Enterprise operation data analysis method and system, storage medium and equipment

Abstract

The invention provides an enterprise operation data analysis method and system, a storage medium and equipment. The analysis method comprises the following steps: acquiring an actual operation data set of a plurality of operation modules; determining enterprise operation data needing to be analyzed, and obtaining a corresponding actual operation data set; determining multi-level weights influencing the enterprise operation data, and determining actual operation data associated with each level of weight from the corresponding actual operation data set based on the priority of each level of weight; according to the method, the actual operation data corresponding to the first-level weight is directly adopted for analysis, so that the obtained actual operation data has representativeness, and the analysis efficiency is improved. The first-level weight directly influencing the enterprise operation data is screened out from the multi-level weight, and the first-level analysis atlas corresponding to the first-level weight is formed. According to the method, the representative actual operation data form the first-level analysis atlas, so that the number of acquired actual operation data during data analysis is reduced, the analysis amount can be greatly reduced, and the analysis speed can be remarkably increased.

Description

Technical Field

[0001] The present invention relates to the technical field of operational data analysis, and in particular to an enterprise operational data analysis method, system, storage medium and device. Background Art

[0002] Enterprise operational data analysis refers to the process of collecting, organizing, analyzing and interpreting various types of data generated by an enterprise in its daily operations to evaluate business performance, identify problems, optimize decisions and improve efficiency. Its core goal is to use data to drive business growth, help enterprises manage resources more scientifically, reduce costs and improve competitiveness. The existing enterprise data analysis method is based on associating the core text data of each functional module in the operation management platform, and integrating the associated core text data to establish a global knowledge graph. However, given that the global knowledge graph requires associating and analyzing each core text data in multiple functional modules, the most important core text data needs to be associated with multiple secondary core text data at the same time, making it impossible for the most important core text data to form the most important knowledge graph in a short period of time, resulting in the problems of large analysis volume and low analysis speed. Summary of the Invention

[0003] Based on this, the purpose of the present invention is to provide an enterprise operation data analysis method, system, storage medium and device to solve the problems of large analysis volume and low analysis speed in the existing enterprise operation data analysis methods in the background technology.

[0004] A first aspect of the present invention is to provide an enterprise operation data analysis method, which is applied on an operation management platform and includes: Acquire actual operation data sets of multiple operation modules in the operation management platform, wherein the actual operation data sets include multiple actual operation data recorded in the operation modules; Determine the enterprise operation data that needs to be analyzed, and obtain the corresponding actual operation data set from the corresponding operation module; Determining multiple levels of weights affecting the enterprise operation data, and determining at least one actual operation data associated with each level of weight from the corresponding actual operation data set based on the priority of each level of weight; At least one first-level weight that directly affects the enterprise operation data is screened out from the multiple levels of weights, and the actual operation data corresponding to the first-level weight is analyzed to form a first-level analysis graph corresponding to the first-level weight.

[0005] Furthermore, after the step of selecting at least one first-level weight that directly affects the enterprise operation data from the multiple-level weights, the method further includes: Filtering out a plurality of second-level weights that indirectly affect the enterprise operation data from the multi-level weights; The actual operating data corresponding to each second-level weight is analyzed respectively to form a plurality of second-level analysis graphs corresponding to each second-level weight.

[0006] Further, the priority of each of the second-level weights is lower than the priority of the first-level weights, and the priority of each of the second-level weights is arranged in order of priority; Wherein, after the step of respectively analyzing the actual operating data corresponding to each second-level weight to form a plurality of second-level analysis graphs corresponding to each second-level weight, the method further includes: According to the priority of each second-level weight, the corresponding actual operation data is analyzed in sequence to form a plurality of second-level analysis graphs corresponding in sequence to the priority order.

[0007] Furthermore, after the step of analyzing the corresponding actual operation data in sequence according to the priority of each second-level weight to form a plurality of second-level analysis graphs corresponding in sequence to the priority order, the method further includes: The first-level analysis map is integrated with a plurality of the second-level analysis maps to form a global analysis map.

