Military equipment monitoring system and method based on edge computing data processing and medium

Abstract

The invention belongs to the field of military equipment supervision, relates to a data analysis technology, is used for solving the problem that an existing military equipment monitoring mode cannot carry out refined level-to-level management and multi-factor fusion analysis on equipment, and particularly relates to a military equipment monitoring system and method based on edge computing data processing and a medium. The monitoring analysis center is in communication connection with a monitoring grading module, a parameter monitoring module, a monitoring analysis module, a fusion analysis module and a storage module; the monitoring grading module is used for carrying out grading monitoring on the military equipment, the parameter monitoring module is used for monitoring monitoring parameters, the monitoring analysis module is used for monitoring and analyzing the monitoring parameters of the graded military equipment, and the fusion analysis module is used for carrying out fusion analysis on the military equipment in combination with various monitoring parameters; according to the method, the risk state of the military equipment can be distinguished, so that targeted monitoring is realized, and the accuracy and the efficiency of judging the state of the military equipment are improved.

Description

Technical Field

[0001] The present invention belongs to the field of military equipment monitoring, involves data analysis technology, and specifically is a military equipment monitoring system based on edge computing data processing. Background Art

[0002] Military equipment refers to various equipment and devices used for military purposes and is an important part of the national defense force; in the modern military field, the complexity and importance of military equipment are constantly increasing, and efficient, accurate, and real-time monitoring of it has become the key to ensuring the smooth development of military operations and the safe and reliable operation of equipment.

[0003] The existing monitoring methods for military equipment often lack refined hierarchical management of equipment, making it difficult to allocate monitoring resources according to the different risk states and importance levels of equipment, and it is also difficult to accurately evaluate the state of equipment through comprehensive fusion analysis of multiple factors.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a military equipment monitoring system, method, and medium based on edge computing data processing, which are used to solve the problems that the existing military equipment monitoring methods cannot perform refined hierarchical management of equipment and multi-factor fusion analysis;

[0006] The technical problem that the present invention needs to solve is: how to provide a military equipment monitoring system, method, and medium based on edge computing data processing that can perform refined hierarchical management of military equipment and multi-factor fusion analysis.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A military equipment monitoring system based on edge computing data processing includes a monitoring and analysis center, and the monitoring and analysis center is communicatively connected to a monitoring classification module, a parameter monitoring module, a monitoring and analysis module, a fusion analysis module, and a storage module;

[0009] The monitoring classification module is used to perform hierarchical monitoring on military equipment: marking military equipment as a monitoring object, with a risk coefficient FX of the monitoring object, comparing the risk coefficient FX of the monitoring object with a preset risk threshold FXmax, and marking the monitoring object as an elastic object or a rigid object;

[0010] The parameter monitoring module is used to monitor monitoring parameters: marking the functional module of the monitoring object as monitoring point n, i = 1, 2,..., i, where i is a positive integer; installing various sensors distributively at monitoring point n to obtain the monitoring parameter m of monitoring point n in real time, where m = 1, 2,..., j, and j is a positive integer;

[0011] The monitoring and analysis module is used to monitor and analyze the monitoring parameters of the military equipment after classification: obtain the standard range [MINm, MAXm] corresponding to the monitoring parameter m, mark the monitoring parameter m that exceeds the corresponding standard range [MINm, MAXm] as a parameter anomaly, and mark the number of times of parameter anomalies occurring at the monitoring point n as the anomaly value YCn; analyze the anomaly value YCn of the monitoring point n in the rigid object and the elastic object to generate an alarm signal or a fusion analysis signal.

[0012] The fusion analysis module is used to perform fusion analysis on the military equipment by combining multiple monitoring parameters: mark the monitoring parameter m with parameter anomalies occurring at the monitoring point n as an abnormal parameter, mark the monitoring parameter m other than the abnormal parameter at the monitoring point n as a reference parameter, and analyze the reference parameter to generate an alarm signal or an observation signal.

