Power equipment data tracing method based on ant colony algorithm

Abstract

The invention discloses a power equipment data tracing method based on an ant colony algorithm, and the method comprises the steps: determining the correlation between data in an incomplete data set of power equipment; and a complete data set is constructed based on the correlation between the data, and power equipment data traceability is carried out through an ant colony algorithm. The method can be applied to power equipment data traceability of an incomplete data set with a large amount of missing data, and the search data traceability efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and particularly to a method for tracing the data of power equipment based on the ant colony algorithm. Background Art

[0002] Power equipment is a key component of the power system, and its performance directly affects the safe and stable operation of the power grid and the quality of power services. With the management mode changing from manual monitoring to automation and intelligence, the technology for tracing the whole-process data of power equipment has emerged as the times require and has become one of the important tools to ensure the efficient and reliable operation of the power system. Tracing the whole-process data of power equipment refers to collecting, storing, processing, and analyzing a large amount of data generated during the entire life cycle of power equipment from design, manufacturing, installation, commissioning, operation to retirement, so as to realize functions such as comprehensive perception of equipment status, fault warning, and performance evaluation. Therefore, while ensuring data security, realizing the effective tracing of the whole-process data of power equipment is of great significance for improving equipment management efficiency and optimizing equipment operation and maintenance.

[0003] Currently, a data tracing method combining blockchain technology is applied to building information modeling, designing a data chain management mode, coordinating the physical, digital, and operational layers of the information model; simulating dynamic supply chain operations, and performing real-time analysis and prediction on the behaviors of data chain participants from resources, processes, and assets; providing a comprehensive conceptual framework for securely and transparently tracing data information by using blockchain-based technology.

[0004] This data tracing method depends on high-quality data input. If the quality of the source data is not high, it will affect the final tracing result. However, the amount of data of power equipment is very large, and there are often a large number of missing data in actual applications. Therefore, if this method is applied to the tracing of power equipment data, it may lead to errors in the tracing result. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to propose a method for tracing the data of power equipment based on the ant colony algorithm, which can be applicable to the tracing of power equipment data with incomplete data sets where there are a large number of missing data, and can improve the efficiency of searching for data tracing.

[0006] Based on the above purpose, the present invention provides a method for tracing the data of power equipment based on the ant colony algorithm, including:

[0007] For the incomplete data set of power equipment, determine the correlation between the data in the data set;

[0008] Based on the complete data set constructed based on the correlation between the data, perform power equipment data tracing through the ant colony algorithm.

[0009] Among them, the incomplete dataset of the power equipment specifically includes the following three categories of data:

[0010] Power equipment information data, personnel information data for maintaining the power equipment, and data generated from maintaining the power equipment.

[0011] Preferably, for the incomplete dataset of the power equipment, determining the correlation between the data in the dataset specifically includes:

[0012] Determining the correlation between different categories of data in the dataset.

[0013] Preferably, determining the correlation between different categories of data in the dataset specifically includes:

[0014] Training the incomplete dataset through a Bayesian network, and based on the partially correlated data in the incomplete dataset, learning the correlation between different categories of data.

[0015] Preferably, when training the incomplete dataset through the Bayesian network, the training threshold of the Bayesian network is set based on the V value:

[0016]

[0017] In Equation 1, χ 2 is the chi-square test coefficient; N is the total number of samples in the dataset; q and w are the number of samples of two different categories of data in the dataset respectively.

[0018] Preferably, for the complete dataset constructed based on the correlation between data, power equipment data tracing is performed through the ant colony algorithm, which specifically includes:

[0019] Taking the power equipment information data in the dataset as the target point to be traced, and taking the personnel information data for maintaining the power equipment and the data generated from maintaining the power equipment as the retrieval points;

[0020] According to the correlation between different categories of data, exploring the path between the retrieval points and the target point through the ant colony algorithm;

[0021] Taking the data involved in the shortest path in the explored path as the data involved in tracing from the retrieval point to the target point.

[0022] Preferably, the objective function of the ant colony algorithm is shown in Equation 2:

[0023]

[0024] Among them, u ij is a binary variable. When the retrieval point j is related to the target point i to be traced, uij = 1; When not relevant, u ij = 0; r ij is the cost function for the retrieval point j to be relevant to the target point i to be traced; S is the number of target points in the complete data set Q'; s t is the number of retrieval points at time t.