[0008] Furthermore, in the step of determining the multi-level weights affecting the enterprise operation data, the method of determining the multi-level weights includes: Obtain historical operational data and train weighted learning models; The enterprise operation data to be analyzed is subjected to weight analysis from the weight learning model to form multi-level weights with a priority order.

[0009] Furthermore, in the step of performing weight analysis on the enterprise operation data analyzed as needed from the weight learning model to form a multi-level weight with a priority order: The priority order of the priorities is determined based on at least one actual operation data set in each of the operation modules; Determine whether each of the actual operating data directly or indirectly affects the analysis graph, and define the core operating data directly affecting the analysis graph as a first weight; defining the core operating data that indirectly affects the analysis graph as a second weight; The actual operation data is determined based on the professional knowledge of each of the operation modules.

[0010] Furthermore, after the step of analyzing the actual operating data corresponding to the first-level weights to form a first-level analysis graph corresponding to the first-level weights, the method further includes: Determining the operation type of the operation management platform; Using a big data analysis model to obtain other operation management platforms that are similar to or have the same operation type as the operation management platform; The first-level analysis map is compared with the adjacent / same other operation management platforms to form a comparison map between the operation management platform and the other operation type maps.

[0011] A second aspect of the present invention is to provide an enterprise operation data analysis system, the system comprising: an acquisition module, configured to acquire actual operation data sets of a plurality of operation modules in the operation management platform, wherein the actual operation data sets include a plurality of actual operation data recorded in the operation modules; A determination module is used to determine the enterprise operation data that needs to be analyzed and obtain the corresponding actual operation data set from the corresponding operation module; The determination module is further configured to determine multiple levels of weights that influence the enterprise operation data; an association module, configured to determine at least one actual operation data associated with each level of the weight from the corresponding actual operation data set based on the priority of each level of the weight; The analysis module is used to screen out at least one first-level weight that directly affects the enterprise operation data from the multiple levels of weights, and analyze the actual operation data corresponding to the first-level weight to form a first-level analysis map corresponding to the first-level weight.

[0012] A third aspect of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned enterprise operation data analysis method when executed by a processor.

[0013] The fourth aspect of the present invention is to provide an enterprise operation data analysis device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned enterprise operation data analysis method when executing the program.

[0014] Compared with the prior art, the enterprise operation data analysis method, system, storage medium and device shown in the present invention have the following beneficial effects: In an enterprise operation data analysis method provided by the present invention, the actual operation data set of multiple operation modules in the operation management platform is first obtained, and the actual operation data set includes multiple actual operation data recorded in the operation modules; then the enterprise operation data to be analyzed is determined, and the corresponding actual operation data set is obtained from the corresponding operation module; the multi-level weights affecting the enterprise operation data are determined, and based on the priority of each level of weight, at least one actual operation data associated with each level of weight is determined from the corresponding actual operation data set; finally, at least one first-level weight that directly affects the enterprise operation data is screened out from the multi-level weights, and analysis is performed based on the actual operation data corresponding to the first-level weight , to form a first-level analysis map corresponding to the first-level weight. The present application screens all the actual operation data from the actual operation data set according to the first-level weight, selects the actual operation data corresponding to the first-level weight for analysis, and forms a first-level analysis map, so that when forming the first-level analysis map, the most representative actual operation data can be directly used, and the representative actual operation data can be analyzed at the first time, thereby reducing the amount of actual operation data obtained when analyzing the data. Compared with the existing technology of analyzing all core text data, the present application can greatly reduce the analysis amount when forming the first-level analysis map, and significantly improve the analysis speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the enterprise operation data analysis method according to the first embodiment of the present invention; Figure 2 Schematic diagram of an enterprise operation data analysis system in a third embodiment of the present invention; Figure 3 Schematic diagram of an enterprise operation data analysis device in a fourth embodiment of the present invention.

[0016] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] First embodiment See also Figure 1 , shown is the enterprise operation data analysis method in the first embodiment of the present invention. The analysis method is applied on the operation management platform. It should be noted that, in this example, the operation management platform can be an APP for managing operation data, or it can be a small program for managing operation data, etc. Since the operation management platform belongs to the conventional existing technology in this field, it will not be specifically explained here.

