Information suppression method, device, computer equipment and readable storage medium

Abstract

The embodiment of the present application discloses an information suppression method, device, computer equipment and readable storage medium. In the embodiment of the present application, fault information is obtained; a logical judgment is performed on the fault information to obtain a first initial false alarm discrimination result of the fault information, and an algorithm judgment is performed on the fault information to obtain a second initial false alarm discrimination result of the fault information; a first target weight of a first parameter corresponding to the logical judgment is obtained, and a second target weight of a second parameter corresponding to the algorithm judgment is obtained; a target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight and the second target weight; if the target false alarm discrimination result is that the fault information is a false alarm, the fault information is suppressed. The embodiment of the present application can save storage resources and reduce the manpower cost and time cost of aircraft maintenance.

Description

Technical Field

[0001] The present invention relates to the field of aircraft, and in particular to an information suppression method, device, computer equipment and readable storage medium. Background Art

[0002] With the development of aviation technology, the complexity of aircraft is increasing, and the requirements for aircraft built-in test (BIT) are also getting higher and higher.

[0003] Power-on test is a type of in-flight test. During the power-on test, false alarms may occur. False alarms refer to the phenomenon that the device under test does not actually have a fault, but fault information is generated (i.e. the device under test is indicated to have a fault). When false alarms occur, storage resources are wasted and the manpower and time costs of aircraft maintenance are increased. Summary of the invention

[0004] The embodiments of the present application provide an information suppression method, apparatus, computer equipment, and readable storage medium, which can save storage resources and reduce the manpower and time costs of aircraft maintenance.

[0005] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0006] In one aspect, a method for suppressing information is provided, comprising:

[0007] Get fault information;

[0008] Performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information, and performing algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information;

[0009] Obtaining a first target weight of the first parameter corresponding to the above logic judgment, and obtaining a second target weight of the second parameter corresponding to the above algorithm judgment;

[0010] Determine a target false alarm discrimination result of the fault information based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight;

[0011] If the target false alarm determination result is that the fault information is a false alarm, the fault information is suppressed.

[0012] Optionally, performing logical judgment on the fault information to obtain a first initial false alarm determination result of the fault information includes:

[0013] Obtain the power-on time of the fault reporting system corresponding to the above fault information;

[0014] If the power-on time is less than or equal to a preset threshold, the first initial false alarm determination result is determined as the fault information being a false alarm.

[0015] Optionally, after obtaining the power-on time of the fault reporting system corresponding to the fault information, the method further includes:

[0016] If the power-on time is greater than the preset threshold, extracting a character string from the fault information to obtain a fault character string;

[0017] A first initial false alarm determination result of the fault information is determined based on the fault character string.

[0018] Optionally, the determining of the first initial false alarm determination result of the fault information based on the fault character string includes:

[0019] determining the state of the state matrix bits in the above fault string;

[0020] If the state of the state matrix bit is an abnormal state, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0021] Optionally, after determining the state of the state matrix bit in the fault character string, the method further includes:

[0022] If the state of the state matrix bit is normal, feature extraction is performed on the fault character string to obtain the fault type corresponding to the fault information;

[0023] If the fault type corresponding to the fault information is an internal fault, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0024] Optionally, the determining the fault type corresponding to the fault information according to the fault characteristic value includes:

[0025] If the fault type corresponding to the above fault information is an external fault, then find the associated system corresponding to the above fault information;

[0026] determining the electrical status of the above-mentioned associated systems;

[0027] If the electrical state of the associated system is a powered-on state, the first initial false alarm determination result determines that the fault information is not a false alarm;

[0028] If the electrical state of the associated system is a power-off state, the first initial false alarm determination result determines that the fault information is a false alarm.

[0029] Optionally, performing algorithmic judgment on the fault information to obtain a second initial false alarm judgment result of the fault information includes:

[0030] The above fault information is input into a trained neural network model for judgment to obtain a second initial false alarm judgment result of the above fault information.

[0031] Optionally, the above-mentioned inputting the above-mentioned fault information into a trained neural network model for judgment to obtain a second initial false alarm judgment result of the above-mentioned fault information includes:

[0032] Obtain external environment parameters corresponding to the above fault information;

[0033] The fault information and the external environment parameters are input into a trained neural network model for judgment, so as to obtain a second initial false alarm judgment result of the fault information.

[0034] Optionally, performing logical judgment on the fault information to obtain a first initial false alarm determination result of the fault information includes:

[0035] Performing logical judgment on the above fault information to obtain a first initial false alarm judgment result of the above fault information;

[0036] If the first initial false alarm determination result of the fault information is that the fault information is a false alarm, a first initial value is used to represent the first initial false alarm determination result;

[0037] If the first initial false alarm determination result of the fault information is that the fault information is not a false alarm, then a second initial value is used to represent the first initial false alarm determination result;

[0038] Accordingly, the algorithm judgment is performed on the fault information to obtain the second initial false alarm judgment result of the fault information, including:

[0039] Performing algorithm judgment on the above fault information to obtain a second initial false alarm judgment result of the above fault information;

[0040] If the second initial false alarm determination result of the fault information is that the fault information is a false alarm, then the first initial value is used to represent the second initial false alarm determination result;

[0041] If the second initial false alarm determination result of the fault information is that the fault information is not a false alarm, the second initial value is used to represent the second initial false alarm determination result.

[0042] Optionally, the step of determining a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight includes:

[0043] Multiplying the first initial false alarm discrimination result by the first target weight to obtain a first target value;

[0044] Multiplying the second initial false alarm discrimination result by the second target weight to obtain a second target value;

[0045] Adding the first target value to the second target value to obtain an addition result;

[0046] The target false alarm discrimination result of the above fault information is determined based on the above addition result.

[0047] Optionally, the step of determining a target false alarm discrimination result of the fault information based on the addition result includes:

[0048] If the above addition result is greater than the preset value, the target false alarm discrimination result of the above fault information is determined as the above fault information is not a false alarm;

[0049] If the above addition result is less than or equal to a preset value, the target false alarm discrimination result of the above fault information is determined as the above fault information being a false alarm.

