An artificial intelligence recognition alarm system based on refrigerator control

Abstract

The present application relates to the field of refrigeration equipment, for solving the problem that the refrigeration equipment lacks effective monitoring means for faults in use, so that the refrigeration equipment works overload to ensure the refrigeration temperature when a fault occurs, exceeding the continuous operation time, further aggravating the fault or causing new faults to occur, specifically a kind of artificial intelligence identification alarm system based on refrigerator control, including temperature management module, component self-checking unit and pressure identification unit;When the present application controls the refrigeration equipment, the operating temperature of the refrigeration equipment is collected, and the collected operating temperature is comprehensively processed in many aspects, so that the temperature compliance, temperature variation trend and temperature variation speed are comprehensively evaluated, so that the synchronous static judgment and dynamic monitoring of temperature are carried out, the precision of temperature monitoring is improved, and the temperature control related fault behavior can be more accurately found.

Description

Technical Field

[0001] This invention relates to the field of refrigeration equipment, specifically to an artificial intelligence recognition alarm system based on freezer control. Background Technology

[0002] With the development of technology, more and more smart devices have entered people's lives. Users can manage multiple smart devices through terminals, obtain the current status of each smart device, as well as the latest information obtained by each smart device, and reasonably adjust the status of smart devices in the next stage based on the latest information obtained, forming rich and effective automated smart scenarios. As equipment that needs to operate continuously for a long time, the intelligent monitoring and alarm functions of refrigeration equipment are particularly important. The intelligent refrigeration control system can be widely used in cold storage equipment in various refrigeration, freezing and preservation industries. Traditional cold storage refrigeration systems have problems such as high energy consumption and inflexible adjustment. With the continuous development of technology, people's requirements for refrigeration systems are also constantly increasing. In order to improve the refrigeration effect and save energy and protect the environment, intelligent adjustment of refrigeration systems is becoming more and more important. Currently, in existing technologies, during the operation of refrigeration equipment, a target temperature is typically set, and the temperature is automatically adjusted when it deviates from the target temperature. However, in practical applications, when there is a malfunction in the refrigeration equipment, the actual temperature often cannot be stabilized within the set target temperature. This causes the refrigeration equipment to detect the temperature deviation and increase the operating power of the refrigeration components to bring the actual temperature closer to the target temperature. This results in the refrigeration components operating at high load for a long time, causing damage to the refrigeration components or exacerbating existing malfunctions in the refrigeration equipment, leading to greater property damage or even safety hazards.

[0003] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0004] This invention, when controlling refrigeration equipment, collects the operating temperature of the refrigeration equipment and performs comprehensive processing on the collected operating temperature to comprehensively evaluate the temperature compliance, temperature change trend, and temperature change rate. This allows for simultaneous static judgment and dynamic monitoring of temperature, improving the accuracy of temperature monitoring and enabling more accurate detection of temperature control-related fault behaviors. It addresses the problem of refrigeration equipment lacking effective fault monitoring methods during use, which leads to overloading and exceeding continuous operating time to maintain refrigeration temperature when faults occur, further aggravating the fault or causing new faults. Therefore, this invention proposes an artificial intelligence-based identification and alarm system for freezer control.

[0005] The objective of this invention can be achieved through the following technical solutions: An artificial intelligence recognition alarm system based on freezer control includes a temperature management module, a component self-test unit, and a pressure recognition unit. The pressure recognition unit is used to collect the pressure in the refrigeration component, obtain the operating pressure parameters based on the collection results, judge the operating pressure parameters, and generate a normal pressure signal or an abnormal pressure signal. The component self-test unit is used to perform operational self-tests on multiple components in the refrigeration component and obtain a component health self-test report based on the self-test results; The temperature management module is used to collect the refrigeration temperature in the freezer, analyze the collected refrigeration temperature in real time, judge the refrigeration temperature based on the real-time analysis results, and generate a temperature too high signal or a temperature too low signal. Meanwhile, the temperature management module analyzes the real-time changes in the cooling temperature, obtains the temperature change trend, and analyzes the temperature dispersion trend based on the temperature change trend, generating a temperature stability signal or a temperature deviation signal through the temperature dispersion trend. The temperature management module also dynamically analyzes the changing trend of the cooling temperature to obtain a temperature stability signal or a temperature fluctuation signal.

