A real-time evaluation method and system for radar equipment status

Abstract

The present application discloses a method and system for real-time evaluation of radar equipment status, and the method includes the following steps: S1, based on the historical fault data of radar and the maintenance and support record manual, construct a real-time evaluation index system for radar equipment status; S2, based on the real-time evaluation index system for radar equipment status, collect historical fault status data information of each indicator; S3, based on the historical fault status data information, obtain the corresponding weight of each indicator; S4, collect historical operation data information of each indicator of radar equipment, and construct a radar equipment status evaluation model based on the corresponding weight of each indicator; S5, collect real-time operation data of radar equipment, and obtain real-time evaluation information of radar equipment status based on the radar equipment status model. The present application constructs a real-time evaluation model for radar equipment based on historical fault data and maintenance records, which can locate the fault location and fault cause in real time and accurately, and improve the reliability of radar equipment.

Description

Technical Field

[0001] The present application belongs to the field of radar equipment status assessment, and specifically relates to a real-time radar equipment status assessment method and system. Background Art

[0002] Radar technology originated during World War II. After decades of development, it has been widely used in various fields. Air intelligence radar is used to search, monitor and identify aerial targets; maritime warning radar is used to detect sea surface targets; airborne early warning radar is installed on early warning aircraft to detect flying targets at various altitudes in the air and guide its own aircraft to intercept enemy aircraft, attack enemy ships or ground targets; over-the-horizon radar uses short waves to jump between the ionosphere and the ground to detect targets below the horizon; ballistic missile early warning radar is used to detect intercontinental, medium-range and submarine-launched ballistic missiles, and determine their instantaneous position, speed, launch point, impact point and other ballistic parameters; civil radars include weather radars, channel radars, and port radars, which are important means of basic meteorological operations and meteorological services; air traffic control radars have been widely used in civil aviation traffic control, and port control radars are widely used in port ship traffic control.

[0003] With the continuous improvement of modern equipment, the complexity of equipment has deepened. The traditional maintenance model relies on intensive regular maintenance plans and passive maintenance practices with high-reliability design and high-redundancy spare parts, which has led to a large waste of maintenance resources. Moreover, the regular maintenance method makes it difficult to repair the equipment in a timely manner when a failure occurs, affecting the daily work of the radar. Summary of the invention

[0004] The present application proposes a real-time evaluation method and system for radar equipment status. A real-time evaluation model for radar equipment is constructed based on historical fault data and maintenance records. The fault location and cause can be accurately located in real time, thereby improving the reliability of radar equipment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] A method for real-time evaluation of radar equipment status comprises the following steps:

[0007] S1. Collect historical fault data and maintenance records of radar equipment to obtain real-time evaluation indicators of radar equipment status;

[0008] S2. Based on the radar equipment status real-time evaluation index, construct a radar equipment status real-time evaluation index system;

[0009] S3. Based on the historical fault data information of the radar equipment, obtaining the corresponding weight of the real-time evaluation index of the radar equipment;

[0010] S4, collecting historical operation data information of each indicator of the radar equipment, and building a real-time status evaluation model of the radar equipment based on the corresponding weights of the real-time evaluation indicators of the radar equipment;

[0011] S5. Collecting real-time operation data of radar equipment, and obtaining real-time evaluation information of radar equipment status based on the real-time status evaluation model of radar equipment.

[0012] Preferably, the radar equipment status real-time evaluation index system includes: first-level evaluation indicators and second-level evaluation indicators.

[0013] Preferably, the first-level evaluation indicators include: signal transmitter, receiving antenna, signal processor, signal transmission system and control system.

[0014] Preferably, the radar equipment real-time evaluation index weight calculation method includes:

[0015] The first weight of the real-time evaluation index of radar equipment is calculated using the analytic hierarchy process;

[0016] Counting the fault types corresponding to the historical fault data information;

[0017] The second weight of the radar equipment real-time evaluation index is obtained by calculating the ratio of the number of occurrences of each type of fault to the total number of all fault types;

[0018] The radar equipment real-time evaluation index weight is calculated based on the first weight and the second weight.

[0019] Preferably, the method for constructing the radar equipment status assessment model includes:

[0020] Classifying the historical operation data information of each indicator of the radar equipment into a training set and a test set;

[0021] According to the training set, a radar equipment status assessment model is constructed based on a BP neural network;

[0022] Based on the test set and the historical fault status data information, the radar equipment status assessment model is tested.

[0023] Preferably, the radar equipment status assessment model testing method includes:

[0024] Dividing the historical fault status data information into a weight set and a fault status test set according to a predetermined ratio;

[0025] Based on the fault state test set and the test set, the radar equipment state assessment model is tested.

