Intelligent decision-making system for regular work execution of hydropower station equipment
By combining equipment databases and intelligent decision-making modules, data is collected and processed in real time to generate dynamic periodic work decision tables, which solves the problems of over-maintenance and human error in the periodic work of hydropower station equipment, and realizes efficient resource utilization and status matching.
Patent Information
- Application Number
- CN202510979926.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-11-04
AI Technical Summary
The regular operation of existing hydropower station equipment relies on fixed cycles or simple conditions, resulting in over-maintenance and resource waste. Furthermore, the lack of a dynamic adjustment mechanism makes it prone to human error.
By employing an equipment database, intelligent decision-making module, and execution feedback module, the system collects and processes data in real time, eliminates invalid data, generates a dynamic periodic work decision table, makes decisions based on the actual status of the equipment, and automatically generates periodic work plans.
It achieves dynamic matching between the equipment's scheduled operation and actual status, reduces manual intervention, avoids repetitive execution, improves resource utilization efficiency, and reduces labor intensity.
Smart Images
Figure CN120893669A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of hydropower stations, and in particular to an intelligent decision-making system for periodic operation of hydropower station equipment. BACKGROUND
[0002] In the field of operation and maintenance of hydropower stations, periodic rotation and testing of equipment are key links to ensure power generation efficiency and safe and stable operation. Periodic rotation refers to the rotation of operating equipment and standby equipment; periodic testing refers to the dynamic or static start of operating equipment or standby equipment, protection transmission, to detect the health level of operating or standby equipment. Periodic rotation and periodic testing are collectively referred to as periodic work.
[0003] Current periodic work of hydropower station equipment generally follows relevant requirements and relies on fixed cycles or simple condition triggers, which has problems of excessive maintenance and resource waste. For example, some equipment has automatically rotated and operated normally within the cycle, and the operating data has reached the required frequency of the regulation, so there is no need for periodic testing and rotation. Actual execution is redundant, wasting manpower and resources. In addition, the current periodic work needs to be manually queried, which consumes time and leads to human errors that result in failure to perform periodic work as required. Moreover, periodic work is disconnected from the actual state of the equipment, and lacks a dynamic adjustment mechanism, which requires manual exclusion of equipment under repair. SUMMARY
[0004] To solve the above problems, the present application provides an intelligent decision-making system for periodic work of hydropower station equipment, and the specific technical solutions are as follows: The system comprises an equipment database, an intelligent decision-making module and an execution feedback module. The equipment database is connected with the intelligent decision-making module, the equipment database collects equipment data through a data interface and stores the data, the execution feedback module obtains a decision table through the intelligent decision-making module and performs unexecuted feedback, the intelligent decision-making module is connected with the execution feedback module, the intelligent decision-making module generates a periodic work decision table according to the data of the equipment database and the feedback data of the execution feedback module, and the execution feedback module is connected with the equipment database.
[0005] Further, the equipment database comprises a data collection module, a data processing module and a storage module. The data collection module is connected with the data processing module, and the data processing module is connected with the storage module. The data collection module collects data of the target equipment in real time, the data processing module analyzes and processes the collected data, removes invalid data, and transmits the data to the storage module for storage.
[0006] Further, the device database collects time sequence data of the target device through a data interface, and detects real-time states of the target device.
[0007] Further, the data processing module eliminates invalid data, including abnormal operation data and fault shutdown data of the target device.
[0008] Further, the elimination of abnormal data of the target device is as follows: The start-stop data of the target device is obtained from the real-time data collected by the data collection module; According to the start-stop data, it is determined whether the time difference between adjacent starts and stops is lower than a first time difference threshold, the number of times lower than the set threshold is recorded, and it is determined whether the recorded number of times exceeds a set number of times threshold; If the set number of times threshold is exceeded, the corresponding start-stop data is determined to be invalid and is eliminated.
[0009] Further, the elimination of fault shutdown data is as follows: The start-stop data of the target device is obtained from the real-time data collected by the data collection module; According to the start-stop data, the interval time of the corresponding single start-stop action is obtained; It is determined whether the interval time is lower than a second time difference threshold, and if it is lower than the second time difference threshold, the corresponding start-stop action is determined to be a fault shutdown, and the data is eliminated.
