Data anomaly detection method based on deep reinforcement learning

The invention relates to the technical field of data detection, in particular to a data anomaly detection method based on deep reinforcement learning. Comprising the steps of obtaining data information of initial detection equipment and generating a data set; rechecking the detection area according to the standby detection equipment and generating a recheck set, setting the test equipment to detect the detection area to generate real-time data, checking and judging whether abnormal data information exists or not, and marking the abnormal data information as an abnormal set when the abnormal data information exists; the standby detection device switches the initial detection device and generates a direction replacement detection queue; the method comprises the following steps: extending a detection area, acquiring detection equipment, generating a secondary detection area, generating a restricted area when standby detection equipment is lacked, and determining to call the standby detection equipment for execut