[0021] Specifically, the analysis methods include S01-S04; S01, obtaining actual operation data sets of multiple operation modules in the operation management platform, where the actual operation data sets include multiple actual operation data recorded in the operation modules.

[0022] To facilitate understanding of this case, the operation management platform includes cost operation module, profit operation module, personnel structure operation module, etc.

[0023] The operation module of a construction company is used as an example. The cost operation module includes the material cost operation submodule, the machinery and equipment cost operation submodule, the project management cost operation submodule, and the quality rework cost operation submodule. The profit operation module includes the project settlement income operation sub-module, etc. The personnel structure operation module includes the management level operation submodule, the technical level operation submodule, and the operational level operation submodule; Among them, the actual operation data set in the operation module is integrated based on the data of each operation sub-module in the operation module. For example, the actual operation data set of the cost operation module is composed of material cost data, machinery and equipment data, project management data, and quality rework data, and material cost data, machinery and equipment data, project management data, and quality rework data are single actual operation data.

[0024] S02: Determine the enterprise operation data that needs to be analyzed and obtain the corresponding actual operation data set from the corresponding operation module; It should be noted that the enterprise operation data that needs to be analyzed can be all the operation data of the entire project, or it can be the operation data of a certain time period in a project. In view of the diversity of analysis operation data, in this example, the corresponding enterprise operation data can be selected according to user customization, and the corresponding actual operation data set can be obtained from the operation module based on the corresponding enterprise operation data.

[0025] S03, determining multiple levels of weights that affect the enterprise operation data, and determining at least one actual operation data associated with each level of weight from the corresponding actual operation data set based on the priority of each level of weight.

[0026] It should be noted that in step S03, in the step of determining the multi-level weights affecting the enterprise operation data, the method for determining the multi-level weights includes: Obtain historical operational data and train weighted learning models; Perform weight analysis from the weight learning model based on the enterprise operation data that needs to be analyzed to form multi-level weights with priority order; In the step of performing weight analysis from a weight learning model according to the enterprise operation data to be analyzed to form a multi-level weight having a priority order: The priority order of the priority is determined based on at least one actual operation data set in each operation module; Determine whether each actual operating data directly / indirectly affects the analysis graph, and define the core operating data corresponding to the direct impact analysis graph as the first weight; The core operating data corresponding to the indirect impact analysis graph is defined as the second weight; The actual operating data is determined based on the professional knowledge of each operating module.

[0027] It should be noted that professional knowledge refers to actual operational data that directly affects weights defined by those skilled in the art.

[0028] S04, selecting at least one first-level weight that directly affects the enterprise's operating data from the multi-level weights, and analyzing the actual operating data corresponding to the first-level weight to form a first-level analysis graph corresponding to the first-level weight.

[0029] Specifically, in view of the different weight priorities corresponding to each level of weight in the multi-level weights, in the present application, the first-level weight corresponds to the optimal weight. According to the first-level weight, all the multiple actual operation data in the actual operation data set are screened, and the actual operation data corresponding to the first-level weight are selected for analysis, and a first-level analysis map is formed. When forming the first-level analysis map, the most representative actual operation data can be directly used, and the representative actual operation data can be analyzed at the first time, thereby reducing the amount of actual operation data obtained when analyzing the data. Compared with the existing technology of analyzing all core text data, the present application can greatly reduce the analysis amount when forming the first-level analysis map, and significantly improve the analysis speed.

[0030] To understand this case, please refer to Table 1. Table 1 In Table 1, the construction industry is used as an example. The actual operation data set is the data set of all sub-modules in the above Table 1, and the sub-modules in the cost operation module and the sub-modules in the profit operation module in Table 1 are not limited to the cost operation module and the profit operation module. It can also include multiple other sub-modules, which are not specifically limited here.

[0031] It should be noted that the part in bold black text is the actual operation data selected from the actual operation data set based on the first-level weight.