[0013] Furthermore, the process of obtaining the risk coefficient FX includes: obtaining the usage duration SC, the number of usage times CS, and the storage quantity SL of the monitoring object; the usage duration SC is the difference between the real-time time when the monitoring object is in use and the start time of service; the number of usage times CS is the number of complete outbound and inbound operations of the monitoring object within the current usage duration SC; the storage quantity SL is the actual storage quantity of the same type of monitoring object in the equipment depot; perform numerical calculations on the usage duration SC, the number of usage times CS, and the storage quantity SL to obtain the risk coefficient FX of the monitoring object.

[0014] Furthermore, compare the risk coefficient FX of the monitoring object with the preset risk threshold FXmax: if the risk coefficient FX is less than the risk threshold FXmax, it is determined that the risk status of the monitoring object meets the requirements, and mark this monitoring object as an elastic object; if the risk coefficient FX is greater than or equal to the risk threshold FXmax, it is determined that the risk status of the monitoring object does not meet the requirements, and mark this monitoring object as a rigid object.

[0015] Furthermore, all sensors are connected to the edge computing node through wireless communication, and the edge computing node is equipped with an independent processor and memory.

[0016] Furthermore, when the monitoring object is a rigid object, if a parameter anomaly occurs at the monitoring point n, obtain the anomaly value YCn corresponding to the monitoring point n. If the anomaly value YCn of the monitoring point n is equal to 0, it is determined that this monitoring point n has an abnormal parameter for the first time, generate a fusion analysis signal and send the signal to the fusion analysis module; if the anomaly value YCn of the monitoring point n is greater than or equal to 1, it is determined that this monitoring point n does not have an abnormal parameter for the first time, generate an alarm signal and send the monitoring object and the monitoring point n to the monitoring analysis center.

[0017] Further, when the monitored object is an elastic object, obtain the abnormal threshold YCmax of monitoring point n through the storage module. If a parameter abnormality occurs at monitoring point n, obtain the abnormal value YCn corresponding to monitoring point n, and compare the abnormal value YCn with the abnormal threshold YCmax: If the abnormal value YCn of monitoring point n is less than or equal to the abnormal threshold YCmax, generate a fusion analysis signal and send the signal to the fusion analysis module; if the abnormal value YCn of monitoring point n is greater than the abnormal threshold YCmax, generate an alarm signal and send the monitored object and monitoring point n to the monitoring analysis center.

[0018] Further, calculate the standard range [MINm, MAXm] of the monitoring parameter m and the preset boundary parameter t: perform a division calculation of MINm by the boundary parameter t to obtain the lower boundary XBm, and perform a multiplication calculation of MAXm by the boundary parameter t to obtain the upper boundary SBm.

[0019] Further, mark the period from L1 seconds before the occurrence of parameter abnormality to the period when the parameter abnormality occurs as the analysis period. Establish a rectangular coordinate system with the reference parameter as the Y-axis of the coordinate system and time as the X-axis of the coordinate system, and draw a reference parameter-time curve graph; equally divide the reference parameter-time curve graph along the X-axis direction to obtain a number of analysis points p, and calculate the slope kp between analysis point p and its previous analysis point; if there exists an analysis point p where the corresponding reference parameter is greater than or equal to the upper boundary SBm, and (kp * kp - 1) is greater than 0, then determine that the monitoring parameter m is continuously increasing and there is a risk of exceeding the standard range [MINm, MAXm], generate an alarm signal and send the monitored object and monitoring point n to the monitoring analysis center; if there exists an analysis point p where the corresponding reference parameter is less than the lower boundary XBm, and (kp * kp - 1) is greater than 0, then determine that the monitoring parameter m is continuously decreasing and there is a risk of exceeding the standard range [MINm, MAXm], generate an alarm signal and send the monitored object and monitoring point n to the monitoring analysis center; otherwise, generate an observation signal and send the monitored object and monitoring point n to the monitoring analysis center.