[0050] Optionally, the step of obtaining a first target weight of a first parameter corresponding to the logical judgment, and obtaining a second target weight of a second parameter corresponding to the algorithm judgment, includes:

[0051] Initializing the first parameter corresponding to the above logic judgment and the second parameter corresponding to the above algorithm judgment to obtain a first initial weight of the above first parameter and a second initial weight of the above second parameter;

[0052] Obtaining a first historical discrimination result obtained by performing a logical judgment on the historical fault information, and obtaining a second historical discrimination result obtained by performing an algorithmic judgment on the historical fault information;

[0053] Determine a final determination result of the historical fault information according to the first historical determination result, the second historical determination result, the first initial weight and the second initial weight;

[0054] If the final judgment result does not meet the preset condition, the first initial weight and the second initial weight are updated, and the process returns to execute the first historical judgment result obtained by performing logical judgment on the historical fault information, and the second historical judgment result obtained by performing algorithmic judgment on the historical fault information;

[0055] If the above final judgment result meets the preset conditions, the above first initial weight is used as the first target weight, and the above second initial weight is used as the second target weight.

[0056] Optionally, after obtaining the fault information, the method further includes:

[0057] The above fault information is stored in a database as training fault information;

[0058] The trained neural network model is optimized based on the training fault information in the database to obtain an optimized neural network model.

[0059] Optionally, after optimizing the trained neural network model based on the training fault information in the database to obtain the optimized neural network model, the method further includes:

[0060] Modifying the first target weight and the second target weight to obtain a modified first target weight and a modified second target weight;

[0061] Accordingly, the method of determining the target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight includes:

[0062] A target false alarm determination result of the fault information is determined based on the first initial false alarm determination result, the second initial false alarm determination result, the modified first target weight, and the modified second target weight.

[0063] In another aspect, an information suppression device is provided, comprising:

[0064] An information acquisition module, used to acquire fault information;

[0065] A judgment module, used for performing a logical judgment on the fault information to obtain a first initial false alarm judgment result of the fault information, and performing an algorithm judgment on the fault information to obtain a second initial false alarm judgment result of the fault information;

[0066] A weight acquisition module, used to acquire a first target weight of the first parameter corresponding to the above logic judgment, and to acquire a second target weight of the second parameter corresponding to the above algorithm judgment;

[0067] A determination module, configured to determine a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight;

[0068] The suppression module is used to suppress the fault information if the target false alarm determination result is that the fault information is a false alarm.

[0069] On the other hand, a computer device is provided, including a processor and a memory, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to implement the information suppression method provided in an embodiment of the present invention.

[0070] On the other hand, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps of any one of the information suppression methods provided in the embodiments of the present invention.

[0071] One of the above technical solutions has the following advantages or beneficial effects: first obtaining fault information; then performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information, and performing algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information; secondly obtaining a first target weight of a first parameter corresponding to the logical judgment, and obtaining a second target weight of a second parameter corresponding to the algorithmic judgment; determining a target false alarm discrimination result of the fault information based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight; if the target false alarm discrimination result is that the fault information is a false alarm, suppressing the fault information.

[0072] That is, in an embodiment of the present application, a first initial false alarm discrimination result and a second initial false alarm discrimination result are obtained by simultaneously performing logical judgment and algorithmic judgment on the fault information, and then a target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight. If the target false alarm discrimination result is that the fault information is a false alarm, the fault information is suppressed so that the fault information that is a false alarm is not prompted or stored, thereby saving storage resources and reducing the manpower cost and time cost of aircraft maintenance.

[0073] Another technical solution among the above technical solutions has the following advantages or beneficial effects: the first initial false alarm determination result is determined according to the power-on time of the fault reporting system of the fault information.

[0074] Another technical solution among the above technical solutions has the following advantages or beneficial effects: determining the first initial false alarm discrimination result according to the fault character string in the fault information.

[0075] Another technical solution among the above technical solutions has the following advantages or beneficial effects: determining the first initial false alarm discrimination result according to the fault type of the fault information makes it possible to exclude whether the fault information is caused by the associated system, thereby improving the accuracy of the first initial false alarm discrimination result.

[0076] Another technical solution in the above technical solution has the following advantages or beneficial effects: the acquired fault information is stored in a database as training fault information, and then the trained neural network model is optimized based on the database, thereby improving the accuracy of the second initial false alarm discrimination result.

[0077] Another technical solution among the above technical solutions has the following advantages or beneficial effects: after optimizing the trained neural network model, the first target weight and the second target weight are modified, thereby improving the accuracy of the target false alarm discrimination result. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments are briefly introduced below. Obviously, the drawings in the following description only relate to some embodiments of the present invention, but are not intended to limit the present invention.

[0079] Figure 1 A schematic diagram of a flow chart of an information suppression method provided by an embodiment of the present invention;

[0080] Figure 2 A schematic diagram of a logic judgment flow chart provided by an embodiment of the present invention;

[0081] Figure 3 An interactive schematic diagram of the information suppression method provided by an embodiment of the present invention;

[0082] Figure 4 A schematic diagram of the structure of an information suppression device provided by an embodiment of the present invention;

[0083] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In the drawings, the shapes and dimensions may be exaggerated for clarity, and the same reference numerals will be used throughout to designate the same or like components.

[0086] Unless otherwise defined, the technical or scientific terms used herein shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar words used in the patent application specification and claims of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantity limitation, but indicate the existence of at least one. "Multiple" refers to two or more. Words such as "include" or "comprise" and similar words mean that the elements or objects appearing before "include" or "comprise" include the elements or objects listed after "include" or "comprise" and their equivalents, and do not exclude other elements or objects. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0087] The embodiments of the present invention provide an information suppression method, device, computer equipment and readable storage medium. The information suppression device can be integrated in a computer equipment, which can be a terminal or other equipment, or an aircraft, or a maintenance system on the aircraft.

[0088] The terminal includes but is not limited to a smart phone, a tablet computer, a laptop computer, a desktop computer, etc. The aircraft includes but is not limited to an airplane, a drone, a space probe, etc.

