Abstract: A system performs intelligent temperature control of equipment, for example, refrigeration equipment, heating equipment, air-conditioning equipment. The system receives signal generated by a sensor mounted in the equipment, for example, a thermistor. The signal monitors an attribute of the equipment, for example, temperature, pressure, or humidity. The system determines an optimal target attribute value for the equipment. The system modifies the signal received from the sensor to generate a modified signal for achieving the optimal target attribute value for the equipment. The system may use a device with variable resistors for generating the modified signal. The system sends the modified signal to a control module of the equipment. The control module controls the equipment to achieve the optimal target attribute value.

Abstract: A system performs intelligent temperature control of equipment, for example, refrigeration equipment, heating equipment, air-conditioning equipment. The system receives signal generated by a sensor mounted in the equipment, for example, a thermistor. The signal monitors an attribute of the equipment, for example, temperature, pressure, or humidity. The system determines an optimal target attribute value for the equipment. The system modifies the signal received from the sensor to generate a modified signal for achieving the optimal target attribute value for the equipment. The system may use a device with variable resistors for generating the modified signal. The system sends the modified signal to a control module of the equipment. The control module controls the equipment to achieve the optimal target attribute value.

Abstract: A system generates alerts based on sensor data obtained from equipment, for example, refrigeration equipment. Examples of equipment instances include refrigeration equipment, heating equipment, air conditioning equipment, and so on. The system accesses a model, for example, a machine learning model trained to predict an alert threshold for generation of alerts. The system modifies the alert threshold value for an equipment instance based on the output of the machine learning model. The system uses the modified alert threshold value for generating alerts. The system may track the number of times the alert threshold was adjusted. If the number of times the alert threshold value is modified exceeds a predetermined fault threshold value, the system determines that the equipment is faulty.

Abstract: A system generates alerts based on sensor data obtained from equipment, for example, refrigeration equipment. Examples of equipment instances include refrigeration equipment, heating equipment, air conditioning equipment, and so on. The system accesses a model, for example, a machine learning model trained to predict an alert threshold for generation of alerts. The system modifies the alert threshold value for an equipment instance based on the output of the machine learning model. The system uses the modified alert threshold value for generating alerts. The system may track the number of times the alert threshold was adjusted. If the number of times the alert threshold value is modified exceeds a predetermined fault threshold value, the system determines that the equipment is faulty.