[0020] A military equipment monitoring method based on edge computing data processing includes the following steps:

[0021] Step 1: Mark the military equipment as the monitored object, and obtain the usage duration SC, usage times CS, and storage quantity SL of the monitored object; perform a numerical calculation on the usage duration SC, usage times CS, and storage quantity SL to obtain the risk coefficient FX, and mark the monitored object as an elastic object or a rigid object by judging the risk coefficient FX;

[0022] Step 2: Mark the functional modules of the monitored object as monitoring points \(n\), where \(i = 1, 2, \ldots, i\) and \(i\) is a positive integer; Install various sensors distributively at the monitoring point \(n\) to obtain the monitoring parameters \(m\) of the monitoring point \(n\) in real time, where \(m = 1, 2, \ldots, j\) and \(j\) is a positive integer;

[0023] Step 3: Obtain the standard range \([MIN_m, MAX_m]\) corresponding to the monitoring parameter \(m\), mark that the monitoring parameter \(m\) exceeds the corresponding standard range \([MIN_m, MAX_m]\) as a parameter anomaly, and mark the number of times of parameter anomaly occurrence at the monitoring point \(n\) as the anomaly value \(YC_n\); Analyze the anomaly value \(YC_n\) of the monitoring point \(n\) in the rigid object and the elastic object to generate an alarm signal or a fusion analysis signal;

[0024] Step 4: Mark the monitoring parameter \(m\) with parameter anomaly occurrence at the monitoring point \(n\) as an abnormal parameter, and mark the monitoring parameter \(m\) other than the abnormal parameter at the monitoring point \(n\) as a reference parameter; Generate an alarm signal or an observation signal by analyzing the reference parameter.

[0025] Furthermore, the present invention provides a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements a military equipment monitoring method based on edge computing data processing.

[0026] Furthermore, the present invention also provides a computer device, including: a processor and a memory; The memory stores a computer program, and the processor executes the computer program stored in the memory so that the computer device executes a military equipment monitoring method based on edge computing data processing.

[0027] The present invention has the following beneficial effects:

[0028] 1. By grading through the comprehensive analysis of the usage duration, usage times, and storage quantity of military equipment by the monitoring grading module, it is possible to accurately distinguish equipment in different risk states, realize the reasonable allocation of resources and key attention, provide a basis for targeted monitoring schemes, and improve the monitoring efficiency;

[0029] 2. By using distributed sensors through the parameter monitoring module to monitor each functional module of military equipment in real time, the comprehensiveness and real-time nature of monitoring data are ensured, and potential problems can be discovered in a timely manner;

[0030] 3. Through the monitoring analysis module, it is possible to make targeted judgments on the abnormal conditions of the monitoring points according to the grading of military equipment, generate different alarm strategies, and also improve the accuracy of judging the state of military equipment;

[0031] 4. The fusion analysis module conducts in-depth analysis by combining multiple monitoring parameters, further improves the accuracy of judging the equipment state, and can also discover potential risks that may exceed the standard range in advance. Brief Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other accompanying drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is the system block diagram of the first embodiment of the present invention;

[0034] Figure 2 It is the method flowchart of the second embodiment of the present invention. Detailed Embodiments

[0035] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0036] Embodiment 1: As Figure 1 shown, a military equipment monitoring system based on edge computing data processing includes a monitoring and analysis center, which is communicatively connected with a monitoring classification module, a parameter monitoring module, a monitoring analysis module, a fusion analysis module, and a storage module;

[0037] The monitoring classification module is used to classify and monitor military equipment: mark the military equipment as a monitoring object, and obtain the usage duration SC, the usage times CS, and the storage quantity SL of the monitoring object; the usage duration SC is the difference between the real-time time when the monitoring object is in use and the start time of service; the usage times CS is the number of complete out-and-in warehouse operations of the monitoring object within the current usage duration SC; the storage quantity SL is the actual storage quantity of the same type of monitoring object in the equipment warehouse; through the formula The risk coefficient FX of the monitored object is obtained through numerical calculation using the usage duration SC, the usage frequency CS, and the storage quantity SL, where k1, k2, and k3 are all proportionality coefficients, and k1 > k2 > k3 > 1; the risk threshold FXmax of the monitored object is obtained through the storage module, and the risk coefficient FX of the monitored object is compared with the preset risk threshold FXmax: if the risk coefficient FX is less than the risk threshold FXmax, it is determined that the risk status of the monitored object meets the requirements, and the monitored object is marked as an elastic object; if the risk coefficient FX is greater than or equal to the risk threshold FXmax, it is determined that the risk status of the monitored object does not meet the requirements, and the monitored object is marked as a rigid object; through the monitoring classification module, the comprehensive analysis of the usage duration, frequency, and storage quantity of military equipment is classified, which can accurately distinguish equipment with different risk statuses, realize the reasonable allocation of resources and key attention, provide a basis for targeted monitoring plans, and improve the monitoring efficiency.