[0089] Embodiment 1

[0090] The information suppression method provided in the embodiments of the present application is described in detail below.

[0091] See also Figure 1 As shown, Figure 1 The method shown is an information suppression method provided by an embodiment of the present invention. The method provided by this embodiment can save storage resources and reduce the time cost and manpower cost of aircraft maintenance. The method provided by this embodiment includes:

[0092] S101. Obtain fault information.

[0093] The power-on test of an aircraft's onboard system (the onboard system refers to each system on the aircraft) refers to a test performed after the system's Line Replaceable Unit (LRU) is powered on to confirm whether there is a fault in the system or equipment on the aircraft.

[0094] Among them, the power-on test can test the LRU hardware (the LRU hardware includes the LRU central processing unit (CPU), the LRU random access memory (RAM), the LRU read-only memory (ROM), the LRU timer, the LRU interrupt controller, etc.), peripheral devices, and any LRU subsystem that does not report its own health status.

[0095] When an onboard system is tested, it is called a system under test. If the system under test has fault information, it is called a fault reporting system. When the aircraft is powered on for testing, if the system under test indicates a fault, it will generate corresponding fault information, and the computer equipment will obtain the fault information.

[0096] S102 , performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information, and performing algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information.

[0097] After acquiring the fault information, the computer device may respectively perform logic judgment and algorithm judgment on the fault information, thereby obtaining a first initial false alarm determination result and a second initial false alarm determination result of the fault information.

[0098] Logical judgment refers to the process of judging fault information based on the power-on time of the fault reporting system corresponding to the fault information and the fault string in the fault information. Algorithmic judgment refers to the process of judging fault information using a preset algorithm. For example, the preset algorithm can be a neural network model or a fault tree.

[0099] The first initial false alarm determination result may include whether the fault information is a false alarm or not. Optionally, the computer device may directly output "yes" or "no", "yes" indicates that the fault information is a false alarm, and "no" indicates that the fault information is not a false alarm. Or

[0100] Alternatively, the first initial value may be used to indicate that the fault information is a false alarm, and the second initial value may be used to indicate that the fault information is not a false alarm, and the computer device may directly output the first initial value and the second initial value, that is, when the first initial false alarm judgment result is that the fault information is a false alarm, the computer device outputs the first initial value, and when the second initial false alarm judgment result is that the fault information is not a false alarm, the computer device outputs the second output value.

[0101] For example, the first initial value is 0, and the second initial value is 1. When the computer device outputs 0, it indicates that the fault information is a false alarm. When the computer device outputs 1, it indicates that the fault information is not a false alarm.

[0102] Similarly, the representation method of the second initial false alarm determination result may refer to the representation method of the first initial false alarm determination result, and this embodiment will not be described in detail here.

[0103] It should be noted that if the aircraft is currently in flight, the computer can directly store and prompt the fault information after obtaining it. If the aircraft is on the ground, the fault information will be subjected to logical and algorithmic judgment.

[0104] In some embodiments, performing logical judgment on the fault information to obtain a first initial false alarm determination result of the fault information includes:

[0105] Obtain the power-on time of the fault reporting system corresponding to the fault information;

[0106] If the power-on time is less than or equal to the preset threshold, the first initial false alarm determination result is determined as the fault information being a false alarm.

[0107] If the power-on time is less than or equal to the preset threshold, it means that the fault information is a false alarm. When the power supply starts to supply power to the alarm system, the indication on the busbar flow of the alarm system will be set to 1. Therefore, the process of determining the power-on time of the alarm system of the fault information can be:

[0108] A target time for setting the indication on the busbar flow of the fault reporting system to 1 is obtained, and a time interval between the target time and the time when the fault information is obtained is used as a power-on time of the fault reporting system.

[0109] If the power-on time is greater than the preset threshold, the first initial false alarm determination result may be determined as the fault information is not a false alarm.

[0110] In other embodiments, if the power-on time is greater than a preset threshold, a character string is extracted from the fault information to obtain a fault character string; and a first initial false alarm determination result of the fault information is determined based on the fault character string.

[0111] Before the power-on test, the format of the fault information may be set so that the fault information includes a fault character string, and the fault character string may be defined.

[0112] For example, the fault string may be a 32-bit number, the 14th to 29th digits in the fault string represent the fault type, and the 30th to 31st digits in the fault string represent the state matrix bit.

[0113] For another example, the fault string may be 64 characters, the 24th to 39th characters in the fault string (in this case, the characters may include numbers and letters) represent the fault type, and the 40th to 41st characters in the fault string represent the state matrix bits.

[0114] The user may select the format and number of characters in the fault character string according to actual conditions, and this application does not limit this.

[0115] In this embodiment, when the power-on time is greater than the preset threshold, the first initial false alarm determination result is not immediately determined as the fault information is not a false alarm, but the first initial false alarm determination result is determined according to the fault character string in the fault information, thereby improving the accuracy of the first initial false alarm determination result.

[0116] Optionally, refer to Figure 2 The process of determining the first initial false alarm discrimination result of the fault information based on the fault character string may be:

[0117] determining the state of the state matrix bits in the fault string;

[0118] If the state of the state matrix bit is an abnormal state, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0119] If the state of the state matrix bit is normal, feature extraction is performed on the fault string to obtain the fault type corresponding to the fault information;

[0120] If the fault type corresponding to the fault information is an internal fault, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0121] For example, when the state matrix bit is 00 or 11, the state of the state matrix bit is an abnormal state, and when the state matrix bit is 01 or 10, the state of the state matrix bit is a normal state.

[0122] In this embodiment, various fault types can be stored in the fault string, so that when the state of the state matrix bit in the fault string is normal, the fault type can be extracted from the fault string. If the fault type is an internal fault, it means that the fault information is not a false alarm.

[0123] If the fault type corresponding to the fault information is an external fault, it means that the fault information may be caused by a fault in an associated system of the fault reporting system. In this case, the associated system corresponding to the fault information is searched, and then the electrical state of the associated system is determined; if the electrical state of the associated system is a powered-on state, the first initial false alarm judgment result determines that the fault information is not a false alarm; if the electrical state of the associated system is a powered-off state, the first initial false alarm judgment result determines that the fault information is a false alarm.