[0025] Preferably, the constraint conditions of the objective function include: and

[0026]

[0027] The present invention also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it is used to implement the steps of the above-mentioned power equipment data tracing method based on the ant colony algorithm.

[0028] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program can be executed by at least one processor, so that the at least one processor executes the steps of the above-mentioned power equipment data tracing method based on the ant colony algorithm.

[0029] In the technical solution of the present invention, for the incomplete data set of power equipment, the correlation between the data in the data set is determined; based on the complete data set constructed based on the correlation between the data, the ant colony algorithm is used to trace the power equipment data. For the power equipment data of the incomplete data set with a large amount of missing data, in the technical solution of the present invention, first, for the incomplete data set, the correlation between the data in the data set is determined, so as to construct a relatively complete data set; furthermore, according to the data with data correlation, the ant colony algorithm is used to let multiple ants explore different paths simultaneously, which greatly improves the search efficiency of data tracing; and the ant colony algorithm will maintain multiple alternative paths. Even if a certain path is attacked or damaged, other paths are still available, ensuring the continuity and integrity of the data. Description of the Drawings

[0030] 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 drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0031] Figure 1 is a schematic structural diagram of a whole-process data tracing model of a power equipment provided by an embodiment of the present invention;

[0032] Figure 2 A flowchart of a method for tracing the data of power equipment based on the ant colony algorithm provided by an embodiment of the present invention;

[0033] Figure 3 A specific flowchart of the ant colony algorithm provided by an embodiment of the present invention;

[0034] Figure 4 A schematic diagram of the hardware structure of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments and the accompanying drawings.

[0036] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meanings understood by those of ordinary skill in the art to which the present disclosure belongs. The "first", "second", and similar terms used in the present disclosure do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", and "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0037] The inventors of the present invention considered that the whole-process data tracing of power equipment mainly involves those equipment that need to record and track detailed data during their life cycles, including transformers, circuit breakers, cables, relays, smart meters, battery energy storage systems, etc.; in actual applications, although a large amount of data is recorded, the correlation between the data is often missing, that is, the generated data set is incomplete, thus causing difficulties in the whole-process data tracing; in view of the incomplete characteristics of the power equipment data, in the technical solution of the present invention, first, for the incomplete data set of the power equipment, the correlation between the data in the data set is determined to construct a complete data set; and then, based on the constructed complete data set, the ant colony algorithm is used to trace the power equipment data.

[0038] The whole-process data traceability model of power equipment is built based on the unified data center for all services, and the traceability information of the equipment is obtained from various management systems. The entire model uses a front-end and back-end separated server architecture and relies on the unified power grid application platform. The model structure is divided in detail according to functions, mainly including multiple logical levels such as the display layer, information interaction layer, business logic layer, and persistence layer, and each layer provides specialized services. The model structure is as shown in Figure 1 shown.

[0039] As can be seen from Figure 1 , the display layer is based on the unified power grid application platform and is an operation interface for obtaining power equipment data suitable for users; the information interaction layer realizes the information interaction between the user side and the business side through the microservice structure; the business logic layer implements the logic of relevant tasks for the whole-process data traceability management of power equipment based on the Java language according to the service framework of the unified power grid application platform; the persistence layer realizes the interaction and storage between the logic layer and the data through the persistence component of the unified power grid application platform and database connection.

[0040] The technical solutions of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0041] A power equipment data traceability method based on the ant colony algorithm proposed in the embodiment of the present invention has a specific process as shown in Figure 2 shown, including the following steps:

[0042] Step S201: Obtain an incomplete data set of power equipment;

[0043] In this step, the business logic layer can obtain the following three types of data stored in the database of the persistence layer: power equipment information data E, personnel information data R for maintaining power equipment, and data U generated by maintaining power equipment.

[0044] Furthermore, an incomplete data set is constructed based on the obtained data; in the incomplete data set, the correlation between some data is recorded; for example, some data generated by maintaining power equipment is related to the corresponding power equipment information data E; however, in the incomplete data set, there is still a lot of data recorded in isolation and has no correlation with other data. For example, some data generated by maintaining power equipment cannot be associated with the corresponding power equipment information data.

[0045] Step S202: Determine the correlation between the data in the incomplete data set of power equipment;

[0046] In this step, for the incomplete data set of power equipment, the business logic layer determines the correlation between the data in the data set, that is, determines the correlation between different types of data in the data set.