[0026] Preferably, the radar equipment status real-time assessment information includes the radar equipment status real-time assessment level;

[0027] The real-time evaluation levels of the radar equipment status include: excellent, good, acceptable, inferior, and poor.

[0028] Preferably, the radar equipment status real-time evaluation grade classification method comprises: obtaining the radar equipment status score based on the radar equipment status evaluation model;

[0029] The radar equipment status scores include:

[0030] If the score is (95-100], the radar equipment status is excellent;

[0031] If the score is (80-95], the radar equipment status is good;

[0032] If the score is (65-80], the radar equipment status is OK;

[0033] If the score is (45-65], the radar equipment status is secondary;

[0034] If the score is [0-45], the radar equipment status is poor.

[0035] The present application also provides a radar equipment status real-time assessment system, comprising:

[0036] Data module, indicator module, weight calculation module, model building module and status assessment module;

[0037] The data module is connected to the indicator module, and the data module is used to collect historical fault data information and maintenance and support records of radar equipment;

[0038] The indicator module is also connected to the weight calculation module, and the indicator module is used to obtain a real-time evaluation indicator of the radar equipment status based on the historical fault data information and maintenance and support records of the radar equipment; and to construct a real-time evaluation indicator system for the radar equipment status based on the real-time evaluation indicator of the radar equipment status;

[0039] The weight calculation module is also connected to the data module, and the weight calculation module is used to obtain the corresponding weight of the real-time evaluation index of the radar equipment based on the historical fault data information of the radar equipment;

[0040] The model building module is also connected to the weight calculation module, and the model building module is used to build a real-time status assessment model of radar equipment based on the historical operation data information of each indicator of the radar equipment and the corresponding weights of the real-time assessment indicators of the radar equipment;

[0041] The state assessment module is connected to the model building module, and is used to obtain real-time assessment information of the radar equipment state based on the real-time operation data of the radar equipment and the real-time state assessment model of the radar equipment.

[0042] The beneficial effects of this application are:

[0043] The present application discloses a real-time evaluation method for radar equipment status. A real-time evaluation model for radar equipment is constructed based on historical fault data and maintenance records. The fault location and cause can be accurately located in real time, thereby improving the reliability of radar equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A schematic flow chart of a method for real-time evaluation of radar equipment status is provided for this application;

[0046] Figure 2 This is a schematic diagram of the structure of a real-time evaluation system for radar equipment status in this application. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0049] Embodiment 1

[0050] like Figure 1 As shown, it is a schematic diagram of a method for real-time evaluation of radar equipment status in this application, which includes the following steps:

[0051] S1. Collect historical fault data and maintenance records of radar equipment to obtain real-time evaluation indicators of radar equipment status;

[0052] By collecting historical fault data and maintenance records of radar equipment, a fault diagnosis database is constructed. The historical fault data and maintenance records are classified to obtain real-time evaluation indicators of radar equipment status.

[0053] Among them, the historical fault data of radar equipment includes fault data that occurred during operation, and the maintenance and support records include the proprietary maintenance manual provided by the radar manufacturer, as well as fault data and solutions that occur during use and are different from those recorded in the maintenance manual.

[0054] S2. Based on the radar equipment status real-time evaluation index, construct a radar equipment status real-time evaluation index system;

[0055] The real-time evaluation index system for radar equipment status includes: first-level evaluation indicators and second-level evaluation indicators; the first-level evaluation indicators include: signal transmitter, receiving antenna, signal processor, signal transmission system and attack system. Corresponding adjustments can be made based on different radar types.

[0056] The fault types obtained based on historical fault data and maintenance and support records are specific. According to the evaluation indicators corresponding to the specific fault types, they are classified to the next higher level and numbered.

[0057] This embodiment takes the signal transmission system as an example, and the signal transmission system indicators include transmission channel output power, transmission channel output power band flatness, transmission channel gain, transmission channel port standing wave ratio, and transmission channel clutter. The signal transmission system is numbered as 01, the transmission channel output power is numbered as 010, the transmission channel output power band flatness is 011, the transmission channel gain is numbered as 012, the transmission channel port standing wave ratio is numbered as 013, and the transmission channel clutter is numbered as 014.