[0010] Further, the intelligent decision module includes a periodic work list module, a decision module, and a periodic work decision table module; The periodic work list module stores periodic work list data, including target devices, periodic work content, and periodic work time; The decision module stores pre-set decision logic; The periodic work decision table module generates a daily periodic work decision table based on the periodic work list and unexecuted periodic work device information fed back by the execution feedback module.
[0011] Further, the generation of the daily periodic work decision table is as follows: The current periodic work list data and unexecuted periodic work device information are obtained to generate a daily periodic work list; The decision logic is executed for dynamic analysis and screening to generate a daily periodic work decision table.
[0012] Further, the decision logic execution process is as follows: S1: Obtain periodic work related device information based on the daily periodic work list; S2: read the device information in turn, and make logical judgment, generate the periodic work decision table according to the judgment output results of all devices, and the logical judgment is as follows: read the running times of the current device in the cycle, if greater than the set number of times, read the running time of the current device in the cycle, otherwise enter the current device state detection; if the running time of the previous device in the cycle is greater than the set time value, output that the current device periodic work does not need to be executed, otherwise enter the current device state detection; if the current device is detected as a normal state, output that the current device periodic work needs to be executed, if the current device is detected as a maintenance state, output that the current device periodic work does not need to be executed.
[0013] Further, the execution feedback module includes a periodic work decision table query module, a periodic work execution record module and a non-executed periodic work device information generation module; The periodic work decision table query module is used for the operation personnel to query data based on the received periodic work decision table through query conditions; The periodic work execution record module is used for the operation personnel to record the execution of each periodic work, and feedback to the device database; The non-executed periodic work device information generation module is used to record the reason information of the non-executed periodic work, and feedback to the intelligent decision module.
[0014] The beneficial effects of the present application are as follows: The present application constructs a device database by real-time collection of device operation big data combined with real-time state detection, avoids the situation that periodic work is separated from actual operation data of the device by establishing a dynamic adjustment mechanism; in the dynamic adjustment process, the decision rules of "frequency standard exemption periodic work" are established by the decision logic based on time and number, the periodic work decision table is automatically generated, the actual state of the device is considered, manual query in the production system is omitted, labor intensity is reduced, and human negligence leading to omission of periodic work is avoided. BRIEF DESCRIPTION OF DRAWINGS
[0015] Figure 1 is a system architecture schematic diagram of the present application.
[0016] Figure 2 is a decision logic flow schematic diagram of the present application. DETAILED DESCRIPTION
[0017] In the following description of the technical solutions in the embodiments of the present application, it is obvious that the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.
[0018] In the description of the embodiments of the present application, it should be noted that the indicated position or positional relationship is based on the position or positional relationship shown in the drawings, or the position or positional relationship commonly placed when the product of the present application is used, or the position or positional relationship commonly understood by those skilled in the art, or the position or positional relationship commonly placed when the product of the present application is used, which is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the indicated device or element must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" are only used for differentiation and cannot be understood as indicating or implying relative importance.
[0019] In the description of the embodiments of the present application, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be directly connected, or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.
[0020] Embodiment 1 Embodiment 1 of the present application discloses an intelligent decision-making system for periodic work execution of hydropower station equipment, as shown in Figure 1 The specific implementation is as follows: The system comprises an equipment database, an intelligent decision-making module and an execution feedback module; The equipment database is in data connection with the intelligent decision-making module, the equipment database collects equipment data through a data interface and stores records, the intelligent decision-making module obtains multi-source data from the equipment database, the execution feedback module obtains a decision table through the intelligent decision-making module and performs non-execution feedback, the intelligent decision-making module is connected with the execution feedback module, the intelligent decision-making module generates a periodic work decision table according to the data of the equipment database and the feedback data of the execution feedback module; the execution feedback module is in feedback connection with the equipment database, and the execution feedback module feeds back execution records to the equipment database.
[0021] As a preferred embodiment, the equipment database comprises a data collection module, a data processing module and a storage module; The data collection module is connected with the data processing module, and the data processing module is connected with the storage module; The data collection module collects data of the target device in real time, the data processing module analyzes and processes the collected data, removes invalid data, and transmits the data to the storage module for storage.