[0047] In a specific embodiment, the business logic layer trains the incomplete data set through a Bayesian network, and learns the correlation between different categories of data based on some relevant data in the incomplete data set.

[0048] Generally, a data in a data set has multiple attributes. When training an incomplete data set through a Bayesian network, the correlation relationship and its conditional probability distribution among the attributes of the learned power equipment data are learned.

[0049] Where Q is the set of attributes with incomplete data, serving as the query attribute of the Bayesian network; W is the set of other attributes, serving as the evidence attribute of the Bayesian network; l is the attribute tuple; P(·) is the probability distribution; P(l[Q]l[W]) is the probability value of the evidence attribute accounting for the query attribute.

[0050] The data relationship function for each data in the incomplete data set Q is L = E ∪ R ∪ U;

[0051] The chi-square test is a method used to evaluate whether there is a correlation between two different categories of data (or variables) in a set. The chi-square test V coefficient V is derived from the chi-square test statistic and is a measure quantifying the association strength between two categories of data (or variables). The calculation formula is shown in Equation 1:

[0052]

[0053] In Equation 1, χ 2 is the chi-square test coefficient; N is the total number of samples in the data set; q and w are the number of samples of two different categories of data in the data set respectively.

[0054] The value range of V is in [0, 1]. The closer V is to 1, the stronger the correlation between two categories of data (or variables); the closer V is to 0, the less relevant the two categories of data are. The training threshold of the Bayesian network can be set according to experience or based on the V value; if the correlation (similarity) of the attribute set is lower than the set training threshold, then continue to add attributes to the attribute set for refinement until the training threshold is reached or no more attributes can be added; thus learning the correlation between different categories of data;

[0055] Based on the learned correlation between different categories of data, a complete data set Q′ of power equipment can be constructed.

[0056] Step S203: Based on the complete data set constructed based on the correlation between data, trace the source of power equipment data through the ant colony algorithm;

[0057] In this step, the business logic layer constructs a complete dataset based on the correlation between data, and traces the power equipment data through the ant colony algorithm.

[0058] Specifically, the ant colony algorithm is an evolutionary algorithm that mimics the foraging behavior of ants in nature, with advantages such as high distributed computing efficiency, strong positive feedback guidance ability, and good robustness. Aiming at the problem of tracing the whole-process data of power equipment, the proposed method improves the ant colony algorithm by introducing an adaptive chaos mechanism, updates the global pheromone, and improves the algorithm's ability to handle specific constraint situations, so as to better meet the actual needs of tracing the whole-process data of power equipment.

[0059] In this step, when applying the ant colony algorithm, the power equipment information data in the complete dataset Q′ is used as the target point to be traced, and the personnel information data of maintaining the power equipment in Q′ and the data generated by maintaining the power equipment are used as the retrieval points; according to the correlation between different types of data, the ant colony algorithm is used to explore the path between the retrieval points and the target point; the data involved in the shortest path in the exploration path is used as the data involved in tracing from the retrieval point to the target point.

[0060] By setting the objective function and constraint conditions of the ant colony algorithm, and setting the boundary of the search process, the ant colony algorithm is prevented from falling into invalid solutions, ensuring the accuracy and reliability of the tracing results.

[0061] Let S be the number of target points in Q′. Introduce the correlation vector K t , and give the correlation set between the retrieval point and the target point: where t is time; j is the encoding of the retrieval point; s t is the number of retrieval points at time t; At , it means that the retrieval point j at time t is related to the target point i.

[0062] The set objective function is shown in Equation 2, and the constraint conditions of the objective function are shown in Equations 3 and 4:

[0063]

[0064] where u ij is a binary variable. When the retrieval point j is related to the target point i to be traced, u ij =1; when not related, u ij =0; r ij is the cost function of the retrieval point j being related to the target point i to be traced, and the calculation formula is shown in Equation 5:

[0065] r ij =-log[P′I×p′(y jt |X t ,kt j = i)] (Equation 5)

[0066] In Equation 5, P′ is the retrieval probability; I is the retrieval point space; is the correlation possibility between the retrieval point j and the target point at time t; X t is the target point matrix to be traced back;

[0067] In the ant colony algorithm, each ant selects the best correlation from the retrieval point to the target point by continuously moving until a complete path is successfully explored. The length of this path is associated with the tightness of the correlation from the retrieval point to the target point: the shorter the path, the stronger the correlation.