[0038] The parameter monitoring module is used to monitor the monitoring parameters: the functional modules of the monitored object are marked as monitoring points n, i = 1, 2,..., i, where i is a positive integer; for example, the functional modules include the power system, the weapon system, the electronic control system, etc., where the power system includes the engine, the battery pack, etc., the weapon system includes the gun barrel, the loading device, etc., and the electronic control system includes the main control computer, the circuit board, etc.; various sensors are distributedly installed at the monitoring point n to obtain the monitoring parameters m of the monitoring point n in real time, where m = 1, 2,..., j, and j is a positive integer; all sensors are connected to the edge computing node through wireless communication, and the edge computing node is equipped with an independent processor and memory; for example, the monitoring parameters of the engine in an armored vehicle generally include parameters such as the surface temperature of the cylinder block, the pressure of the fuel pipeline, and the flow rate of the fuel pipeline; through the parameter monitoring module, the distributed sensors are used to monitor each functional module of the military equipment in real time, ensuring the comprehensiveness and real-time nature of the monitoring data and being able to detect potential problems in a timely manner.

[0039] The monitoring and analysis module is used to monitor and analyze the monitoring parameters of the classified military equipment: the standard range [MINm, MAXm] corresponding to the monitoring parameter m is obtained through the storage module; during the operation of the monitored object, if the monitoring parameter m of the monitoring point n exceeds the corresponding standard range [MINm, MAXm], it is marked as a parameter anomaly, and the number of times of parameter anomaly occurrence at the monitoring point n is marked as the anomaly value YCn; the anomaly value YCn of the monitoring point n in the monitored object is analyzed:

[0040] When the monitored object is a rigid object, if a parameter anomaly occurs at monitoring point n, obtain the anomaly value YCn corresponding to monitoring point n. If the anomaly value YCn of monitoring point n is equal to 0, it is determined that this monitoring point n has an initial parameter anomaly, generate a fusion analysis signal and send the signal to the fusion analysis module; if the anomaly value YCn of monitoring point n is greater than or equal to 1, it is determined that this monitoring point n does not have an initial parameter anomaly, generate an alarm signal and send the monitored object and monitoring point n to the monitoring analysis center;

[0041] When the monitored object is an elastic object, obtain the anomaly threshold YCmax of monitoring point n through the storage module. If a parameter anomaly occurs at monitoring point n, obtain the anomaly value YCn corresponding to monitoring point n, and compare the anomaly value YCn with the anomaly threshold YCmax: if the anomaly value YCn of monitoring point n is less than or equal to the anomaly threshold YCmax, generate a fusion analysis signal and send the signal to the fusion analysis module; if the anomaly value YCn of monitoring point n is greater than the anomaly threshold YCmax, generate an alarm signal and send the monitored object and monitoring point n to the monitoring analysis center; through the monitoring analysis module, it is possible to make targeted judgments on the anomaly conditions of monitoring points according to the classification of military industrial equipment, generate different alarm strategies, and also improve the accuracy of judging the status of military industrial equipment.

[0042] The fusion analysis module is used to perform fusion analysis on military industrial equipment by combining multiple monitoring parameters: if the fusion analysis module receives a fusion analysis signal, it performs fusion analysis on the monitoring parameter m of monitoring point n: calculate the standard range [MINm, MAXm] of the monitoring parameter m and the preset boundary parameter t: perform division calculation on MINm and the boundary parameter t to obtain the lower boundary XBm, and perform multiplication calculation on MAXm and the boundary parameter t to obtain the upper boundary SBm, where the value of t is 0.9;

