Flying spot generation method and system and medium

Abstract

The embodiment of the invention provides a flying spot generation method and system and a medium, and the method comprises the steps: obtaining monitoring data, extracting monitoring data features, and generating a plurality of monitoring point data features; comparing the monitoring point data characteristics with a set standard monitoring waveform to obtain a data deviation rate; judging whether the data deviation rate is greater than or equal to a set deviation rate threshold value or not; if yes, generating flying spot data, transmitting the flying spot data to the terminal in real time, and generating a monitoring time node of the flying spot data; if yes, fitting the monitoring data to generate a monitoring waveform; by analyzing and comparing the monitoring data and comparing the judged data characteristics with the standard monitoring waveform, the monitoring data deviating from the standard monitoring waveform are accurately obtained, the flying spot data are obtained, the monitoring data are accurately analyzed, and the analysis precision is improved.

Description

Technical Field

[0001] The present application relates to the technical field of flying spot detection, and in particular to a flying spot generation method, system and medium. Background Art

[0002] In recent years, China's e-commerce has developed rapidly, which is obvious to all. It has also promoted the rapid and efficient development of the logistics and express delivery industry. The combination of the Internet and express delivery has brought people a new and convenient life. In 2017, the total amount of express delivery in my country exceeded 40 billion, accounting for half of the global total. However, with the explosive development of the logistics and express delivery industry, and many express delivery companies have adopted a franchise-based business model, so companies often find it difficult to ensure that each outlet is watertight in receiving, inspecting, and distributing. The logistics and express delivery industry has increased supervision of parcels and the investigation of safety hazards is very urgent. As the country with the largest number of express parcels in the world today, fast, comprehensive and accurate security inspection technology for logistics and express delivery is of great significance. In the existing flying point generation method, it is difficult to analyze the monitoring data that deviates from the standard monitoring waveform through real-time analysis of monitoring data, resulting in analysis errors of flying point data, affecting the analysis accuracy of the data, and causing low analysis accuracy during data analysis. For the above problems, effective technical solutions are urgently needed. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a flying spot generation method, system and medium, which analyzes and compares the monitoring data, determines the data characteristics and compares them with the standard monitoring waveform, thereby accurately acquiring the monitoring data that deviates from the standard monitoring waveform, obtaining the flying spot data, accurately analyzing the monitoring data, and improving the analysis accuracy.

[0004] The present application also provides a flying point generation method, including:

[0005] Acquire monitoring data, extract monitoring data features, and generate data features of several monitoring points;

[0006] Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate;

[0007] Determining whether the data deviation rate is greater than or equal to a set deviation rate threshold;

[0008] If it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated;

[0009] If it is less than, the monitoring data will be fitted to generate a monitoring waveform.

[0010] Optionally, in the flying point generation method described in the embodiment of the present application, acquiring monitoring data, extracting monitoring data features, and generating a plurality of monitoring point data features specifically include:

[0011] Establishing a number of monitoring points, and obtaining monitoring data corresponding to a number of monitoring points based on the number of monitoring points;

[0012] Extract the data features of each monitoring point, compare the data features of several monitoring points with the set features, and obtain the feature deviation rate;

[0013] Determining whether the characteristic deviation rate is greater than or equal to a set characteristic deviation rate threshold;

[0014] If it is greater than or equal to, the corresponding data feature is eliminated;

[0015] If it is less than, then several monitoring point data features are obtained.

[0016] Optionally, in the flying spot generation method described in the embodiment of the present application, the data feature of the monitoring point is compared with a set standard monitoring waveform to obtain a data deviation rate, which specifically includes:

[0017] Obtain the data features of the monitoring points at different time nodes, compare the data features of the monitoring points with the monitoring data of the standard monitoring waveform at the corresponding time nodes, and calculate the Euclidean distance;

[0018] Compare the Euclidean distance with the set distance threshold to obtain the distance difference;

[0019] Generate data deviation information based on the distance difference;

[0020] The data deviation information of adjacent time nodes is compared to obtain the data deviation rate.

[0021] Optionally, in the flying point generation method described in the embodiment of the present application, if is greater than or equal to, then generating flying point data, transmitting the flying point data to the terminal in real time, and generating a monitoring time node for the flying point data, specifically including:

[0022] Acquire flying point data, generate monitoring time nodes of flying point data, and establish data pairs;

[0023] Establishing data encryption strategy information based on the data pair, encrypting the flying point data based on the data encryption strategy information to obtain ciphertext data;

[0024] Transmit the ciphertext data to the terminal in real time and generate the matching decryption key;

[0025] The decryption key is stored separately in the storage area.

