Privacy calculation method and system for equipment maintenance data
By mapping and similarity calculations of the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus, accurately locate and desensitize the privacy data, the problem of privacy data leakage in the prior art is solved and data security is achieved.
Patent Information
- Application Number
- CN202410787744.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-18
- Publication Date
- 2025-03-04
AI Technical Summary
In the prior art, it is difficult to accurately locate the privacy data in the equipment maintenance data, resulting in the problem of easy leakage of privacy data.
By obtaining the word feature vectors in the equipment maintenance data and mapping them with the word feature vectors in the preset privacy corpus, the similarity is calculated using the feature vector similarity model, and data exceeding the threshold is defined as privacy data for desensitization.
It can accurately locate the privacy data in the equipment maintenance data, and ensure its security through desensitization processing to avoid privacy data leakage.
Smart Images
Figure CN119557912A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of data privacy processing, and more specifically, relates to a privacy calculation method and system for equipment maintenance data. Background Art
[0002] Equipment maintenance data typically includes a variety of information related to equipment status, operation, and maintenance. The following are some common items that may be included in this type of data:
[0003] Device information: includes basic information such as the device’s unique identifier, model, manufacturer, and installation location.
[0004] Sensor data: Data collected by various sensors on the device, such as temperature, humidity, pressure, vibration, etc.
[0005] Equipment status: record the operating status of the equipment, such as operating time, downtime, fault status, etc.
[0006] Maintenance record: records the maintenance history of the equipment, including maintenance date, maintenance type, maintenance personnel, maintenance consumables and other information.
[0007] Alarm information: Alarm or fault information that may be generated by the device, such as abnormal events, alarm codes, alarm levels, etc.
[0008] Operation log: equipment operation records, such as equipment start, stop, parameter adjustment and other operation information.
[0009] Environmental data: data related to the environment in which the device is located, such as ambient temperature, humidity, air pressure and other environmental information.
[0010] Production data: production data related to the equipment, such as production quantity, quality indicators, etc.
[0011] This data is usually collected in real time through IoT devices and may be stored, analyzed, and used for predictive maintenance, fault diagnosis, efficiency optimization, etc. When processing this data, it is critical to protect data privacy and security.
[0012] However, there is no technical solution in the prior art that can accurately locate the privacy data in the equipment maintenance data, and the privacy data is prone to leakage. Summary of the invention
[0013] In order to solve the above technical problems, the present invention proposes a privacy calculation method for equipment maintenance data, which is used for privacy calculation of equipment maintenance data when repairing mechanical equipment, and is characterized by comprising:
[0014] Acquire word feature vectors in the equipment maintenance data, set a preset privacy corpus, and map the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0015] Setting a feature vector similarity model to calculate the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0016] The word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold are defined as privacy data and desensitized.
[0017] Furthermore, the feature vector similarity model includes:
[0018]
[0019] Among them, S is the similarity between word feature vector A and word feature vector B, a is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0020] Furthermore, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0021]
[0022] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0023] Furthermore, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0024]
[0025] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0026] Furthermore, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0027]
[0028] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0029] The present invention also proposes a privacy computing system for equipment maintenance data, which is used for privacy computing of equipment maintenance data when repairing mechanical equipment, and includes:
[0030] A vector extraction module, used to obtain word feature vectors in the equipment maintenance data, and set a preset privacy corpus, and map the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0031] A similarity calculation module, used to set a feature vector similarity model, and calculate the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0032] The desensitization module is used to define the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data and perform desensitization processing.
[0033] Furthermore, the feature vector similarity model includes:
[0034]
[0035] Among them, S is the similarity between word feature vector A and word feature vector B, a is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0036] Furthermore, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0037]
[0038] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0039] Furthermore, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0040]
[0041] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0042] Furthermore, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0043]
[0044] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0045] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:
[0046] The present invention obtains word feature vectors in the equipment maintenance data, and sets a preset privacy corpus, maps the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function; sets a feature vector similarity model, calculates the similarity between the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus in the feature vector space; defines the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data, and performs desensitization processing. Through the above technical scheme, the present invention can determine the privacy data in the equipment maintenance data according to the similarity between the preset privacy corpus and the equipment maintenance data, thereby performing desensitization processing and ensuring the security of the privacy data. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;
[0048] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION
[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.
[0050] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.
[0051] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.
[0052] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.
[0053] The display is used to show the interactive cross-section of each application.
[0054] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.
[0055] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.
[0056] Example 1
[0057] like Figure 1 As shown, an embodiment of the present invention provides a privacy calculation method for equipment maintenance data, which is used for privacy calculation of equipment maintenance data when repairing mechanical equipment, including:
[0058] Step 101, obtaining word feature vectors in the equipment maintenance data, and setting a preset privacy corpus, and mapping the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0059] Specifically, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0060]
[0061] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0062] Specifically, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0063]
[0064] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0065] Step 102, setting a feature vector similarity model, and calculating the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0066] Specifically, the feature vector similarity model includes:
[0067]
[0068] Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0069] Specifically, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0070]
[0071] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0072] Step 103 , defining the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data, and performing desensitization processing.