[0032] When generating the first-level analysis chart, directly select the total material cost: 648,720 yuan, the comprehensive equipment cost: 38,000 yuan, the total management cost: 350,000 yuan, the total rework cost: 45,000 yuan and the total rework cost: 45,000 yuan to generate the first-level analysis chart. In other words, the amounts represented by the above costs are all actual operating data corresponding to the first-level weights.

[0033] In summary, the enterprise operation data analysis method shown in this embodiment has at least the following advantages compared to the enterprise operation data analysis methods in the prior art: In an enterprise operation data analysis method provided by the present invention, the actual operation data sets of multiple operation modules in the operation management platform are first obtained, and the actual operation data sets include multiple actual operation data recorded in the operation modules; then the enterprise operation data that needs to be analyzed is determined, and the corresponding actual operation data sets are obtained from the corresponding operation modules; the multi-level weights that affect the enterprise operation data are determined, and based on the priority of each level of weight, at least one actual operation data associated with each level of weight is determined from the corresponding actual operation data sets; finally, at least one first-level weight that directly affects the enterprise operation data is screened out from the multi-level weights, and the actual operation data corresponding to the first-level weight is analyzed. In order to form a first-level analysis map corresponding to the first-level weight, all the multiple actual operation data in the actual operation data set are screened according to the first-level weight, the actual operation data corresponding to the first-level weight are selected for analysis, and a first-level analysis map is formed. When forming the first-level analysis map, the most representative actual operation data can be directly used, and the representative actual operation data can be analyzed at the first time, thereby reducing the amount of actual operation data obtained when analyzing the data. Compared with the prior art of analyzing all core text data, this application can greatly reduce the analysis amount when forming the first-level analysis map, and significantly improve the analysis speed.

[0034] Second embodiment The second embodiment of the present invention provides a method for analyzing enterprise operation data. The method for analyzing enterprise operation data in this embodiment differs from the method for analyzing enterprise operation data in the first embodiment in that: After the step of selecting at least one first-level weight that directly affects the enterprise operation data from the multi-level weights, the method further includes: Filter out multiple second-level weights that indirectly affect the enterprise's operating data from the multi-level weights; Analyze the actual operating data corresponding to each second-level weight respectively to form a plurality of second-level analysis graphs corresponding to each second-level weight; The priority of each second-level weight is less than the priority of the first-level weight, and the priority of each second-level weight is arranged in the order of priority; After analyzing the actual operating data corresponding to each second-level weight to form a plurality of second-level analysis graphs corresponding to each second-level weight, the method further includes: According to the priority of each second-level weight, the corresponding actual operation data is analyzed in sequence to form a plurality of second-level analysis maps corresponding to the priority order; After analyzing the corresponding actual operation data according to the priority of each second-level weight in sequence to form a plurality of second-level analysis maps corresponding to the priority order, the method further includes: The first-level analysis map is integrated with multiple second-level analysis maps to form a global analysis map.

[0035] It should be noted that the first-level analysis map generated in the first embodiment is representative, but in view of the limited analysis data obtained, it is impossible to perform a comprehensive analysis based on all the actual operating data. In this example, multiple second-level weights that indirectly affect the enterprise operating data are screened out from the multi-level weights, and the actual operating data corresponding to each second-level weight are analyzed separately to form multiple second-level analysis maps corresponding to each second-level weight. That is to say, after generating the first-level analysis map, the actual operating data corresponding to the second-level weights are analyzed separately and the corresponding second-level analysis maps are generated. Finally, the first-level analysis map and the second-level analysis map are integrated to generate a global analysis map.

[0036] In this embodiment, although all actual operation data are analyzed, the analysis is performed in order according to the priority of the multi-level weights, so that a global analysis map can be obtained during the analysis, effectively ensuring the integrity and reliability of the operation data when analyzing.

[0037] In some other preferred embodiments, after the step of analyzing the actual operating data corresponding to the first-level weights to form a first-level analysis graph corresponding to the first-level weights, the method further includes: Determining the operation type of the operation management platform; Using a big data analysis model to obtain other operation management platforms that are similar to or have the same operation type as the operation management platform; The first-level analysis map is compared with the adjacent / same other operation management platforms to form a comparison map between the operation management platform and the other operation type maps.