[0026] Optionally, in the flying spot generation method described in the embodiment of the present application, if is less than , the monitoring data is fitted to generate a monitoring waveform, specifically including:

[0027] Obtain data features of different time nodes, fit the data features of adjacent time nodes, and obtain data curves of adjacent time nodes;

[0028] Divide a number of time nodes into a plurality of adjacent time nodes, and obtain data curves of the plurality of adjacent time nodes;

[0029] The data curves of multiple adjacent time nodes are fitted to generate monitoring waveforms.

[0030] Optionally, in the flying spot generation method described in the embodiment of the present application, fitting the monitoring data to generate a monitoring waveform further includes:

[0031] Establish corresponding sampling points based on several time nodes, and obtain monitoring data based on several sampling points;

[0032] Compare the monitoring data with the set monitoring information to obtain monitoring abnormality information;

[0033] Compare the monitored abnormal information with the set abnormal information to obtain the abnormal value;

[0034] Determine whether the abnormal value is greater than or equal to a set abnormal threshold;

[0035] If it is greater than or equal to, abnormal data is generated, and an abnormal waveform is generated based on the abnormal data;

[0036] If it is less than, normal data is obtained, and a normal waveform is generated based on the normal data;

[0037] Generate a monitoring waveform based on an abnormal waveform or a normal waveform.

[0038] In a second aspect, an embodiment of the present application provides a flying spot generation system, the system comprising: a memory and a processor, the memory comprising a program of a flying spot generation method, and the program of the flying spot generation method is executed by the processor to implement the following steps:

[0039] Acquire monitoring data, extract monitoring data features, and generate data features of several monitoring points;

[0040] Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate;

[0041] Determining whether the data deviation rate is greater than or equal to a set deviation rate threshold;

[0042] If it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated;

[0043] If it is less than, the monitoring data will be fitted to generate a monitoring waveform.

[0044] Optionally, in the flying point generation system described in the embodiment of the present application, acquiring monitoring data, extracting monitoring data features, and generating a plurality of monitoring point data features specifically include:

[0045] Establishing a number of monitoring points, and obtaining monitoring data corresponding to a number of monitoring points based on the number of monitoring points;

[0046] Extract the data features of each monitoring point, compare the data features of several monitoring points with the set features, and obtain the feature deviation rate;

[0047] Determining whether the characteristic deviation rate is greater than or equal to a set characteristic deviation rate threshold;

[0048] If it is greater than or equal to, the corresponding data feature is eliminated;

[0049] If it is less than, then several monitoring point data features are obtained.

[0050] Optionally, in the flying spot generation system described in the embodiment of the present application, the data feature of the monitoring point is compared with a set standard monitoring waveform to obtain a data deviation rate, which specifically includes:

[0051] Obtain the data features of the monitoring points at different time nodes, compare the data features of the monitoring points with the monitoring data of the standard monitoring waveform at the corresponding time nodes, and calculate the Euclidean distance;

[0052] Compare the Euclidean distance with the set distance threshold to obtain the distance difference;

[0053] Generate data deviation information based on the distance difference;

[0054] The data deviation information of adjacent time nodes is compared to obtain the data deviation rate.

[0055] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a flying spot generation method program, and when the flying spot generation method program is executed by a processor, the steps of the flying spot generation method as described in any one of the above items are implemented.

[0056] As can be seen from the above, a flying point generation method, system and medium provided in the embodiments of the present application obtain monitoring data, extract monitoring data features, and generate several monitoring point data features; compare the monitoring point data features with the set standard monitoring waveform to obtain the data deviation rate; determine whether the data deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generate flying point data, transmit the flying point data to the terminal in real time, and generate a monitoring time node for the flying point data; if less than, fit the monitoring data to generate a monitoring waveform; by analyzing and comparing the monitoring data, determine that the data features are compared with the standard monitoring waveform, thereby accurately obtaining the monitoring data that deviates from the standard monitoring waveform, obtaining the flying point data, accurately analyzing the monitoring data, and improving the analysis accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flowchart of a flying point generation method provided in an embodiment of the present application;

[0059] Figure 2 A flow chart of a method for acquiring monitoring data features of a flying spot generation method provided in an embodiment of the present application;

[0060] Figure 3 A flow chart of a method for obtaining a data deviation rate of a flying spot generation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0062] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0063] Please refer to Figure 1 , Figure 1 1 is a flow chart of a flying point generation method in some embodiments of the present application. The flying point generation method is used in a terminal device, and the flying point generation method includes the following steps:

[0064] S101, acquiring monitoring data, extracting monitoring data features, and generating data features of several monitoring points;

[0065] S102, comparing the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate;

[0066] S103, determining whether the data deviation rate is greater than or equal to a set deviation rate threshold;

[0067] S104, if it is greater than or equal to, generating flying point data, transmitting the flying point data to the terminal in real time, and generating a monitoring time node for the flying point data;

[0068] S105: If it is less than, the monitoring data is fitted to generate a monitoring waveform.