[0068] The method of the present invention assigns appropriate retrieval points to each target according to the principle of minimizing the cost function. As can be seen from the above Equation 5, the cost function is associated with the likelihood estimation of the retrieval point. Therefore, the appropriate correlation selection is essentially based on the principle of maximizing the likelihood estimation of the retrieval partition. In addition, the probability of the ant colony selecting a path and the amount of pheromone deposited on the correlation from the retrieval point to the target point are both related to the likelihood function. To sum up, the best path searched by the ant colony is actually a set of retrieval points that can maximize the likelihood function. The specific process is as Figure 3 shown, including the following sub-steps:

[0069] Sub-step S301: Start tracing back, initialize the iteration number to 1, randomly place a ants on b routes, and start performing the relevant calculations for their respective target points;

[0070] Sub-step S302: Ant c traces back to the target with probability h, establishes an emergency table tab to save the possibility values of the selected routes during the detection process of ant c;

[0071] Specifically, the possibility value of the selected route during the detection process of ant c, that is, the movement probability, is calculated according to Equation 6:

[0072]

[0073] where α is the information heuristic factor; β is the expected heuristic factor; τ mn is the pheromone left by ant c around the retrieval point m and the retrieval point n (i.e., the retrieval point (m, n)); τ cn is the pheromone left by ant c around the retrieval point n, η is the visibility index; d mn is the distance between the retrieval point m and the retrieval point n; make η mn = 1 / d mn .

[0074] Sub-step S303: Update the pheromone and increment the iteration number by 1;

[0075] Specifically, when the ant colony selects a route, it will leave pheromones around the retrieval point (m, n), and the pheromone range is set to τ mn ∈(τ min , τ max ). To avoid the retrieval process falling into a local optimum, the proposed method introduces an adaptive chaos mechanism to update the pheromones. The calculation formula is shown in Equation 7:

[0076] τ′ mn =(1 - ρ)τ mn +Vτ mn +λz mn (Equation 7)

[0077] Wherein, τ′ mn is the updated pheromone; ρ is the pheromone evaporation factor; λ is the adjustment factor; z mn is the chaotic variable value; Vτ mn is the pheromone update value sorted according to the length of the route passed by the (σ - 1)-th ant; is the pheromone value released by the μ-th ant around the retrieval point (m, n):

[0078] Wherein, G is the pheromone intensity; J is the route length of the μ-th ant during the retrieval process.

[0079] Sub-step S304: Detect whether the maximum number of iterations has been reached; if so, calculate the optimal pheromone value, output the optimal solution, that is, obtain the data traceability result, and end the algorithm retrieval; otherwise, return to sub-step S302.

[0080] In the technical solution of the present invention, for the incomplete data set of power equipment, the correlation between the data in the data set is determined; based on the complete data set constructed based on the correlation between the data, the ant colony algorithm is used for power equipment data traceability. For the power equipment data of the incomplete data set with a large number of missing data, in the technical solution of the present invention, first, for the incomplete data set, the correlation between the data in the data set is determined, so as to construct a relatively complete data set; furthermore, based on the data with data correlation, the ant colony algorithm is used to simultaneously explore different paths by multiple ants, greatly improving the search data traceability efficiency; and the ant colony algorithm will maintain multiple alternative paths. Even if a certain path is attacked or damaged, other paths are still available, ensuring the continuity and integrity of the data.

[0081] Figure 4FIG. 0 schematically shows a hardware architecture diagram of a computer device 1300 for a power equipment data traceability method based on an ant colony algorithm according to an embodiment of the present application. In this embodiment, the computer device 1300 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. For example, it can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of multiple servers), etc. As Figure 4 shown, the computer device 1300 at least includes, but is not limited to: a memory 1310, a processor 1320, and a network interface 1330 that can be communicatively linked to each other through a system bus. Among them:

[0082] The memory 1310 at least includes one type of computer-readable storage medium. The readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 1310 can be an internal storage module of the computer device 1300, such as the hard disk or memory of the computer device 1300. In other embodiments, the memory 1310 can also be an external storage device of the computer device 1300, such as a plug-in hard disk equipped on the computer device 1300, a smart media card (abbreviated as SMC), a secure digital (abbreviated as SD) card, a flash card, etc. Of course, the memory 1310 can also include both the internal storage module and the external storage device of the computer device 1300. In this embodiment, the memory 1310 is generally used to store the operating system and various application software installed on the computer device 1300, such as the program code of the power equipment data traceability method based on the ant colony algorithm. In addition, the memory 1310 can also be used to temporarily store various types of data that have been output or will be output.