[0124] After the output information of system B is input into system A, system A performs a power-on test. That is, when system A performs a power-on test, the output information of system B is used as the input information of system A. Then system B is called an associated system of system A.

[0125] It should be noted that the output information of system B can be directly used as the input information of system A, or the output information of system B can also be indirectly used as the input information of system A. For example, the output information of system B can be used as the input information of system C, and the output information of system C can be used as the input information of system A. In this case, system B is also called the associated system of system A.

[0126] The computer device can associate and store the fault information with the associated system based on the predefined fault logic. For example, the fault logic is: when the C system is powered on, the output information of the A system and the output information of the B system are required to be used as the input information of the C system. If the output information of the A system and / or the output information of the B system are not input to the C system when the C system is powered on, the C system generates fault information. Then the fault information can be associated and stored with the A system and the B system.

[0127] Therefore, when the fault type corresponding to the fault information is an external fault, the associated systems corresponding to the fault information are searched: system A and system B. Then the electrical state of the associated system is determined. If the electrical state of the associated system is a powered-on state, it means that the output information of system A and the output information of system B are input to system C, there is no problem with system A and system B, and there is a fault in the fault reporting system. The first initial false alarm determination result is determined as the fault information is not a false alarm.

[0128] If the power state of the associated system is a power-off state, it means that the fault information is caused by the lack of input information of the associated system, and the fault reporting system does not have a fault, and the first initial false alarm determination result is determined as the fault information being a false alarm.

[0129] The process of determining the power state of the associated system may be: checking the indication of the associated system; if the indication of the associated system is 1, it indicates that the power state of the associated system is powered on; if the indication of the associated system is not 1, it indicates that the power state of the associated system is powered off.

[0130] In other embodiments, performing algorithmic judgment on the fault information to obtain a second initial false alarm judgment result of the fault information includes:

[0131] The fault information is input into the trained neural network model for judgment, and a second initial false alarm judgment result of the fault information is obtained.

[0132] In this embodiment, the fault information is algorithmically judged by using a trained neural network model, thereby obtaining a second initial false alarm judgment result of the fault information.

[0133] For the specific type of the trained neural network model, the user can select it according to the actual situation. For example, in this embodiment, the trained neural network model can be a convolutional neural network or a support vector machine (SVM), which is not specifically limited in this embodiment.

[0134] In other embodiments, in the process of inputting fault information into a trained neural network model for judgment, external environmental parameters corresponding to the fault information can also be input into the trained neural network model at the same time, so that the trained neural network model can judge the fault information based on the external environmental parameters.

[0135] Furthermore, before inputting the fault information and external environmental parameters into the trained neural network model, the fault information and external environmental parameters may be preprocessed. The preprocessing includes but is not limited to extracting the fault character string from the fault information, vectorizing, standardizing, processing missing values, and extracting feature values ​​of the external environmental parameters.

[0136] The external environmental parameters include the configuration version number of the aircraft configuration (because when the configuration version number of the aircraft configuration is different, the version numbers of the software and hardware used are also different, and the functions of software and hardware with different version numbers are different, the fault logic is also different. Therefore, the external parameters may include the configuration version number of the aircraft configuration), the power-on time of the fault reporting system and the indication of the busbar flow of the fault reporting system, the service life of the fault reporting system, the indication of the busbar flow of the associated systems of the fault reporting system, the association relationship between various airborne systems, etc.

[0137] It should be noted that after the fault information is input into the trained neural network model for judgment, a probability value is actually obtained. When the second initial false alarm judgment result is represented by the first initial value and the second initial value, if the probability value is less than the preset probability, the first initial value is used to represent the second initial false alarm judgment result; if the probability value is greater than or equal to the preset probability, the second initial value is used to represent the second initial false alarm judgment result.

[0138] For example, the preset probability value is 0.5, the first initial value is 0, and the second initial value is 1, 0 indicates that the second initial false alarm determination result is that the fault information is a false alarm, and 1 indicates that the second initial false alarm determination result is that the fault information is not a false alarm. When the probability value is 0.4, 0.4 is less than 0.5, then 0 is used to represent the second initial false alarm determination result, and when the probability value is 0.6, 0.6 is greater than 0.5, then 1 is used to represent the second initial false alarm determination result.

[0139] Before inputting the fault information into the trained neural network model for judgment, the neural network model to be trained may be trained first. The training process may be:

[0140] The training fault information is obtained, and the training fault information is classified using the preset data dictionary criteria to obtain the true label of the training fault information. The fault information and the true label of the fault information are input into the neural network model to be trained to obtain the predicted label corresponding to the training fault information, and the target loss value is determined based on the predicted label and the true label.

[0141] If the target loss value is less than the preset value, the training is stopped to obtain the trained neural network model. If the target loss value is greater than or equal to the preset value, the network parameters of the neural network model to be trained are updated based on the target loss value, and the execution is returned to input the fault information and the real label of the fault information into the neural network model to be trained to obtain the predicted label corresponding to the training fault information.

[0142] The preset data dictionary criteria is a tool that defines all fault information. For example, when the format of the fault information is bus name + "missing", the category of the fault information is interface fault information, that is, the preset data dictionary criteria defines the description method of the interface fault information as bus name + "missing".

[0143] S103, obtaining a first target weight of a first parameter corresponding to the logical judgment, and obtaining a second target weight of a second parameter corresponding to the algorithmic judgment.

[0144] Before performing logical judgment and algorithmic judgment on the fault information, the computer device can set and store the first target weight of the first parameter of the logical judgment and the second target weight of the second parameter of the algorithmic judgment. After performing logical judgment and algorithmic judgment on the fault information, the computer device can directly obtain the first target weight and the second target weight from the storage space.

[0145] The computer device may also set the first target weight and the second target weight after performing logical judgment and algorithm judgment on the fault information.

[0146] The user may select the setting time of the first target weight and the second target weight according to actual conditions, and this application does not make any specific restrictions here. The sum of the first initial weight and the second initial weight may be 1.