[0043] Mark the monitoring parameter m with parameter anomaly at the monitoring point n as an abnormal parameter, and mark the monitoring parameter m other than the abnormal parameter at the monitoring point n as a reference parameter; mark the period from L1 seconds before the occurrence of the parameter anomaly to the period when the parameter anomaly occurs as the analysis period, establish a rectangular coordinate system with the reference parameter as the Y-axis of the coordinate system and time as the X-axis of the coordinate system, and draw a reference parameter-time curve graph; equally divide the reference parameter-time curve graph along the X-axis direction to obtain several analysis points p, then the slope Kp between the analysis point p and its previous analysis point is Kp = (Yp - Yp-1) / (Xp - Xp-1); analyze all the analysis points p: if there exists an analysis point p where the corresponding reference parameter is greater than or equal to the upper boundary SBm and (kp * kp-1) is greater than 0, then it is judged that the monitoring parameter m is continuously increasing and there is a risk of exceeding the standard range [MINm, MAXm], generate an alarm signal and send the monitoring object and the monitoring point n to the monitoring analysis center; if there exists an analysis point p where the corresponding reference parameter is less than the lower boundary XBm and (kp * kp-1) is greater than 0, then it is judged that the monitoring parameter m is continuously decreasing and there is a risk of exceeding the standard range [MINm, MAXm], generate an alarm signal and send the monitoring object and the monitoring point n to the monitoring analysis center; otherwise, generate an observation signal and send the monitoring object and the monitoring point n to the monitoring analysis center; the fusion analysis module combines multiple monitoring parameters for in-depth analysis, further improving the accuracy of the judgment of the equipment status and being able to detect in advance the risks that may exceed the standard range.

[0044] Embodiment 2: As Figure 2 shown, a military equipment monitoring method based on edge computing data processing includes the following steps:

[0045] Step 1: Mark the military equipment as the monitoring object, and obtain the usage duration SC, the usage times CS, and the storage quantity SL of the monitoring object; perform numerical calculations on the usage duration SC, the usage times CS, and the storage quantity SL to obtain the risk coefficient FX, and mark the monitoring object as an elastic object or a rigid object by judging the risk coefficient FX;

[0046] Step 2: Mark the functional modules of the monitoring object as the monitoring points n, i = 1, 2,..., i, where i is a positive integer; install various sensors distributedly at the monitoring point n to obtain the monitoring parameters m of the monitoring point n in real time, where m = 1, 2,..., j, and j is a positive integer;

[0047] Step 3: Obtain the standard range [MINm, MAXm] corresponding to the monitoring parameter m, mark the monitoring parameter m exceeding the corresponding standard range [MINm, MAXm] as a parameter anomaly, and mark the number of times of parameter anomaly occurring at the monitoring point n as the anomaly value YCn; analyze the anomaly value YCn of the monitoring point n in the rigid object and the elastic object to generate an alarm signal or a fusion analysis signal;

[0048] Step 4: Mark the monitoring parameter m with abnormal parameters in the monitoring point n as abnormal parameters, and mark the monitoring parameter m other than the abnormal parameters in the monitoring point n as reference parameters; generate an alarm signal or an observation signal by analyzing the reference parameters.

[0049] The present invention also includes a readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned military equipment monitoring method based on edge computing data processing. Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to the computer program. The foregoing computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the foregoing storage medium includes: ROM, RAM, magnetic disk or optical disc and other various media that can store program codes.

[0050] The terminal of the present invention includes: a processor and a memory; the memory is used to store a computer program; the processor is connected to the memory and is used to execute the computer program stored in the memory, so that the terminal executes any military equipment monitoring method based on edge computing data processing. Specifically, the memory includes: ROM, RAM, magnetic disk, USB flash drive, memory card or optical disc and other various media that can store program codes.

[0051] Preferably, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; it may also be a digital signal processor (Digital Signal Processor, abbreviated as DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0052] Military equipment monitoring system based on edge computing data processing. During operation, military equipment is marked as a monitoring object, and the risk coefficient of the monitoring object is obtained. The monitoring object is marked as an elastic object or a rigid object by judging the risk coefficient. The functional modules of the monitoring object are marked as monitoring points, and various sensors are installed distributively at the monitoring points to obtain the monitoring parameters of the monitoring points in real time. The standard range corresponding to the monitoring parameters is obtained, and the situation where the monitoring parameters exceed the corresponding standard range is marked as parameter abnormality. The number of times of parameter abnormality occurring at the monitoring point is marked as the abnormal value. The abnormal values of the monitoring points in the rigid object and the elastic object are analyzed to generate an alarm signal or a fusion analysis signal. The monitoring parameters with parameter abnormality occurring at the monitoring point are marked as abnormal parameters, and the monitoring parameters other than the abnormal parameters at the monitoring point are marked as reference parameters. Finally, the reference parameters are analyzed to generate an alarm signal or an observation signal.