[0058] S3. Based on the historical fault data information of the radar equipment, the corresponding weights of the real-time evaluation indicators of the radar equipment are obtained;

[0059] The weight calculation method of radar equipment real-time evaluation index includes:

[0060] The first weight of the real-time evaluation index of radar equipment is calculated using the analytic hierarchy process;

[0061] Statistics on the fault types corresponding to the historical fault data information;

[0062] The second weight of the radar equipment real-time evaluation index is obtained by calculating the ratio of the number of occurrences of each type of fault to the total number of all fault types;

[0063] The radar equipment real-time evaluation index weight is calculated based on the first weight and the second weight.

[0064] Because the radar equipment itself has a complex structure and many parts, there must be enough historical fault data to provide reasonable support for weight calculation and prediction model testing. Therefore, the collection of fault data is arduous and there is a lot of data to be processed. This embodiment adopts a method of correcting the first weight value by the second weight value, which can improve the problem of the difficulty in determining the weight of the analysis hierarchy method when there are too many processing indicators and a large amount of data.

[0065] S4. Collect historical operation data information of various indicators of radar equipment, and build a real-time status evaluation model of radar equipment based on the corresponding weights of real-time evaluation indicators of radar equipment;

[0066] The method for constructing a radar equipment status assessment model includes:

[0067] The historical operating data information of each indicator of radar equipment is classified into training set and test set; the ratio of training set to test set is 8:2.

[0068] Before classifying the historical operating data information of each indicator of radar equipment, pre-process the data to remove erroneous data and reduce the impact on model accuracy. Normalize the pre-processed data;

[0069] According to the training set, a radar equipment status assessment model is constructed based on the BP neural network; the steps include:

[0070] The training set data is grouped into m groups, each containing n data. The first n-1 values ​​are used as the input of the network, and the nth value is used as the output of the network.

[0071] The root mean square error is used as the objective function of training;

[0072] The predicted value is obtained by using the denormalization operation.

[0073] Based on the test set and historical fault status data information, the radar equipment status assessment model is tested.

[0074] The historical fault status data information is divided into a weight set and a fault status test set according to a predetermined ratio; wherein the ratio of the weight set to the fault status test set is 7:3.

[0075] Based on the fault state test set and test set, the radar equipment status assessment model is tested.

[0076] The fault state test set is mixed with the test set, and the mixed data is input into the radar equipment state assessment model to test it and calculate the accuracy. The state assessment model is adjusted using parameter-based transfer learning theory to obtain an accurate radar equipment state assessment model.

[0077] S5. Collect real-time operation data of radar equipment, and obtain real-time evaluation information of radar equipment status based on the real-time status evaluation model of radar equipment.

[0078] The radar equipment status real-time assessment information includes the radar equipment status real-time assessment level;

[0079] The real-time evaluation levels of radar equipment status include: excellent, good, acceptable, inferior, and poor;

[0080] The radar equipment status real-time evaluation level classification method includes: obtaining the radar equipment status score based on the radar equipment status evaluation model; the radar equipment status score includes:

[0081] If the score is (95-100], the radar equipment status is excellent;

[0082] If the score is (80-95], the radar equipment status is good;

[0083] If the score is (65-80], the radar equipment status is OK;

[0084] If the score is (45-65], the radar equipment status is secondary;

[0085] If the score is [0-45], the radar equipment status is poor.

[0086] In this embodiment, the signal transmitter weight is 0.4, the predicted score is 20, the receiving antenna weight is 0.2, the predicted score is 88, the signal processor weight is 0.1, the predicted score is 82, the signal transmission system weight is 0.1, the predicted score is 92, and the control system weight is 0.2, the predicted score is 95. Finally, the signal transmitter status score is 62 points, and the corresponding status is sub.

[0087] Radar equipment itself contains a lot of components. For important components, the common cause of failure is aging failure. For important components, their service life can also be used as one of the factors reflecting the status of radar equipment.

[0088] Embodiment 2

[0089] like Figure 2 As shown, the present application also provides a radar equipment status real-time assessment system, comprising:

[0090] Data module, indicator module, weight calculation module, model building module and status assessment module;

[0091] The data module is connected to the indicator module, and the data module is used to collect historical fault data information and maintenance and support records of radar equipment;

[0092] The data module workflow is:

[0093] By collecting historical fault data and maintenance records of radar equipment, a fault diagnosis database is constructed, and the historical fault data and maintenance records are classified.

[0094] Among them, the historical fault data of radar equipment includes fault data that occurred during operation, and the maintenance and support records include the proprietary maintenance manual provided by the radar manufacturer, as well as fault data and solutions that occur during use and are different from those recorded in the maintenance manual.