[0147] In some embodiments, the process of setting the first target weight of the first parameter and the second target weight of the second parameter may be:

[0148] Initialize the first parameter and the second parameter to obtain a first initial weight of the first parameter and a second initial weight of the second parameter.

[0149] Obtaining a first historical discrimination result obtained by performing a logical judgment on the historical fault information, and obtaining a second historical discrimination result obtained by performing an algorithmic judgment on the historical fault information;

[0150] Determine a final determination result of the historical fault information according to the first historical determination result, the second historical determination result, the first initial weight, and the second initial weight;

[0151] If the final judgment result does not meet the preset condition, the first initial weight and the second initial weight are updated, and the process returns to execute the first historical judgment result obtained by performing a logical judgment on the historical fault information, and the second historical judgment result obtained by performing an algorithmic judgment on the historical fault information;

[0152] If the final judgment result meets the preset conditions, the first initial weight is used as the first target weight, and the second initial weight is used as the second target weight.

[0153] The first initial weight and the second initial weight may be updated manually based on experience, or may be updated according to a preset search algorithm. The preset search algorithm may be a best-first search strategy or a backtracking method. This embodiment is not specifically limited here.

[0154] It should be noted that the value range of the first system and the value range of the second coefficient can be determined based on the discrimination accuracy of the trained neural network model and the discrimination accuracy of the logical judgment, and then the first target weight and the second target weight can be found from the value range of the first coefficient and the value range of the second coefficient according to the preset search algorithm.

[0155] The discrimination accuracy can be determined based on historical discrimination results. For example, if 5 out of 10 historical discrimination results are accurate, the discrimination accuracy is 0.5.

[0156] However, when performing algorithmic judgment through the trained neural network model, since the number of training fault information in the database is small at the beginning, the accuracy of the second historical judgment result obtained by the trained neural network model through algorithmic judgment of the fault information at the beginning may not be very high, that is, the judgment accuracy of the trained neural network model at this time may be lower than the judgment accuracy of the logical judgment.

[0157] Then the first target weight finally obtained can be set to be greater than the second target weight. That is, when setting the preset condition, the setting can be made based on the first target weight being greater than the second target weight.

[0158] Moreover, at this time, the first historical judgment result and the second historical judgment result can be replaced by the judgment accuracy, that is, at this time, the final judgment result of the historical fault information is determined according to the judgment accuracy of the logical judgment, the judgment accuracy of the algorithm judgment, the first initial weight and the second initial weight.

[0159] The process of determining the final judgment result of the historical fault information according to the judgment accuracy of the logical judgment, the judgment accuracy of the algorithm judgment, the first initial weight and the second initial weight may be:

[0160] The first historical value is obtained by multiplying the discrimination accuracy of the logical judgment with the first initial weight, and the second historical value is obtained by multiplying the discrimination accuracy of the algorithm judgment with the second initial weight. The first historical value and the second historical value are added to obtain a historical addition result, and the final discrimination result is determined according to the historical addition result.

[0161] S104 , determining a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight.

[0162] In this embodiment, a weighted fusion is performed on the first initial false alarm discrimination result and the second initial false alarm discrimination result of the fault information, so as to obtain a target false alarm discrimination result of the fault information.

[0163] The method of weighted fusion of the first initial false alarm discrimination result and the second initial false alarm discrimination result of the fault information may be:

[0164] Multiplying the first initial false alarm discrimination result by the first target weight to obtain a first target value;

[0165] Multiplying the second initial false alarm discrimination result by the second target weight to obtain a second target value;

[0166] Adding the first target value to the second target value to obtain an addition result;

[0167] A target false alarm discrimination result of the fault information is determined based on the addition result.

[0168] Optionally, the process of determining the target false alarm discrimination result of the fault information based on the addition result may be:

[0169] If the added result is greater than the preset value, the target false alarm discrimination result of the fault information is determined as the fault information is not a false alarm;

[0170] If the addition result is less than or equal to the preset value, the target false alarm discrimination result of the fault information is determined as the fault information being a false alarm.

[0171] For example, the first target weight is 0.4, the second target weight is 0.6, and the preset value is 0.5. When the first initial false alarm discrimination result is 1 and the second initial false alarm discrimination result is 0, the first target value is 0.4, the second target value is 0, and the sum is 0.4. Since 0.4 is less than 0.5, the target false alarm discrimination result is that the fault information is a false alarm.

[0172] When the first initial false alarm determination result is 0 and the second initial false alarm determination result is 1, the first target value is 0 and the second target value is 0.6, and the sum is 0.6. Since 0.6 is greater than 0.5, the target false alarm determination result is that the fault information is not a false alarm.

[0173] In this embodiment, by simultaneously performing logical judgment and algorithm judgment on the fault information, and then weightedly fusing the results of the logical judgment and the results of the algorithm judgment, the accuracy of the target false alarm discrimination result of the fault information is improved.

[0174] For example, Figure 3 As shown, after the fault information is obtained, the fault information and the external environment parameters are respectively subjected to logical judgment and algorithm judgment to obtain a first initial false alarm discrimination result and a second initial false alarm discrimination result. Then, the first initial false alarm discrimination result and the second initial false alarm discrimination result are weightedly fused to obtain a target false alarm discrimination result.

[0175] In some embodiments, after a computer device obtains fault information, it can store the fault information in a database as training fault information. Therefore, the amount of training fault information in the database will increase. Subsequently, the trained neural network model can be optimized according to the database to obtain an optimized neural network model, thereby improving the discrimination accuracy of the neural network model.

[0176] It should be noted that when it is detected that the number of training fault information added in the database reaches a preset number, the trained neural network model is optimized according to the database. Alternatively, when a preset time interval is reached, the trained neural network model is optimized according to the database.

[0177] In this embodiment, the trained neural network model is optimized to improve the accuracy of the second initial false alarm discrimination result.

[0178] When the discrimination accuracy of the neural network model is improved, the value of the second parameter can be increased, thereby improving the accuracy of the target false alarm discrimination result. Therefore, after optimizing the trained neural network model, the first target weight and the second target weight can be modified to obtain a modified first target weight and a modified second target weight, wherein the modified second target weight is greater than the second target weight, and the modified first target weight is less than the first target weight.