[0073] Example 2
[0074] like Figure 2 As shown, the embodiment of the present invention further proposes a privacy computing system for equipment maintenance data, which is used for privacy computing of equipment maintenance data when repairing mechanical equipment, including:
[0075] A vector extraction module, used to obtain word feature vectors in the equipment maintenance data, and set a preset privacy corpus, and map the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0076] Specifically, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0077]
[0078] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0079] Specifically, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0080]
[0081] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0082] A similarity calculation module, used to set a feature vector similarity model, and calculate the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0083] Specifically, the feature vector similarity model includes:
[0084]
[0085] Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0086] Specifically, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0087]
[0088] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0089] The desensitization module is used to define the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data and perform desensitization processing.
[0090] Example 3
[0091] An embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the privacy computing method for equipment maintenance data.
[0092] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.
[0093] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, obtaining word feature vectors in the equipment maintenance data, and setting a preset privacy corpus, mapping the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0094] Specifically, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0095]
[0096] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0097] Specifically, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0098]
[0099] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0100] Step 102, setting a feature vector similarity model, and calculating the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0101] Specifically, the feature vector similarity model includes:
[0102]
[0103] Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0104] Specifically, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0105]
[0106] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0107] Step 103 , defining the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data, and performing desensitization processing.
[0108] Example 4
[0109] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor to enable the processor to perform a privacy computing method for equipment maintenance data.
[0110] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
[0111] Among them, the storage medium can be used to store software programs and modules, such as a privacy computing method for equipment maintenance data in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned privacy computing method for equipment maintenance data. The storage medium may include high-speed random storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely located relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
[0112] The processor may call the information and application stored in the storage medium through the transmission system to execute the following steps: Step 101, obtaining the word feature vector in the equipment maintenance data, and setting a preset privacy corpus, and mapping the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus to a feature vector space through a feature vector mapping function;
[0113] Specifically, the feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes:
[0114]
[0115] Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
[0116] Specifically, the feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes:
[0117]
[0118] Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
[0119] Step 102, setting a feature vector similarity model, and calculating the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space;
[0120] Specifically, the feature vector similarity model includes:
[0121]
[0122] Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
[0123] Specifically, the function n(f(A)-f(B)) for enhancing the ability to express the similarity between the word feature vector A and the word feature vector B includes:
[0124]
[0125] Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
[0126] Step 103 , defining the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data, and performing desensitization processing.
[0127] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.
[0128] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0129] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.
[0130] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, 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 solution of this embodiment.
[0131] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.
[0133] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.
Claims
1. A privacy calculation method for equipment maintenance data, which is used for privacy calculation of equipment maintenance data when repairing mechanical equipment, and is characterized by: include: Acquire word feature vectors in the equipment maintenance data, set a preset privacy corpus, and map the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function; Setting a feature vector similarity model to calculate the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space; The word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold are defined as privacy data and desensitized.
2. A privacy computing method for equipment maintenance data as claimed in claim 1, characterized in that: The feature vector similarity model includes: Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
3. A privacy calculation method for equipment maintenance data as described in claim 2, characterized in that: The function n(f(A)-f(B)) used to enhance the ability to express the similarity between the word feature vector A and the word feature vector B includes: Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
4. A privacy calculation method for equipment maintenance data as claimed in claim 3, characterized in that: The feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes: Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
5. A privacy calculation method for equipment maintenance data as claimed in claim 4, characterized in that: The feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes: Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.
6. A privacy computing system for equipment maintenance data, used for privacy computing of equipment maintenance data when repairing mechanical equipment, characterized in that: include: A vector extraction module, used to obtain word feature vectors in the equipment maintenance data, and set a preset privacy corpus, and map the word feature vectors in the equipment maintenance data and the word feature vectors in the preset privacy corpus to a feature vector space through a feature vector mapping function; A similarity calculation module, used to set a feature vector similarity model, and calculate the similarity between the word feature vector in the equipment maintenance data and the word feature vector in the preset privacy corpus in the feature vector space; The desensitization module is used to define the word feature vectors in the equipment maintenance data whose similarity exceeds a preset similarity threshold as privacy data and perform desensitization processing.
7. A privacy computing system for equipment maintenance data as claimed in claim 6, characterized in that: The feature vector similarity model includes: Among them, S is the similarity between word feature vector A and word feature vector B, α is the similarity weight, f(A) is the feature vector mapping function of word feature vector A in the preset privacy corpus, f(B) is the feature vector mapping function of word feature vector B in equipment maintenance data, β is the adjustment factor, and n(f(A)-f(B)) is a function used to enhance the ability to express the similarity between word feature vector A and word feature vector B.
8. A privacy computing system for equipment maintenance data as claimed in claim 7, characterized in that: The function n(f(A)-f(B)) used to enhance the ability to express the similarity between the word feature vector A and the word feature vector B includes: Where k is the order of the polynomial, a′ i is the coefficient of the i-th polynomial, which is used to adjust the influence of each order.
9. A privacy computing system for equipment maintenance data as claimed in claim 8, characterized in that: The feature vector mapping function f(A) of the word feature vector A in the preset privacy corpus includes: Where k′ is the number of radial basis functions, w i′ is the weight of the i′th radial basis function, γ i′ is the adjustment factor of the i′th radial basis function, c i′A is the center of the i′th radial basis function of the word feature vector A.
10. A privacy computing system for equipment maintenance data as claimed in claim 9, characterized in that: The feature vector mapping function f(B) of the word feature vector B in the equipment maintenance data includes: Among them, c i′B is the center of the i′th radial basis function of the word feature vector B.