[0022] As a preferred embodiment, the equipment database collects time sequence data of the target device through a data interface, and detects the real-time state of the target device.
[0023] Specifically, the time sequence data includes cumulative running times and continuous time lengths of the target device; and the equipment database can be linked with a big data platform and other production data systems through a data interface to exchange information data.
[0024] Specifically, the real-time state detection of the equipment is based on comprehensive analysis and judgment of multi-source data, including physical signals of maintenance handles, switches and other normally open / closed contacts, and permission issuance of related work orders in the system, which is more reliable than direct judgment based on a single signal, ensuring that the stored data is real and reliable, and the data called by the intelligent decision module is consistent with the actual operation of the equipment.
[0025] As a preferred embodiment, the data processing module removes invalid data, including abnormal operation data and fault shutdown data of the target device. The removal of abnormal data of the target device is as follows: The start-stop data of the target device is obtained from the real-time data collected by the data collection module. According to the start-stop data, it is determined whether the time difference between adjacent starts and stops is less than a first time difference threshold, the number of times that the time difference is less than the first time difference threshold is recorded, and it is determined whether the recorded number of times exceeds a number threshold. If the number threshold is exceeded, the corresponding start-stop data is determined to be invalid and is removed.
[0026] The removal of fault shutdown data is as follows: The start-stop data of the target device is obtained from the real-time data collected by the data collection module. According to the start-stop data, the interval time of a single start-stop action is obtained. It is determined whether the interval time is less than a second time difference threshold, and if the interval time is less than the second time difference threshold, the corresponding start-stop action is determined to be a fault shutdown, and the data is removed.
[0027] As a preferred embodiment, the intelligent decision module includes a periodic work list module, a decision module and a periodic work decision table module. The periodic work list module stores periodic work list data, including a target device, periodic work content and periodic work time. The decision module stores a pre-set decision logic. The periodic work decision table module calls decision logic to generate a daily periodic work decision table based on the periodic work list and unexecuted periodic work equipment information fed back by the execution feedback module.
[0028] The daily periodic work decision table is generated in particular as follows: Current periodic work list data and unexecuted periodic work equipment information are acquired to generate a daily periodic work list. In particular, the current periodic work list is a periodic work list formed by periodic work content, time and equipment entered in advance, which is used for the intelligent decision module to query whether there is periodic work on the day and to determine the equipment involved in the daily periodic work; the periodic work list is allowed to be modified, and after modification, it supports re-generating the daily periodic work decision table according to the new list.
[0029] The decision logic is executed for dynamic analysis and screening to generate the daily periodic work decision table.
[0030] As a preferred embodiment, as shown in Figure 2 The decision logic execution process is in particular as follows: S1: Acquire periodic work related equipment information based on the daily periodic work list; S2: Read the equipment information in turn and perform logical judgment, and generate a periodic work decision table according to the judgment output results of all equipment, the logical judgment being as follows: Read the number of times of running in the period of the current equipment, if greater than a set number of times, read the running time in the period of the current equipment, otherwise, enter the current equipment state detection; If the running time in the period of the current equipment is greater than a set time length, output that the periodic work of the current equipment does not need to be executed, otherwise, enter the current equipment state detection; If the current equipment is detected to be in a normal state, output that the periodic work of the current equipment needs to be executed, if the current equipment is detected to be in a maintenance state, output that the periodic work of the current equipment does not need to be executed.
[0031] The daily periodic work decision table is shown in Table 1 as follows: Table 1: Daily periodic work decision table
[0032] As a preferred embodiment, the execution feedback module includes a periodic work decision table query module, a periodic work execution record module and an unexecuted periodic work equipment information generation module. The periodic work decision table query module is used for an operator to perform data query based on the received periodic work decision table through query conditions. The periodic work execution record module is used for recording execution of each periodic work by the staff and feeding back to the equipment database. The unexecuted periodic work equipment information generation module is used for recording reason information of unexecuted periodic work and feeding back to the intelligent decision module.