[0069] It should be noted that by analyzing the monitoring data of several monitoring points and judging the difference in data, the flying point data can be accurately obtained, and the flying point data and the corresponding monitoring time can be accurately obtained and transmitted to the terminal, thereby improving the analysis accuracy of the flying point data.

[0070] Please refer to Figure 2 , Figure 2 The following is a flow chart of a method for acquiring monitoring data features of a flying point generation method in some embodiments of the present application. According to an embodiment of the present invention, acquiring monitoring data, extracting monitoring data features, and generating several monitoring point data features specifically include:

[0071] S201, establishing a plurality of monitoring points, and acquiring monitoring data corresponding to the plurality of monitoring points based on the plurality of monitoring points;

[0072] S202, extracting data features of each monitoring point, comparing the data features of several monitoring points with the set features, and obtaining a feature deviation rate;

[0073] S203, determining whether the characteristic deviation rate is greater than or equal to a set characteristic deviation rate threshold;

[0074] S204, if greater than or equal to, remove the corresponding data feature;

[0075] S205: If it is less than, then obtain data features of several monitoring points.

[0076] It should be noted that by analyzing the data characteristics of the monitoring points, data with large characteristic deviation rates can be eliminated, the accuracy of data analysis can be improved, and the accuracy of monitoring data can be ensured.

[0077] Please refer to Figure 3 , Figure 3 The following is a flow chart of a method for obtaining a data deviation rate of a flying point generation method in some embodiments of the present application. According to an embodiment of the present invention, the data characteristics of the monitoring point are compared with the set standard monitoring waveform to obtain the data deviation rate, which specifically includes:

[0078] S301, obtaining data features of monitoring points at different time nodes, comparing the data features of the monitoring points with monitoring data of standard monitoring waveforms at corresponding time nodes, and calculating Euclidean distance;

[0079] S302, comparing the Euclidean distance with a set distance threshold to obtain a distance difference;

[0080] S303, generating data deviation information based on the distance difference;

[0081] S304: Compare the data deviation information of adjacent time nodes to obtain a data deviation rate.

[0082] It should be noted that by analyzing the data characteristics of the monitoring points at different time nodes, the data difference between the monitoring data and the standard monitoring waveform is determined to obtain different data deviation rates.

[0083] According to an embodiment of the present invention, if it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated, specifically including:

[0084] Acquire flying point data, generate monitoring time nodes of flying point data, and establish data pairs;

[0085] Establishing data encryption strategy information based on the data pair, encrypting the flying point data based on the data encryption strategy information to obtain ciphertext data;

[0086] Transmit the ciphertext data to the terminal in real time and generate the matching decryption key;

[0087] The decryption key is stored separately in the storage area.

[0088] It should be noted that the flying point data is obtained, and different flying point data are encrypted with different strategies to improve the encryption security. In addition, the encrypted ciphertext data is transmitted to the terminal to obtain a matching decryption key. When the ciphertext data is retrieved, the ciphertext data can be accurately decrypted to ensure the accuracy of data encryption.

[0089] According to an embodiment of the present invention, if is less than , the monitoring data is fitted to generate a monitoring waveform, which specifically includes:

[0090] Obtain data features of different time nodes, fit the data features of adjacent time nodes, and obtain data curves of adjacent time nodes;

[0091] Divide a number of time nodes into a plurality of adjacent time nodes, and obtain data curves of the plurality of adjacent time nodes;

[0092] The data curves of multiple adjacent time nodes are fitted to generate monitoring waveforms.

[0093] It should be noted that by analyzing the data characteristics of different time nodes and performing fitting, the data of adjacent time nodes can be fitted to accurately obtain the monitoring waveform and improve the analysis accuracy of the monitoring waveform.