[0179] The first target weight and the second target weight can be modified manually or according to a preset search algorithm. In the process of searching and modifying the second target weight and modifying the first target weight according to the preset search algorithm, since the discrimination accuracy of the optimized neural network model is improved at this time, the value range of the second coefficient can be modified, and then the preset search algorithm is used to search and modify the first target weight and modify the second target weight from the value range of the first coefficient and the modified value range of the second coefficient, so that the modified second target weight is greater than the modified first target weight.

[0180] And / or, in the process of searching and modifying the second target weight and modifying the first target weight according to the preset search algorithm, the preset condition may also be modified so that modifying the second target weight is greater than modifying the first target weight.

[0181] Finally, the target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the modification of the first target weight, and the modification of the second target weight. That is, after optimizing the trained neural network model and modifying the first target weight and the second target weight, if fault information is obtained again, the optimized neural network model is used to perform algorithmic judgment on the fault information, and the target false alarm discrimination result is determined based on the modification of the first target weight and the modification of the second target weight.

[0182] In this embodiment, the first target weight and the second target weight are modified to improve the accuracy of the target false alarm discrimination result.

[0183] S105: If the target false alarm determination result is that the fault information is a false alarm, the fault information is suppressed.

[0184] After the computer device obtains the target false alarm discrimination result, if the target false alarm discrimination result is that the fault information is a false alarm, the fault information is suppressed. Suppressing the fault information means not prompting and not storing the fault information.

[0185] If the target false alarm discrimination result is that the fault information is not a false alarm, the fault information is prompted and stored, or stored first and then prompted, or prompted first and then stored.

[0186] As can be seen from the above, in the embodiment of the present application, fault information is obtained in sequence; then, a logical judgment is performed on the fault information to obtain a first initial false alarm judgment result of the fault information, and an algorithmic judgment is performed on the fault information to obtain a second initial false alarm judgment result of the fault information; secondly, a first target weight of a first parameter corresponding to the logical judgment is obtained, and a second target weight of a second parameter corresponding to the algorithmic judgment is obtained; a target false alarm judgment result of the fault information is determined based on the first initial false alarm judgment result, the second initial false alarm judgment result, the first target weight, and the second target weight; if the target false alarm judgment result is that the fault information is a false alarm, the fault information is suppressed.

[0187] That is, in an embodiment of the present application, a first initial false alarm discrimination result and a second initial false alarm discrimination result are obtained by simultaneously performing logical judgment and algorithmic judgment on the fault information, and then a target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight. If the target false alarm discrimination result is that the fault information is a false alarm, the fault information is suppressed so that the fault information that is a false alarm is not prompted or stored, thereby saving storage resources and reducing the manpower cost and time cost of aircraft maintenance.

[0188] Embodiment 2

[0189] In order to better implement the above method, the embodiment of the present invention further provides an information suppression device, for example, Figure 4 As shown, the information suppression device may include:

[0190] The information acquisition module 401 is used to acquire fault information.

[0191] The judgment module 402 is used to perform a logical judgment on the fault information to obtain a first initial false alarm judgment result of the fault information, and to perform an algorithmic judgment on the fault information to obtain a second initial false alarm judgment result of the fault information.

[0192] The weight acquisition module 403 is used to acquire a first target weight of a first parameter corresponding to a logical judgment, and to acquire a second target weight of a second parameter corresponding to an algorithmic judgment.

[0193] The determination module 404 is used to determine a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight.

[0194] The suppression module 405 is used to suppress the fault information if the target false alarm determination result is that the fault information is a false alarm.

[0195] Optionally, the determination module 402 is specifically configured to execute:

[0196] Obtain the power-on time of the fault reporting system corresponding to the fault information;

[0197] If the power-on time is less than or equal to the preset threshold, the first initial false alarm determination result is determined as the fault information being a false alarm.

[0198] Optionally, the determination module 402 is specifically configured to execute:

[0199] If the power-on time is greater than a preset threshold, the fault information is subjected to character string extraction to obtain a fault character string;

[0200] A first initial false alarm determination result of the fault information is determined based on the fault character string.

[0201] Optionally, the determination module 402 is specifically configured to execute:

[0202] If the fault character string does not include the state matrix bit, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0203] Optionally, the determination module 402 is specifically configured to execute:

[0204] If the fault character string includes the state matrix bit, feature extraction is performed on the fault character string to obtain the fault type corresponding to the fault information;

[0205] If the fault type corresponding to the fault information is an internal fault, the first initial false alarm determination result is determined as the fault information is not a false alarm.

[0206] Optionally, the determination module 402 is specifically configured to execute:

[0207] If the fault type corresponding to the fault information is an external fault, then find the associated system corresponding to the fault information;

[0208] determining the electrical status of associated systems;

[0209] If the electrical state of the associated system is a powered-on state, the first initial false alarm determination result determines that the fault information is not a false alarm;

[0210] If the electrical state of the associated system is a power-off state, the first initial false alarm determination result determines that the fault information is a false alarm.

[0211] Optionally, the determination module 402 is specifically configured to execute:

[0212] The fault information is input into the trained neural network model for judgment, and a second initial false alarm judgment result of the fault information is obtained.

[0213] Optionally, the determination module 402 is specifically configured to execute:

[0214] Obtain external environment parameters corresponding to the fault information;

[0215] The fault information and external environmental parameters are input into the trained neural network model for judgment, and a second initial false alarm judgment result of the fault information is obtained.

[0216] Optionally, the determination module 402 is specifically configured to execute:

[0217] Performing logical judgment on the fault information to obtain a first initial false alarm judgment result of the fault information;

[0218] If the first initial false alarm determination result of the fault information is that the fault information is a false alarm, then a first initial value is used to represent the first initial false alarm determination result;

[0219] If the first initial false alarm determination result of the fault information is that the fault information is not a false alarm, the second initial value is used to represent the first initial false alarm determination result;

[0220] Performing algorithmic judgment on the fault information to obtain a second initial false alarm judgment result of the fault information;

[0221] If the second initial false alarm determination result of the fault information is that the fault information is a false alarm, the first initial value is used to represent the second initial false alarm determination result;

[0222] If the second initial false alarm determination result of the fault information is that the fault information is not a false alarm, the second initial value is used to represent the second initial false alarm determination result.