[0053] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

[0054] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. For example, the formula Those skilled in the art collect multiple groups of sample data and set corresponding risk coefficients for each group of sample data. The set risk coefficients and the collected sample data are substituted into the formula. Any three formulas form a system of linear equations with three variables. The calculated coefficients are screened and the average value is taken to obtain the values of k1, k2, and k3 as 3.12, 2.05, and 1.65 respectively.

[0055] The magnitude of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the risk coefficients initially set by those skilled in the art for each group of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, for example, the risk coefficient is proportional to the value of the usage duration.

[0056] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0057] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, according to the content of this specification, many modifications and variations can be made. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. Military equipment monitoring system based on edge computing data processing, characterized by: It includes a monitoring and analysis center, which is communicatively connected to a monitoring classification module, a parameter monitoring module, a monitoring and analysis module, a fusion analysis module and a storage module; The monitoring classification module is used to perform hierarchical monitoring on military equipment: marking the military equipment as a monitoring object, monitoring the risk coefficient FX of the monitoring object, comparing the risk coefficient FX of the monitoring object with a preset risk threshold FXmax, and marking the monitoring object as an elastic object or a rigid object; The parameter monitoring module is used to monitor the monitoring parameters: the functional module of the monitored object is marked as monitoring point n, i=1, 2, ..., i, i is a positive integer; various sensors are distributedly installed at the monitoring point n to obtain the monitoring parameter m of the monitoring point n in real time, where m=1, 2, ..., j, j is a positive integer; The monitoring and analysis module is used to monitor and analyze the monitoring parameters of the classified military equipment: obtain the standard range [MINm, MAXm] corresponding to the monitoring parameter m, mark the monitoring parameter m exceeding the corresponding standard range [MINm, MAXm] as parameter abnormality, and mark the number of times the parameter abnormality occurs at the monitoring point n as an abnormal value YCn; analyze the abnormal value YCn of the monitoring point n in the rigid object and the elastic object, and generate an alarm signal or a fusion analysis signal; The fusion analysis module is used to perform fusion analysis on military equipment in combination with multiple monitoring parameters: marking the monitoring parameter m with parameter abnormality in the monitoring point n as an abnormal parameter, marking the monitoring parameter m other than the abnormal parameter in the monitoring point n as a reference parameter, and analyzing the reference parameter to generate an alarm signal or an observation signal.

2. The military equipment monitoring system based on edge computing data processing according to claim 1 is characterized in that: The process of obtaining the risk coefficient FX includes: obtaining the usage time SC, usage times CS and storage quantity SL of the monitored object; the usage time SC is the difference between the real time when the monitored object is in use and the service start time; the usage times CS is the number of times the monitored object is completely out of storage and in storage within the current usage time SC; the storage quantity SL is the actual storage quantity of the same type of monitored objects in the equipment warehouse; the usage time SC, usage times CS and storage quantity SL are numerically calculated to obtain the risk coefficient FX of the monitored object.

3. The military equipment monitoring system based on edge computing data processing according to claim 2 is characterized in that: Compare the risk factor FX of the monitored object with the preset risk threshold FXmax: if the risk factor FX is less than the risk threshold FXmax, it is determined that the risk status of the monitored object meets the requirements, and the monitored object is marked as an elastic object; If the risk factor FX is greater than or equal to the risk threshold FXmax, it is determined that the risk status of the monitored object does not meet the requirements and the monitored object is marked as a rigid object.

4. The military equipment monitoring system based on edge computing data processing according to claim 3 is characterized in that: All sensors are connected to edge computing nodes through wireless communication, and the edge computing nodes are equipped with independent processors and memory.

5. The military equipment monitoring system based on edge computing data processing according to claim 4 is characterized in that: When the monitored object is a rigid object, if a parameter abnormality occurs at the monitoring point n, the abnormal value YCn corresponding to the monitoring point n is obtained. If the abnormal value YCn of the monitoring point n is equal to 0, it is judged that the parameter abnormality occurs at the monitoring point n for the first time, and a fusion analysis signal is generated and sent to the fusion analysis module; if the abnormal value YCn of the monitoring point n is greater than or equal to 1, it is judged that this is not the first time that the parameter abnormality occurs at the monitoring point n, an alarm signal is generated, and the monitored object and the monitoring point n are sent to the monitoring analysis center.