[0094] According to an embodiment of the present invention, fitting the monitoring data to generate a monitoring waveform also includes:

[0095] Establish corresponding sampling points based on several time nodes, and obtain monitoring data based on several sampling points;

[0096] Compare the monitoring data with the set monitoring information to obtain monitoring abnormality information;

[0097] Compare the monitored abnormal information with the set abnormal information to obtain the abnormal value;

[0098] Determine whether the abnormal value is greater than or equal to the set abnormal threshold;

[0099] If it is greater than or equal to, abnormal data is generated, and an abnormal waveform is generated based on the abnormal data;

[0100] If it is less than, normal data is obtained, and a normal waveform is generated based on the normal data;

[0101] Generate a monitoring waveform based on an abnormal waveform or a normal waveform.

[0102] It should be noted that by analyzing the monitoring data to obtain abnormal monitoring information, the abnormal situation of the data is analyzed, the abnormal waveform is obtained, and the detection waveform is accurately obtained, thereby improving the monitoring accuracy of the monitoring data.

[0103] According to an embodiment of the present invention, the method further includes: setting a plurality of sampling points, and acquiring monitoring data of corresponding sampling points based on the plurality of sampling points;

[0104] Generate monitoring waveform based on monitoring data fitting of several sampling points;

[0105] Calculate the monitoring amplitude of each sampling point based on the monitoring waveform;

[0106] Two sampling points are set as a group, and the monitored amplitudes of the two sampling points in one group are compared to obtain the amplitude difference;

[0107] Determine whether the amplitude difference is greater than or equal to the set difference;

[0108] If it is greater than or equal to, it is determined that there is flying point data in the current group, and the monitoring data of adjacent groups are compared to obtain the flying point data;

[0109] If it is less than, the data in the current group is considered normal.

[0110] It should be noted that by dividing the sampling points into groups, multiple monitoring groups are obtained. By analyzing two sampling points in a monitoring group and analyzing the monitoring data of adjacent monitoring groups at the same time, the flying point data can be accurately obtained.

[0111] In a second aspect, an embodiment of the present application provides a flying spot generation system, the system comprising: a memory and a processor, the memory comprising a program of a flying spot generation method, and when the program of the flying spot generation method is executed by the processor, the following steps are implemented:

[0112] Acquire monitoring data, extract monitoring data features, and generate data features of several monitoring points;

[0113] Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate;

[0114] Determine whether the data deviation rate is greater than or equal to a set deviation rate threshold;

[0115] If it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated;

[0116] If it is less than, the monitoring data will be fitted to generate a monitoring waveform.

[0117] It should be noted that by analyzing the monitoring data of several monitoring points and judging the difference in data, the flying point data can be accurately obtained, and the flying point data and the corresponding monitoring time can be accurately obtained and transmitted to the terminal, thereby improving the analysis accuracy of the flying point data.

[0118] According to an embodiment of the present invention, monitoring data is acquired, monitoring data features are extracted, and several monitoring point data features are generated, specifically including:

[0119] Establishing a number of monitoring points, and obtaining monitoring data corresponding to a number of monitoring points based on the number of monitoring points;

[0120] Extract the data features of each monitoring point, compare the data features of several monitoring points with the set features, and obtain the feature deviation rate;

[0121] Determine whether the feature deviation rate is greater than or equal to a set feature deviation rate threshold;

[0122] If it is greater than or equal to, the corresponding data feature is eliminated;

[0123] If it is less than, then several monitoring point data features are obtained.

[0124] It should be noted that by analyzing the data characteristics of the monitoring points, data with large characteristic deviation rates can be eliminated, the accuracy of data analysis can be improved, and the accuracy of monitoring data can be ensured.

[0125] According to an embodiment of the present invention, the data characteristics of the monitoring point are compared with the set standard monitoring waveform to obtain the data deviation rate, which specifically includes:

[0126] Obtain the data features of the monitoring points at different time nodes, compare the data features of the monitoring points with the monitoring data of the standard monitoring waveform at the corresponding time nodes, and calculate the Euclidean distance;

[0127] Compare the Euclidean distance with the set distance threshold to obtain the distance difference;

[0128] Generate data deviation information based on the distance difference;

[0129] The data deviation information of adjacent time nodes is compared to obtain the data deviation rate.

[0130] It should be noted that by analyzing the data characteristics of the monitoring points at different time nodes, the data difference between the monitoring data and the standard monitoring waveform is determined to obtain different data deviation rates.