[0095] The indicator module is also connected to the weight calculation module. The indicator module is used to obtain the real-time evaluation indicator of the radar equipment status based on the historical fault data information and maintenance and support records of the radar equipment; and based on the real-time evaluation indicator of the radar equipment status, a real-time evaluation indicator system of the radar equipment status is constructed;

[0096] The workflow of the indicator building block includes:

[0097] Based on the historical fault data information and maintenance records of radar equipment, the real-time evaluation index of radar equipment status is obtained; and based on the real-time evaluation index of radar equipment status, a real-time evaluation index system of radar equipment status is constructed;

[0098] The real-time evaluation index system for radar equipment status includes: first-level evaluation indicators and second-level evaluation indicators; the first-level evaluation indicators include: signal transmitter, receiving antenna, signal processor, signal transmission system and attack system.

[0099] The fault types obtained based on historical fault data and maintenance and support records are specific. According to the evaluation indicators corresponding to the specific fault types, they are classified to the next higher level and numbered.

[0100] This embodiment takes the signal transmission system as an example, and the signal transmission system indicators include transmission channel output power, transmission channel output power band flatness, transmission channel gain, transmission channel port standing wave ratio, and transmission channel clutter. The signal transmission system is numbered as 01, the transmission channel output power is numbered as 010, the transmission channel output power band flatness is 011, the transmission channel gain is numbered as 012, the transmission channel port standing wave ratio is numbered as 013, and the transmission channel clutter is numbered as 014.

[0101] The weight calculation module is also connected to the data module, and the weight calculation module is used to obtain the corresponding weight of the radar equipment real-time evaluation index based on the historical fault data information of the radar equipment;

[0102] The workflow of the weight module includes:

[0103] The first weight of the real-time evaluation index of radar equipment is calculated using the analytic hierarchy process;

[0104] Statistics on the fault types corresponding to the historical fault data information;

[0105] The second weight of the radar equipment real-time evaluation index is obtained by calculating the ratio of the number of occurrences of each type of fault to the total number of all fault types;

[0106] The radar equipment real-time evaluation index weight is calculated based on the first weight and the second weight.

[0107] The model building module is also connected to the weight calculation module. The model building module is used to build a real-time status evaluation model for radar equipment based on historical operation data information of various indicators of radar equipment and corresponding weights of real-time evaluation indicators of radar equipment;

[0108] The model building module workflow includes:

[0109] The historical operating data information of each indicator of radar equipment is classified into training set and test set; the ratio of training set to test set is 8:2.

[0110] Before classifying the historical operating data information of each indicator of radar equipment, pre-process the data to remove erroneous data and reduce the impact on model accuracy. Normalize the pre-processed data;

[0111] According to the training set, a radar equipment status assessment model is constructed based on the BP neural network; the steps include:

[0112] The training set data is grouped into m groups, each containing n data. The first n-1 values ​​are used as the input of the network, and the nth value is used as the output of the network.

[0113] The root mean square error is used as the objective function of training;

[0114] The predicted value is obtained by using the denormalization operation.

[0115] Based on the test set and historical fault status data information, the radar equipment status assessment model is tested.

[0116] The historical fault status data information is divided into a weight set and a fault status test set according to a predetermined ratio; wherein the ratio of the weight set to the fault status test set is 7:3.

[0117] Based on the fault state test set and test set, the radar equipment status assessment model is tested.

[0118] The fault state test set is mixed with the test set, and the mixed data is input into the radar equipment state assessment model to test it and calculate the accuracy. The state assessment model is adjusted using parameter-based transfer learning theory to obtain an accurate radar equipment state assessment model.

[0119] The status assessment module is connected to the model building module. The status assessment module is used to obtain real-time assessment information of the radar equipment status based on the real-time operation data of the radar equipment and the real-time status assessment model of the radar equipment.

[0120] The workflow of the status assessment module includes:

[0121] Conduct real-time status assessment and rating of radar equipment, including: excellent, good, acceptable, inferior, and poor;

[0122] The radar equipment status real-time evaluation level classification method includes: obtaining the radar equipment status score based on the radar equipment status evaluation model; the radar equipment status score includes:

[0123] If the score is (95-100], the radar equipment status is excellent;

[0124] If the score is (80-95], the radar equipment status is good;

[0125] If the score is (65-80], the radar equipment status is OK;

[0126] If the score is (45-65], the radar equipment status is secondary;

[0127] If the score is [0-45], the radar equipment status is poor.

[0128] In this embodiment, the signal transmitter weight is 0.4, the predicted score is 20, the receiving antenna weight is 0.2, the predicted score is 88, the signal processor weight is 0.1, the predicted score is 82, the signal transmission system weight is 0.1, the predicted score is 92, and the control system weight is 0.2, the predicted score is 95. Finally, the signal transmitter status score is 62 points, and the corresponding status is sub.