[0006] In a preferred embodiment of the present invention, the temperature management module includes a temperature acquisition unit, a temperature discrete statistics unit, and a trend analysis unit. The temperature acquisition unit is used to acquire the real-time temperature inside the freezer and compare the acquired real-time temperature with the set temperature range. Based on the comparison result, it generates a temperature too high signal, a temperature too low signal, or a normal temperature signal.

[0007] In a preferred embodiment of the present invention, after generating a normal temperature signal, the temperature acquisition unit records the real-time temperature and the temperature acquisition time. The temperature change data is obtained by combining the temperature acquisition time and the acquired real-time temperature. The trend analysis unit analyzes the temperature change data, calculates the difference between the real-time temperatures of two adjacent time points, and calculates the ratio of the difference to the time interval between the two time points, thereby obtaining the rate of temperature change. The trend analysis unit selects the latest real-time temperature and compares it with the previous set of real-time temperatures on the timeline. If the latest real-time temperature is greater than the previous set of real-time temperatures, it is recorded as an upward trend in temperature. If the latest real-time temperature is less than the previous set of real-time temperatures, it is recorded as a downward trend in temperature. The temperature change analysis unit compares the rate of temperature change with a set speed threshold. If the rate of temperature change is greater than the set speed threshold, a temperature fluctuation signal is generated. If the rate of temperature change is not greater than the set speed threshold, a temperature stability signal is generated.

[0008] In a preferred embodiment of the present invention, after the temperature acquisition unit generates an excessively high temperature signal or an excessively low temperature signal, the temperature discrete statistics unit obtains the temperature change rate and real-time temperature through the change trend analysis unit. The temperature discrete statistics unit calculates the difference between the real-time temperature and the midpoint of the set temperature range, and records it as the temperature deviation value. When the real-time temperature is less than the midpoint of the temperature range, it is recorded as low temperature deviation, and when the real-time temperature is greater than the midpoint of the temperature range, it is recorded as high temperature deviation. The temperature discrete unit obtains the current temperature change trend, compares the temperature change trend with the temperature deviation direction, and obtains the temperature discrete trend.

[0009] In a preferred embodiment of the present invention, the method by which the temperature discrete statistical unit compares the temperature change trend and the temperature deviation direction is as follows: If the temperature deviation direction is low temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete expansion; If the temperature deviation direction is low temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete expansion.

[0010] In a preferred embodiment of the present invention, after acquiring the pressure parameters, the pressure identification unit compares the pressure parameters with the set pressure parameter range, and generates a normal pressure signal or an abnormal pressure signal based on the comparison result.

[0011] In a preferred embodiment of the present invention, after acquiring the abnormal pressure signal, the excessively high temperature signal, the excessively low temperature signal, the temperature deviation signal, and the temperature fluctuation signal, the component self-test unit performs a self-test of the refrigeration component and obtains a component health self-test report based on the self-test results of the refrigeration component.

[0012] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, when controlling the refrigeration equipment, the operating temperature of the refrigeration equipment is collected and the collected operating temperature is comprehensively processed from multiple aspects, thereby comprehensively evaluating the temperature compliance status, temperature change trend, and temperature change rate. This allows for simultaneous static judgment and dynamic monitoring of the temperature, improving the accuracy of temperature monitoring and enabling more accurate detection of temperature control-related fault behaviors.