[0131] According to an embodiment of the present invention, if it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated, specifically including:

[0132] Acquire flying point data, generate monitoring time nodes of flying point data, and establish data pairs;

[0133] Establishing data encryption strategy information based on the data pair, encrypting the flying point data based on the data encryption strategy information to obtain ciphertext data;

[0134] Transmit the ciphertext data to the terminal in real time and generate the matching decryption key;

[0135] The decryption key is stored separately in the storage area.

[0136] It should be noted that the flying point data is obtained, and different flying point data are encrypted with different strategies to improve the encryption security. In addition, the encrypted ciphertext data is transmitted to the terminal to obtain a matching decryption key. When the ciphertext data is retrieved, the ciphertext data can be accurately decrypted to ensure the accuracy of data encryption.

[0137] According to an embodiment of the present invention, if is less than , the monitoring data is fitted to generate a monitoring waveform, which specifically includes:

[0138] Obtain data features of different time nodes, fit the data features of adjacent time nodes, and obtain data curves of adjacent time nodes;

[0139] Divide a number of time nodes into a plurality of adjacent time nodes, and obtain data curves of the plurality of adjacent time nodes;

[0140] The data curves of multiple adjacent time nodes are fitted to generate monitoring waveforms.

[0141] It should be noted that by analyzing the data characteristics of different time nodes and performing fitting, the data of adjacent time nodes can be fitted to accurately obtain the monitoring waveform and improve the analysis accuracy of the monitoring waveform.

[0142] According to an embodiment of the present invention, fitting the monitoring data to generate a monitoring waveform also includes:

[0143] Establish corresponding sampling points based on several time nodes, and obtain monitoring data based on several sampling points;

[0144] Compare the monitoring data with the set monitoring information to obtain monitoring abnormality information;

[0145] Compare the monitored abnormal information with the set abnormal information to obtain the abnormal value;

[0146] Determine whether the abnormal value is greater than or equal to the set abnormal threshold;

[0147] If it is greater than or equal to, abnormal data is generated, and an abnormal waveform is generated based on the abnormal data;

[0148] If it is less than, normal data is obtained, and a normal waveform is generated based on the normal data;

[0149] Generate a monitoring waveform based on an abnormal waveform or a normal waveform.

[0150] It should be noted that by analyzing the monitoring data to obtain abnormal monitoring information, the abnormal situation of the data is analyzed, the abnormal waveform is obtained, and the detection waveform is accurately obtained, thereby improving the monitoring accuracy of the monitoring data.

[0151] According to an embodiment of the present invention, the method further includes: setting a plurality of sampling points, and acquiring monitoring data of corresponding sampling points based on the plurality of sampling points;

[0152] Generate monitoring waveform based on monitoring data fitting of several sampling points;

[0153] Calculate the monitoring amplitude of each sampling point based on the monitoring waveform;

[0154] Two sampling points are set as a group, and the monitored amplitudes of the two sampling points in one group are compared to obtain the amplitude difference;

[0155] Determine whether the amplitude difference is greater than or equal to the set difference;

[0156] If it is greater than or equal to, it is determined that there is flying point data in the current group, and the monitoring data of adjacent groups are compared to obtain the flying point data;

[0157] If it is less than, the data in the current group is considered normal.

[0158] It should be noted that by dividing the sampling points into groups, multiple monitoring groups are obtained. By analyzing two sampling points in a monitoring group and analyzing the monitoring data of adjacent monitoring groups at the same time, the flying point data can be accurately obtained.

[0159] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a flying spot generation method program. When the flying spot generation method program is executed by a processor, the steps of any of the above-mentioned flying spot generation methods are implemented.

[0160] The present invention discloses a flying point generation method, system and medium. The method comprises the following steps: acquiring monitoring data, extracting monitoring data features and generating a plurality of monitoring point data features; comparing the monitoring point data features with a set standard monitoring waveform to obtain a data deviation rate; judging whether the data deviation rate is greater than or equal to a set deviation rate threshold; if greater than or equal to a set deviation rate threshold, generating flying point data, transmitting the flying point data to a terminal in real time, and generating a monitoring time node for the flying point data; if less than a set deviation rate threshold, fitting the monitoring data and generating a monitoring waveform; and comparing the monitoring data, judging that the data features are compared with the standard monitoring waveform, thereby accurately acquiring monitoring data deviating from the standard monitoring waveform, obtaining flying point data, accurately analyzing the monitoring data and improving the analysis accuracy.

[0161] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0162] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0163] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0164] Those skilled 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 program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0165] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A flying spot generation method, characterized in that: include: Acquire monitoring data, extract monitoring data features, and generate data features of several monitoring points; Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate; Determining whether the data deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated; If it is less than, the monitoring data will be fitted to generate a monitoring waveform.