[0083] In some embodiments, the processor 1320 can be a central processing unit (abbreviated as CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 1320 is generally used to control the overall operation of the computer device 1300, such as performing control and processing related to data interaction or communication with the computer device 1300. In this embodiment, the processor 1320 is used to run the program code stored in the memory 1310 or process data.

[0084] The network interface 1330 may include a wireless network interface or a wired network interface, which is generally used to establish a communication link between the computer device 1300 and other computer devices. For example, the network interface 1330 is used to connect the computer device 1300 to an external terminal through a network, and establish a data transmission channel and a communication link between the computer device 1300 and the external terminal. The network may be a wireless or wired network such as an enterprise intranet (Intranet), the Internet, the Global System of Mobile communication (GSM for short), Wideband Code Division Multiple Access (WCDMA for short), 4G network, 5G network, Bluetooth, Wi-Fi, etc.

[0085] It should be noted that Figure 4 Only the computer device with components 1310 - 1330 is shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0086] In this embodiment, the power device data tracing method based on the ant colony algorithm stored in the memory 1310 can also be divided into one or more program modules and executed by one or more processors (processor 1320 in this embodiment) to complete the embodiments of the present application.

[0087] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0088] Those of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, and for the sake of brevity, they are not provided in detail.

[0089] In addition, for simplicity of explanation and discussion, and in order not to make the present invention difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Further, the devices may be shown in block diagram form in order to avoid making the present invention difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the present invention is to be implemented (i.e., these details should be entirely within the understanding of those skilled in the art). In cases where specific details (such as circuits) are set forth to describe exemplary embodiments of the present invention, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details or with variations of these specific details. Accordingly, these descriptions should be considered illustrative rather than restrictive.

[0090] Although the present invention has been described in connection with specific embodiments of the present invention, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0091] Embodiments of the present invention are intended to cover all such alternatives, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for tracing the source of power equipment data based on ant colony algorithm, characterized in that: include: For an incomplete data set of an electric power device, determining correlation between data in the data set; Based on the complete data set constructed based on the correlation between data, the power equipment data is traced through the ant colony algorithm.

2. The method according to claim 1, characterized in that The incomplete data set of the power equipment specifically includes the following three types of data: Power equipment information data, information data of personnel who maintain power equipment, and data generated by maintaining power equipment.

3. The method according to claim 1, characterized in that The step of determining the correlation between the data in the incomplete data set of the electric power equipment is as follows: Determine the correlation between different categories of data in the data set.

4. The method according to claim 3, characterized in that The determining of the correlation between different categories of data in the data set is specifically: The incomplete data set is trained by a Bayesian network, and the correlation between data of different categories is learned based on part of the correlated data in the incomplete data set.

5. The method according to claim 3, characterized in that: When the incomplete data set is trained by the Bayesian network, the training threshold of the Bayesian network is set based on the V value: In formula 1, χ 2 is the chi-square test coefficient; N is the total number of samples in the data set; q and w are the number of samples of two different categories of data in the data set, respectively.

6. The method according to claim 1, characterized in that The complete data set constructed based on the correlation between data is used to trace the power equipment data through the ant colony algorithm, specifically including: The power equipment information data in the data set is used as the target point to be traced, and the personnel information data of the power equipment maintenance and the data generated by the power equipment maintenance are used as the retrieval points; According to the correlation between different categories of data, the path between the retrieval point and the target point is explored through the ant colony algorithm; The data involved in the shortest path in the exploration path is used as the data involved in tracing from the retrieval point to the target point.

7. The method according to claim 1, characterized in that The objective function of the ant colony algorithm is shown in Formula 2: Among them, u ij is a binary variable. When the retrieval point j is related to the target point i to be traced, u ij =1; when irrelevant, u ij =0; r ij is the cost function related to the retrieval point j and the target point i to be traced; S is the number of target points in the complete data set Q′; s t is the number of retrieval points at time t.

8. The method according to claim 7, characterized in that The constraints of the objective function include: as well as 9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it is used to implement the steps of the power equipment data tracing method based on ant colony algorithm as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program can be executed by at least one processor so that the at least one processor executes the steps of the power equipment data tracing method based on ant colony algorithm as described in any one of claims 1 to 8.

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