[0013] 2. In this invention, the internal pressure of the refrigeration component is monitored, and the operational safety level of the refrigeration component is judged based on the pressure condition. At the same time, the operation of the component self-test program of the refrigeration equipment is controlled based on the pressure judgment result and the temperature judgment result. This ensures the normal operation of the refrigeration equipment while reducing the frequency of the self-test program, and avoids the situation of missed fault detection. Attached Figure Description

[0014] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0015] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please see Figure 1 As shown, an artificial intelligence recognition alarm system based on freezer control includes a temperature management module, a component self-test unit, and a pressure recognition unit. The pressure recognition unit is used to collect the pressure in the refrigeration component and obtain the operating pressure parameters based on the collection results. After obtaining the pressure parameters, the pressure recognition unit compares the pressure parameters with the set pressure parameter range. If the pressure parameters are within the set pressure parameter range, a normal pressure signal is generated; if the pressure parameters are outside the set pressure parameter range, an abnormal pressure signal is generated. The temperature management module includes a temperature acquisition unit, a temperature discrete statistics unit, and a trend analysis unit. The temperature acquisition unit is used to acquire the real-time temperature inside the freezer and compare the acquired real-time temperature with the set temperature range. If the real-time temperature is greater than the maximum value in the set temperature range, an overheat signal is generated; if the real-time temperature is less than the minimum value in the set temperature range, an underheat signal is generated; if the real-time temperature is within the set temperature range, a normal temperature signal is generated. After generating a normal temperature signal, the temperature acquisition unit records the real-time temperature and the time of temperature acquisition. The temperature change data is obtained by combining the temperature acquisition time and the acquired real-time temperature. The trend analysis unit selects the latest real-time temperature and compares it with the previous set of real-time temperatures on the timeline. If the latest real-time temperature is greater than the previous set of real-time temperatures, it is recorded as an upward trend in temperature. If the latest real-time temperature is less than the previous set of real-time temperatures, it is recorded as a downward trend in temperature. The upward and downward trends in temperature are recorded as the temperature change trend. After the temperature acquisition unit generates an over-temperature signal or an under-temperature signal, the temperature discrete statistics unit obtains the temperature change rate and real-time temperature through the change trend analysis unit. The temperature discrete statistics unit calculates the difference between the real-time temperature and the midpoint of the set temperature range, and records it as the temperature deviation value. When the real-time temperature is less than the midpoint of the temperature range, it is recorded as low temperature deviation, and when the real-time temperature is greater than the midpoint of the temperature range, it is recorded as high temperature deviation. The temperature discrete unit obtains the current temperature change trend, compares the temperature change trend with the temperature deviation direction, and obtains the temperature discrete trend. The temperature discrete trend includes discrete expansion and discrete contraction. When generating the discrete expansion trend, the temperature discrete statistics unit generates a temperature deviation signal and sends the temperature deviation signal to the component self-test unit. The method by which the temperature discrete statistical unit compares the temperature change trend and the direction of temperature deviation is as follows: If the temperature deviation direction is low temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete expansion; If the temperature deviation direction is low temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete expansion.

[0018] The trend analysis unit analyzes the temperature change data, calculates the difference between the real-time temperatures of two adjacent time points, and calculates the ratio of this difference to the time interval between the two time points, thereby obtaining the rate of temperature change. The temperature change analysis unit compares the rate of temperature change with a set speed threshold. If the rate of temperature change is greater than the set speed threshold, a temperature fluctuation signal is generated; if the rate of temperature change is not greater than the set speed threshold, a temperature stability signal is generated.

[0019] Example 2: Please see Figures 1-2 As shown, the component self-test unit is used to perform operational self-tests on multiple components in the refrigeration system and obtain a component health self-test report based on the self-test results. After acquiring abnormal pressure signals, excessively high temperature signals, excessively low temperature signals, temperature deviation signals, and temperature fluctuation signals, the component self-test unit performs a self-test of the refrigeration components. By running the set self-test program, it performs self-tests on each component, obtains a self-test log, analyzes the self-test log using a set algorithm, extracts the fault codes, and records them as the self-test results of the refrigeration components. Based on the self-test results of the refrigeration components, it obtains a component health self-test report, and uses the component health self-test report to perform a comprehensive analysis of the refrigeration equipment.