[0223] Optionally, the determination module 404 is specifically configured to execute:

[0224] Multiplying the first initial false alarm discrimination result by the first target weight to obtain a first target value;

[0225] Multiplying the second initial false alarm discrimination result by the second target weight to obtain a second target value;

[0226] Adding the first target value to the second target value to obtain an addition result;

[0227] A target false alarm discrimination result of the fault information is determined based on the addition result.

[0228] Optionally, the determination module 404 is specifically configured to execute:

[0229] If the added result is greater than the preset value, the target false alarm discrimination result of the fault information is determined as the fault information is not a false alarm;

[0230] If the addition result is less than or equal to the preset value, the target false alarm discrimination result of the fault information is determined as the fault information being a false alarm.

[0231] Optionally, the weight acquisition module 403 is specifically configured to execute:

[0232] Initialize a first parameter corresponding to the logic judgment and a second parameter corresponding to the algorithm judgment to obtain a first initial weight of the first parameter and a second initial weight of the second parameter;

[0233] Obtaining a first historical discrimination result obtained by performing a logical judgment on the historical fault information, and obtaining a second historical discrimination result obtained by performing an algorithmic judgment on the historical fault information;

[0234] Determine a final determination result of the historical fault information according to the first historical determination result, the second historical determination result, the first initial weight, and the second initial weight;

[0235] If the final judgment result does not meet the preset condition, the first initial weight and the second initial weight are updated, and the process returns to execute the first historical judgment result obtained by performing a logical judgment on the historical fault information, and the second historical judgment result obtained by performing an algorithmic judgment on the historical fault information;

[0236] If the final judgment result meets the preset conditions, the first initial weight is used as the first target weight, and the second initial weight is used as the second target weight.

[0237] Optionally, the information suppression device further includes:

[0238] A storage module, used for storing the fault information as training fault information in a database;

[0239] The optimization module is used to optimize the trained neural network model based on the training fault information in the database to obtain the optimized neural network model.

[0240] Optionally, the information suppression device further includes:

[0241] A modification module, used to modify the first target weight and the second target weight to obtain a modified first target weight and a modified second target weight;

[0242] Accordingly, the determination module 404 is specifically configured to execute:

[0243] A target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the modified first target weight, and the modified second target weight.

[0244] The functions of the information suppression device provided in this embodiment correspond to the functions implemented in the first embodiment, so for other functions of this embodiment, please refer to the contents of the first embodiment, which will not be described in detail here.

[0245] Embodiment 3

[0246] The embodiment of the present invention also provides a computer device, such as Figure 5 As shown, it shows a schematic diagram of the structure of a computer device involved in an embodiment of the present invention, specifically:

[0247] The computer device may include one or more processing core processors 501, one or more computer-readable storage media memories 502, a power supply 503, an input unit 504 and other components. Those skilled in the art will appreciate that Figure 5 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0248] The processor 501 is the control center of the computer device. It uses various interfaces and lines to connect various parts of the entire computer device. By running or executing computer programs and / or modules stored in the memory 502 and calling data stored in the memory 502, it executes various functions of the computer device and processes data, thereby monitoring the computer device as a whole. Optionally, the processor 501 may include one or more processing cores; preferably, the processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 501.

[0249] The memory 502 can be used to store computer programs and modules. The processor 501 executes various functional applications and data processing by running the computer programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, a computer program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 502 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0250] The computer device also includes a power supply 503 for supplying power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, so as to manage charging, discharging, power consumption and other functions through the power management system. The power supply 503 can also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators and other arbitrary components.

[0251] The computer device may further include an input unit 504, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0252] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 501 in the computer device will load the executable files corresponding to the processes of one or more computer programs into the memory 502 according to the following instructions, and the processor 501 will run the computer programs stored in the memory 502, thereby realizing various functions, such as:

[0253] Get fault information;

[0254] Performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information, and performing algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information;

[0255] Obtaining a first target weight of a first parameter corresponding to a logical judgment, and obtaining a second target weight of a second parameter corresponding to an algorithmic judgment;

[0256] Determine a target false alarm discrimination result of the fault information based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight;

[0257] If the target false alarm determination result is that the fault information is a false alarm, the fault information is suppressed.

[0258] The specific implementation of the above operations and the corresponding beneficial effects can be found in the previous embodiments, which will not be described in detail here.

[0259] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by a computer program, or by controlling related hardware through a computer program. The computer program may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0260] To this end, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored. The computer program can be loaded by a processor to execute the steps in any information suppression method provided by the embodiment of the present invention.

[0261] The specific implementation of the above operations and the corresponding beneficial effects can be found in the previous embodiments, which will not be described in detail here.

[0262] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0263] Since the computer program stored in the computer-readable storage medium can execute the steps in any information suppression method provided in the embodiments of the present invention, the beneficial effects that can be achieved by any information suppression method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0264] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above-mentioned information suppression method.

[0265] The above is a detailed introduction to an information suppression method, device, computer equipment and readable storage medium provided in an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An information suppression method, characterized in that: include: Get fault information; Performing a logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information, and performing an algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information; Obtaining a first target weight of a first parameter corresponding to the logical judgment, and obtaining a second target weight of a second parameter corresponding to the algorithmic judgment; Determine a target false alarm discrimination result of the fault information based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the first target weight, and the second target weight; If the target false alarm discrimination result is that the fault information is a false alarm, suppressing the fault information; The performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information includes: Obtaining the power-on time of the fault reporting system corresponding to the fault information; If the power-on time is less than or equal to a preset threshold, determining the first initial false alarm determination result as the fault information being a false alarm; The performing algorithm judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information includes: The fault information is input into a trained neural network model for judgment to obtain a second initial false alarm judgment result of the fault information.

2. The information suppression method according to claim 1, characterized in that: After obtaining the power-on time of the fault reporting system corresponding to the fault information, the method further includes: If the power-on time is greater than the preset threshold, extracting the character string from the fault information to obtain a fault character string; A first initial false alarm determination result of the fault information is determined based on the fault character string.