[0129] The embodiments described above are only descriptions of the preferred methods of the present application, and are not intended to limit the scope of the present application. Without departing from the design spirit of the present application, various modifications and improvements made to the technical solutions of the present application by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present application.

Claims

1. A real-time evaluation method for radar equipment status, characterized in that: The following steps are involved: S1. Collect historical fault status data and maintenance records of radar equipment to obtain real-time evaluation indicators of radar equipment status; S2. Based on the radar equipment status real-time evaluation index, construct a radar equipment status real-time evaluation index system; S3. Based on the historical fault status data information of the radar equipment, obtain the corresponding weight of the real-time evaluation index of the radar equipment status; S4, collecting historical operation data information of each indicator of the radar equipment, and constructing a real-time status assessment model of the radar equipment based on the corresponding weights of the real-time status assessment indicators of the radar equipment; S5. Collecting real-time operation data of radar equipment, and obtaining real-time evaluation information of radar equipment status based on the real-time status evaluation model of radar equipment.

2. A radar equipment status real-time assessment method according to claim 1, characterized in that: The radar equipment status real-time evaluation index system includes: first-level evaluation index and second-level evaluation index.

3. A radar equipment status real-time assessment method according to claim 2, characterized in that: The first-level evaluation indicators include: signal transmitter, receiving antenna, signal processor, signal transmission system and control system.

4. The method for real-time evaluation of radar equipment status according to claim 1, characterized in that: The radar equipment status real-time evaluation index weight calculation method includes: The first weight of the real-time evaluation index of radar equipment status is calculated using the analytic hierarchy process; Counting the fault types corresponding to the historical fault status data information; The second weight of the radar equipment status real-time evaluation index is obtained by calculating the ratio of the number of occurrences of each type of fault to the total number of occurrences of all types of faults; The radar equipment status real-time evaluation index weight is calculated based on the first weight and the second weight.

5. The method for real-time evaluation of radar equipment status according to claim 1, characterized in that: The method for constructing a real-time status assessment model for radar equipment includes: Classifying the historical operation data information of each indicator of the radar equipment into a training set and a test set; According to the training set, a radar equipment real-time status assessment model is constructed based on a BP neural network; Based on the test set and the historical fault status data information, the radar equipment real-time status assessment model is tested.

6. A radar equipment status real-time assessment method according to claim 5, characterized in that: The radar equipment real-time status assessment model testing method comprises: Dividing the historical fault status data information into a weight set and a fault status test set according to a predetermined ratio; Based on the fault state test set and the test set, the radar equipment real-time state assessment model is tested.

7. A radar equipment status real-time assessment method according to claim 1, characterized in that: The radar equipment status real-time assessment information includes the radar equipment status real-time assessment level; The real-time evaluation levels of the radar equipment status include: excellent, good, acceptable, inferior, and poor.

8. A radar equipment status real-time assessment method according to claim 7, characterized in that: The radar equipment status real-time evaluation grade classification method comprises: obtaining a radar equipment status score based on the radar equipment real-time status evaluation model; The radar equipment status scores include: If the score is (95-100], the radar equipment status is excellent; If the score is (80-95], the radar equipment status is good; If the score is (65-80], the radar equipment status is OK; If the score is (45-65], the radar equipment status is secondary; If the score is [0-45], the radar equipment status is poor.

9. A radar equipment status real-time assessment system, characterized in that: include: Data module, indicator module, weight calculation module, model building module and status assessment module; The data module is connected to the indicator module, and the data module is used to collect historical fault status data information and maintenance and support records of radar equipment; The indicator module is also connected to the weight calculation module, and the indicator module is used to obtain a real-time evaluation indicator of the radar equipment status based on the historical fault status data information and maintenance and support records of the radar equipment; and to construct a real-time evaluation indicator system for the radar equipment status based on the real-time evaluation indicator of the radar equipment status; The weight calculation module is also connected to the data module, and the weight calculation module is used to obtain the corresponding weight of the real-time evaluation index of the radar equipment status based on the historical fault status data information of the radar equipment; The model building module is also connected to the weight calculation module, and the model building module is used to build a radar equipment real-time status assessment model based on the historical operation data information of each indicator of the radar equipment and the corresponding weights of the radar equipment status real-time assessment indicators; The state assessment module is connected to the model building module, and is used to obtain real-time assessment information of the radar equipment state based on the real-time operation data of the radar equipment and the real-time state assessment model of the radar equipment.

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