6. The military equipment monitoring system based on edge computing data processing according to claim 5 is characterized in that: When the monitored object is an elastic object, the abnormal threshold value YCmax of the monitoring point n is obtained through the storage module. If a parameter abnormality occurs at the monitoring point n, the abnormal value YCn corresponding to the monitoring point n is obtained, and the abnormal value YCn is compared with the abnormal threshold value YCmax: if the abnormal value YCn of the monitoring point n is less than or equal to the abnormal threshold value YCmax, a fusion analysis signal is generated and sent to the fusion analysis module; if the abnormal value YCn of the monitoring point n is greater than the abnormal threshold value YCmax, an alarm signal is generated and the monitored object and the monitoring point n are sent to the monitoring and analysis center.

7. The military equipment monitoring system based on edge computing data processing according to claim 6 is characterized in that: The standard range [MINm, MAXm] of the monitoring parameter m is calculated with the preset boundary parameter t: MINm is divided by the boundary parameter t to obtain the lower boundary XBm, and MAXm is multiplied by the boundary parameter t to obtain the upper boundary SBm.

8. The military equipment monitoring system based on edge computing data processing according to claim 7 is characterized in that: Mark the period from L1 seconds before the occurrence of the parameter abnormality to the occurrence of the parameter abnormality as the analysis period, establish a rectangular coordinate system with the reference parameter as the Y axis of the coordinate system and time as the X axis of the coordinate system, and draw a reference parameter-time curve; Take a number of analysis points p equally along the X-axis from the reference parameter-time curve, and calculate the slope kp between the analysis point p and the previous analysis point; If there is a reference parameter corresponding to the analysis point p that is greater than or equal to the upper boundary SBm, and (kp*kp-1) is greater than 0, then it is judged that the monitoring parameter m is continuously increasing and there is a risk of exceeding the standard range [MINm, MAXm], an alarm signal is generated and the monitored object and the monitoring point n are sent to the monitoring and analysis center; if there is a reference parameter corresponding to the analysis point p that is less than the lower boundary XBm, and (kp*kp-1) is greater than 0, then it is judged that the monitoring parameter m is continuously decreasing and there is a risk of exceeding the standard range [MINm, MAXm], an alarm signal is generated and the monitored object and the monitoring point n are sent to the monitoring and analysis center; otherwise, an observation signal is generated and the monitored object and the monitoring point n are sent to the monitoring and analysis center.

9. A method for monitoring military equipment based on edge computing data processing, characterized in that: The military equipment monitoring system based on edge computing data processing as described in any one of claims 1 to 8 comprises the following steps: Step 1: Mark the military equipment as a monitoring object, obtain the usage time SC, usage times CS and storage quantity SL of the monitoring object; perform numerical calculations on the usage time SC, usage times CS and storage quantity SL to obtain the risk coefficient FX, and mark the monitoring object as an elastic object or a rigid object by judging the risk coefficient FX; Step 2: Mark the functional modules of the monitored object as monitoring point n, i = 1, 2, ..., i, i is a positive integer; install various sensors at the monitoring point n in a distributed manner, and obtain the monitoring parameter m of the monitoring point n in real time, where m = 1, 2, ..., j, j is a positive integer; Step 3: Obtain the standard range [MINm, MAXm] corresponding to the monitoring parameter m, mark the monitoring parameter m exceeding the corresponding standard range [MINm, MAXm] as parameter abnormality, and mark the number of times the parameter abnormality occurs at the monitoring point n as the abnormal value YCn; analyze the abnormal value YCn of the monitoring point n in the rigid object and the elastic object, and generate an alarm signal or a fusion analysis signal; Step 4: Mark the monitoring parameter m with abnormal parameters in the monitoring point n as abnormal parameters, and mark the monitoring parameters m other than the abnormal parameters in the monitoring point n as reference parameters; generate an alarm signal or an observation signal by analyzing the reference parameters.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the military equipment monitoring method based on edge computing data processing as described in claim 9.

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