3. The information suppression method according to claim 2, characterized in that: The determining a first initial false alarm discrimination result of the fault information based on the fault character string comprises: determining states of state matrix bits in the fault string; If the state of the state matrix bit is an abnormal state, the first initial false alarm determination result is determined as the fault information is not a false alarm.

4. The information suppression method according to claim 3, characterized in that: After determining the state of the state matrix bit in the fault string, the method further comprises: If the state of the state matrix bit is a normal state, feature extraction is performed on the fault character string to obtain the fault type corresponding to the fault information; If the fault type corresponding to the fault information is an internal fault, the first initial false alarm determination result is determined as the fault information is not a false alarm.

5. The information suppression method according to claim 4, characterized in that: The method further comprises: If the fault type corresponding to the fault information is an external fault, searching for a related system corresponding to the fault information; determining an electrical state of the associated system; If the electrical state of the associated system is a powered-on state, the first initial false alarm determination result determines that the fault information is not a false alarm; If the electrical state of the associated system is a power-off state, the first initial false alarm determination result determines that the fault information is a false alarm.

6. The information suppression method according to claim 1, wherein: The step of inputting the fault information into a trained neural network model for determination to obtain a second initial false alarm determination result of the fault information includes: Obtaining external environment parameters corresponding to the fault information; The fault information and the external environment parameters are input into a trained neural network model for judgment, so as to obtain a second initial false alarm judgment result of the fault information.

7. The information suppression method according to claim 1, characterized in that: The performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information includes: Performing logical judgment on the fault information to obtain a first initial false alarm judgment result of the fault information; If the first initial false alarm determination result of the fault information is that the fault information is a false alarm, using a first initial value to represent the first initial false alarm determination result; If the first initial false alarm determination result of the fault information is that the fault information is not a false alarm, using a second initial value to represent the first initial false alarm determination result; Accordingly, performing algorithmic judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information includes: Performing algorithmic judgment on the fault information to obtain a second initial false alarm judgment result of the fault information; If the second initial false alarm determination result of the fault information is that the fault information is a false alarm, then using the first initial value to represent the second initial false alarm determination result; If the second initial false alarm determination result of the fault information is that the fault information is not a false alarm, the second initial value is used to represent the second initial false alarm determination result.

8. The information suppression method according to claim 7, characterized in that: The step of determining a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight includes: Multiplying the first initial false alarm discrimination result by the first target weight to obtain a first target value; Multiplying the second initial false alarm discrimination result by the second target weight to obtain a second target value; Adding the first target value and the second target value to obtain an addition result; A target false alarm discrimination result of the fault information is determined based on the addition result.

9. The information suppression method according to claim 8, characterized in that: The determining of the target false alarm discrimination result of the fault information based on the addition result includes: If the addition result is greater than a preset value, the target false alarm discrimination result of the fault information is determined as the fault information is not a false alarm; If the addition result is less than or equal to a preset value, the target false alarm discrimination result of the fault information is determined as the fault information being a false alarm.

10. The information suppression method according to any one of claims 1 to 9, characterized in that: The obtaining of a first target weight of a first parameter corresponding to the logic judgment, and the obtaining of a second target weight of a second parameter corresponding to the algorithm judgment, include: Initializing a first parameter corresponding to the logic judgment and a second parameter corresponding to the algorithm judgment to obtain a first initial weight of the first parameter and a second initial weight of the second parameter; Obtaining a first historical discrimination result obtained by performing a logical judgment on the historical fault information, and obtaining a second historical discrimination result obtained by performing an algorithmic judgment on the historical fault information; Determine a final determination result of the historical fault information according to the first historical determination result, the second historical determination result, the first initial weight, and the second initial weight; If the final judgment result does not meet the preset condition, the first initial weight and the second initial weight are updated, and the process returns to execute the first historical judgment result obtained by performing a logical judgment on the historical fault information, and the second historical judgment result obtained by performing an algorithmic judgment on the historical fault information; If the final judgment result meets the preset condition, the first initial weight is used as the first target weight, and the second initial weight is used as the second target weight.

11. The information suppression method according to claim 1, wherein: After obtaining the fault information, the method further includes: storing the fault information as training fault information in a database; The trained neural network model is optimized based on the training fault information in the database to obtain an optimized neural network model.

12. The information suppression method according to claim 11, characterized in that: After optimizing the trained neural network model based on the training fault information in the database to obtain the optimized neural network model, the method further includes: Modifying the first target weight and the second target weight to obtain a modified first target weight and a modified second target weight; Accordingly, the step of determining a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight includes: A target false alarm discrimination result of the fault information is determined based on the first initial false alarm discrimination result, the second initial false alarm discrimination result, the modified first target weight, and the modified second target weight.

13. An information suppression device, characterized in that: include: An information acquisition module, used to acquire fault information; A judgment module, used for performing a logical judgment on the fault information to obtain a first initial false alarm judgment result of the fault information, and performing an algorithm judgment on the fault information to obtain a second initial false alarm judgment result of the fault information; A weight acquisition module, used to acquire a first target weight of a first parameter corresponding to the logic judgment, and to acquire a second target weight of a second parameter corresponding to the algorithm judgment; A determination module, configured to determine a target false alarm determination result of the fault information based on the first initial false alarm determination result, the second initial false alarm determination result, the first target weight, and the second target weight; A suppression module, configured to suppress the fault information if the target false alarm determination result is that the fault information is a false alarm; The performing logical judgment on the fault information to obtain a first initial false alarm discrimination result of the fault information includes: Obtaining the power-on time of the fault reporting system corresponding to the fault information; If the power-on time is less than or equal to a preset threshold, determining the first initial false alarm determination result as the fault information being a false alarm; The performing algorithm judgment on the fault information to obtain a second initial false alarm discrimination result of the fault information includes: The fault information is input into a trained neural network model for judgment to obtain a second initial false alarm judgment result of the fault information.

14. A computer device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the information suppression method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the information suppression method according to any one of claims 1 to 12.

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