[0020] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An artificial intelligence-based identification alarm system for freezer control, characterized in that, It includes a temperature management module, a component self-test unit, and a pressure identification unit. The pressure identification unit is used to collect the pressure in the refrigeration component, obtain the operating pressure parameters based on the collection results, judge the operating pressure parameters, and generate a normal pressure signal or an abnormal pressure signal. The component self-test unit is used to perform operational self-tests on multiple components in the refrigeration component and obtain a component health self-test report based on the self-test results; The temperature management module is used to collect the refrigeration temperature in the freezer, analyze the collected refrigeration temperature in real time, judge the refrigeration temperature based on the real-time analysis results, and generate a temperature too high signal or a temperature too low signal. Meanwhile, the temperature management module analyzes the real-time changes in the cooling temperature, obtains the temperature change trend, and analyzes the temperature dispersion trend based on the temperature change trend, generating a temperature stability signal or a temperature deviation signal through the temperature dispersion trend. The temperature management module also dynamically analyzes the changing trend of the cooling temperature to obtain a temperature stability signal or a temperature fluctuation signal.

2. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, The temperature management module includes a temperature acquisition unit, a temperature discrete statistics unit, and a trend analysis unit. The temperature acquisition unit is used to acquire the real-time temperature inside the freezer and compare the acquired real-time temperature with the set temperature range. Based on the comparison result, it generates a signal indicating that the temperature is too high, too low, or normal.

3. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, After generating a normal temperature signal, the temperature acquisition unit records the real-time temperature and the time of temperature acquisition. The temperature acquisition time and the acquired real-time temperature are combined to obtain temperature change data. The trend analysis unit analyzes the temperature change data, calculates the difference between the real-time temperatures of two adjacent time points, and calculates the ratio of the difference to the time interval between the two time points, thereby obtaining the rate of temperature change. The trend analysis unit selects the latest real-time temperature and compares it with the previous set of real-time temperatures on the timeline. If the latest real-time temperature is greater than the previous set of real-time temperatures, it is recorded as an upward trend in temperature. If the latest real-time temperature is less than the previous set of real-time temperatures, it is recorded as a downward trend in temperature. The temperature change analysis unit compares the rate of temperature change with a set speed threshold. If the rate of temperature change is greater than the set speed threshold, a temperature fluctuation signal is generated. If the rate of temperature change is not greater than the set speed threshold, a temperature stability signal is generated.

4. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, After the temperature acquisition unit generates an excessively high temperature signal or an excessively low temperature signal, the temperature discrete statistics unit obtains the temperature change rate and real-time temperature through the change trend analysis unit. The temperature discrete statistics unit calculates the difference between the real-time temperature and the midpoint of the set temperature range, and records it as the temperature deviation value. When the real-time temperature is less than the midpoint of the temperature range, it is recorded as low temperature deviation, and when the real-time temperature is greater than the midpoint of the temperature range, it is recorded as high temperature deviation. The temperature discrete unit obtains the current temperature change trend, compares the temperature change trend with the temperature deviation direction, and obtains the temperature discrete trend.

5. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, The method by which the temperature discrete statistical unit compares the temperature change trend and the temperature deviation direction is as follows: If the temperature deviation direction is low temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete expansion; If the temperature deviation direction is low temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is a decreasing temperature trend, it is recorded as discrete reduction; If the temperature deviation direction is high temperature deviation and the temperature change trend is an upward temperature trend, it is recorded as discrete expansion.

6. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, After acquiring the pressure parameters, the pressure identification unit compares the pressure parameters with the set pressure parameter range, and generates a normal pressure signal or an abnormal pressure signal based on the comparison result.

7. The artificial intelligence recognition alarm system based on freezer control according to claim 1, characterized in that, After acquiring abnormal pressure signals, excessively high temperature signals, excessively low temperature signals, temperature deviation signals, and temperature fluctuation signals, the component self-test unit performs a self-test of the refrigeration component and obtains a component health self-test report based on the refrigeration component self-test results.

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