the abnormality; The fault warning module performs reliability evaluation on the measurement information and prediction information, uses the evaluation value to correct the reliability of the abnormal measurement point source warning, and performs a time series stationary analysis on the abnormal measurement point source information to determine the reliability of the abnormal measurement point source warning. When the reliability reaches 95%, a warning is issued; The fault code acquisition module acquires the code information recognized by the electronic control module, and converts the code information into fault information according to the corresponding relationship stored in the control module; The fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning measurement point source, it further combines multiple measurement point information sequences to check whether there are any abnormal conditions, locate the location and cause of the fault, and issue an alarm.

2. A refrigeration and heating system according to claim 1, characterized in that: The fault location alarm module performs fault diagnosis according to the representation type and knowledge base of the fault information. When the diagnosis result matches the warning measurement point source, it further combines multiple measurement point information sequences to check whether there is any abnormality, locate the location and cause of the fault, and issue an alarm. Specifically: Step 1, use matching method to match the representation type with the knowledge base; Step 2: Filter the successfully matched knowledge base data to obtain the reverse data set; Step 3: Match the reverse data set with the measurement point source information and perform similarity analysis; Step 4: When the data match, combine the information sequences of multiple measurement points to check whether there is any abnormality; Step 5: locate the fault location and determine the cause of the fault; Step 6: Perform refined alarm and display according to the location and cause of the fault.

3. A refrigeration and heating system according to claim 1, characterized in that: The reliability assessment of the measurement information and prediction information includes the assessment of the closeness between the prediction information and the measurement information of the next period, the consistency assessment between different measurement information, and the assessment of data volume and integrity. The reliability assessment result is obtained by combining the evaluation results with a weighted fusion algorithm.

4. A refrigeration and heating system according to claim 2, characterized in that: The knowledge base is a knowledge base summarized from historical faults and causes. The results and causes confirmed during maintenance are stored in the knowledge base, and the degree of proximity between the refined diagnosis results and the results and causes confirmed during maintenance is compared. When the degree of proximity is high, the refined diagnosis results are stored in the knowledge base. When the degree of proximity is low, the refined diagnosis is optimized and adjusted.

5. A refrigeration and heating system according to claim 1, characterized in that: The sensor module information is used, and preprocessing of data extraction, outlier elimination and data compensation is performed, the sampling order of abnormal value compensation is performed, and the sampling order is further compensated according to the changing trend of the abnormal value.

6. A refrigeration and heating system according to claim 1, characterized in that: The measuring point information enters the control module after being processed by acquisition time synchronization.

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