[0033] The present application is not limited to the foregoing specific embodiments. The present application extends to any novel one, or any novel combination, of the features disclosed in this specification, and to any novel method or process disclosed, or any novel combination of steps of any of the methods or processes disclosed.
Claims
1. A smart decision-making system for the periodic operation of hydropower station equipment, characterized in that, This includes a device database, an intelligent decision-making module, and an execution feedback module; The device database is connected to the intelligent decision-making module. The device database collects device data and stores records through a data interface. The execution feedback module obtains the decision table through the intelligent decision-making module and provides feedback on unexecuted actions. The intelligent decision-making module is connected to the execution feedback module. The intelligent decision-making module generates a periodic work decision table based on the data from the device database and the feedback data from the execution feedback module. The execution feedback module is connected to the device database for feedback.
2. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 1, characterized in that, The device database includes a data acquisition module, a data processing module, and a storage module; The data acquisition module is connected to the data processing module, and the data processing module is connected to the storage module; The data acquisition module collects data from the target device in real time, and the data processing module analyzes and processes the collected data, removes invalid data, and transmits it to the storage module for storage.
3. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 2, characterized in that, The device database collects time-series data of the target device through a data interface to detect and obtain the real-time status of the device.
4. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 2, characterized in that, The data processing module removes invalid data, including data on abnormal operation of the target equipment and data on equipment failure and shutdown.
5. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 4, characterized in that, The removal of abnormal data from the target device is detailed below: The start / stop data of the target device is obtained from the real-time data collected by the data acquisition module; Based on the start-stop data, calculate whether the time difference between adjacent start-stops is lower than a set first time difference threshold, record the number of times it is lower than the set threshold, and determine whether the number of recorded times exceeds the set number threshold. If the number of times exceeds the set threshold, the corresponding start / stop data is deemed invalid and discarded.
6. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 4, characterized in that, The data on system outages and failures will be removed as follows: The start / stop data of the target device is obtained from the real-time data collected by the data acquisition module; Based on the start-stop data, obtain the interval time of the corresponding single start-stop action; If the interval is lower than the set second time difference threshold, the corresponding start / stop action is determined to be a fault shutdown and the data is removed.
7. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 1, characterized in that, The intelligent decision-making module includes a periodic work list module, a decision module, and a periodic work decision table module; The recurring work list module stores recurring work list data, including target devices, recurring work content, and recurring work time; The decision module stores pre-set decision logic; The scheduled work decision table module generates the scheduled work decision table for the day based on the scheduled work list and the equipment information of unexecuted scheduled work fed back by the execution feedback module, and calls the decision logic.
8. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 7, characterized in that, The process of generating the daily scheduled work decision table is as follows: Retrieve the current list of scheduled tasks and information on equipment that has not performed scheduled tasks, and generate the list of scheduled tasks for the day. The execution decision-making logic is dynamically analyzed and filtered to generate a daily routine work decision table.
9. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 8, characterized in that, The specific execution process of the decision-making logic is as follows: S1: Obtain equipment information related to the scheduled work based on the daily scheduled work list; S2: Read device information sequentially and perform logical judgments. Based on the judgment results of all devices, generate a periodic work decision table. The logical judgments are as follows: Read the number of times the device runs within the current cycle. If the number of times exceeds the set value, read the running time of the device within the current cycle; otherwise, enter the current device status detection. If the runtime of the previous device cycle is greater than the set duration value, then output that the current device's periodic work does not need to be executed; otherwise, enter the current device status detection. If the current device is detected to be in normal condition, output that the current device's periodic work needs to be executed; if the current device is detected to be in maintenance condition, output that the current device's periodic work does not need to be executed.
10. The intelligent decision-making system for the periodic operation of hydropower station equipment according to claim 1, characterized in that, The execution feedback module includes a periodic work decision table query module, a periodic work execution record module, and an equipment information generation module for non-performed periodic work. The periodic work decision table query module is used by operators to query data based on the received periodic work decision table according to query conditions; The periodic work execution record module is used by operators to record the execution status of various periodic tasks and feed them back to the equipment database; The non-performed scheduled work equipment information generation module is used to record the reasons for the non-performed scheduled work and feed them back to the intelligent decision-making module.