2. The flying point generation method according to claim 1, characterized in that: Acquire monitoring data, extract monitoring data features, and generate several monitoring point data features, including: Establishing a number of monitoring points, and obtaining monitoring data corresponding to a number of monitoring points based on the number of monitoring points; Extract the data features of each monitoring point, compare the data features of several monitoring points with the set features, and obtain the feature deviation rate; Determining whether the characteristic deviation rate is greater than or equal to a set characteristic deviation rate threshold; If it is greater than or equal to, the corresponding data feature is eliminated; If it is less than, then several monitoring point data features are obtained.

3. The flying point generation method according to claim 2, characterized in that: Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate, including: Obtain the data features of the monitoring points at different time nodes, compare the data features of the monitoring points with the monitoring data of the standard monitoring waveform at the corresponding time nodes, and calculate the Euclidean distance; Compare the Euclidean distance with the set distance threshold to obtain the distance difference; Generate data deviation information based on the distance difference; The data deviation information of adjacent time nodes is compared to obtain the data deviation rate.

4. The flying point generation method according to claim 3, characterized in that: If it is greater than or equal to, the flying point data is generated, the flying point data is transmitted to the terminal in real time, and the monitoring time node of the flying point data is generated, specifically including: Acquire flying point data, generate monitoring time nodes of flying point data, and establish data pairs; Establishing data encryption strategy information based on the data pair, encrypting the flying point data based on the data encryption strategy information to obtain ciphertext data; Transmit the ciphertext data to the terminal in real time and generate the matching decryption key; The decryption key is stored separately in the storage area.

5. The flying point generation method according to claim 4, characterized in that: If it is less than, the monitoring data is fitted to generate a monitoring waveform, including: Obtain data features of different time nodes, fit the data features of adjacent time nodes, and obtain data curves of adjacent time nodes; Divide a number of time nodes into a plurality of adjacent time nodes, and obtain data curves of the plurality of adjacent time nodes; The data curves of multiple adjacent time nodes are fitted to generate monitoring waveforms.

6. The flying point generation method according to claim 4, characterized in that: Fitting the monitoring data to generate monitoring waveforms also includes: Establish corresponding sampling points based on several time nodes, and obtain monitoring data based on several sampling points; Compare the monitoring data with the set monitoring information to obtain monitoring abnormality information; Compare the monitored abnormal information with the set abnormal information to obtain the abnormal value; Determine whether the abnormal value is greater than or equal to a set abnormal threshold; If it is greater than or equal to, abnormal data is generated, and an abnormal waveform is generated based on the abnormal data; If it is less than, normal data is obtained, and a normal waveform is generated based on the normal data; Generate a monitoring waveform based on an abnormal waveform or a normal waveform.

7. A flying point generation system, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises a program of a flying point generation method, and when the program of the flying point generation method is executed by the processor, the following steps are implemented: Acquire monitoring data, extract monitoring data features, and generate data features of several monitoring points; Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate; Determining whether the data deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, then the flying point data is generated, the flying point data is transmitted to the terminal in real time, and a monitoring time node of the flying point data is generated; If it is less than, the monitoring data will be fitted to generate a monitoring waveform.

8. The flying point generation system according to claim 7, characterized in that: Acquire monitoring data, extract monitoring data features, and generate several monitoring point data features, including: Establishing a number of monitoring points, and obtaining monitoring data corresponding to a number of monitoring points based on the number of monitoring points; Extract the data features of each monitoring point, compare the data features of several monitoring points with the set features, and obtain the feature deviation rate; Determining whether the characteristic deviation rate is greater than or equal to a set characteristic deviation rate threshold; If it is greater than or equal to, the corresponding data feature is eliminated; If it is less than, then several monitoring point data features are obtained.

9. The flying point generation system according to claim 8, characterized in that: Compare the data characteristics of the monitoring point with the set standard monitoring waveform to obtain the data deviation rate, including: Obtain the data features of the monitoring points at different time nodes, compare the data features of the monitoring points with the monitoring data of the standard monitoring waveform at the corresponding time nodes, and calculate the Euclidean distance; Compare the Euclidean distance with the set distance threshold to obtain the distance difference; Generate data deviation information based on the distance difference; The data deviation information of adjacent time nodes is compared to obtain the data deviation rate.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a flying spot generation method program, and when the flying spot generation method program is executed by a processor, the steps of the flying spot generation method according to any one of claims 1 to 6 are implemented.

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