[0038] It should be noted that the big data analysis model directly compares the graphs of other operation management platforms that are known in the big data and are the same / similar to the operation management platform in this application. Other operation management platforms can be other building operation platforms similar to the building operation platform. By comparing the generated global analysis graph with the analysis graphs of other operation management platforms, the gaps and advantages between the operation platform shown in this application and other operation platforms can be effectively analyzed.

[0039] For example, the operation management platform shown in this application is the operation management platform of building A, and the other operation management platform is the operation management platform of building B. The global analysis map of the operation management platform of building A is analyzed and compared with the global analysis map of the operation management platform of building B. The example is not limiting. This application can also adopt the operation management platforms of other industries, such as catering, clothing, etc.

[0040] In summary, the enterprise operation data analysis method shown in this embodiment has at least the following beneficial effects compared to the enterprise operation data analysis method in the first embodiment: In the enterprise operation data analysis method of the second embodiment of the present invention, multiple second-level weights that indirectly affect the enterprise operation data are screened out from the multi-level weights, and the actual operation data corresponding to each second-level weight are analyzed separately to form multiple second-level analysis maps corresponding to each second-level weight. That is to say, after generating the first-level analysis map, the actual operation data corresponding to the second-level weight are analyzed separately and the corresponding second-level analysis map is generated. Finally, the first-level analysis map and the second-level analysis map are integrated to generate a global analysis map, which effectively ensures the integrity and reliability when analyzing the operation data.

[0041] Third embodiment Another aspect of the present invention is to provide a picture generation system. Figure 2 , which shows a picture generation system in a third embodiment of the present invention, and includes: An acquisition module 11 is configured to acquire actual operation data sets of multiple operation modules in the operation management platform, where the actual operation data sets include multiple actual operation data recorded in the operation modules; Determination module 12, used to determine the enterprise operation data that needs to be analyzed and obtain the corresponding actual operation data set from the corresponding operation module; The determination module 12 is further used to determine the multi-level weights that affect the enterprise operation data; an association module 13, configured to determine at least one actual operation data associated with each level of weight from the corresponding actual operation data set based on the priority of each level of weight; The analysis module 14 is used to select at least one first-level weight that directly affects the enterprise operation data from the multi-level weights, and analyze the actual operation data corresponding to the first-level weight to form a first-level analysis map corresponding to the first-level weight.

[0042] Fourth embodiment Another aspect of the present invention is to provide an enterprise operation data analysis device, see Figure 3, shown is an enterprise operation data analysis device in the fourth embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the enterprise operation data analysis method as described above is implemented.

[0043] Among them, the device of the enterprise operation data analysis method can be specifically a vehicle, and the processor 10 in some embodiments can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, which is used to run the program code stored in the memory 20 or process data, such as executing access restriction programs.

[0044] The memory 20 includes at least one type of readable storage medium, including flash memory, hard disks, multimedia cards, card-type memories (e.g., SD or DX memories), magnetic memories, magnetic disks, optical disks, and the like. In some embodiments, the memory 20 may be an internal storage unit of the enterprise operations data analysis device, such as the device's hard disk. In other embodiments, the memory 20 may also be an external storage device of the enterprise operations data analysis device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, and the like. Furthermore, the memory 20 may include both the internal storage unit and an external storage device of the enterprise operations data analysis device. The memory 20 can be used not only to store application software and various data installed in the enterprise operations data analysis device, but also to temporarily store data that has been output or is about to be output.

[0045] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the enterprise operation data analysis device. In other embodiments, the enterprise operation data analysis device may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0046] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the enterprise operation data analysis method as described above when the program is executed by a processor.

[0047] Those skilled in the art will appreciate that the logic or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, including a processor system, or other system capable of fetching instructions from and executing instructions on an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0048] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0049] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0050] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0051] The above embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for analyzing enterprise operation data, characterized in that: The analysis method is applied on the operation management platform, and the analysis method includes: Acquire actual operation data sets of multiple operation modules in the operation management platform, wherein the actual operation data sets include multiple actual operation data recorded in the operation modules; Determine the enterprise operation data that needs to be analyzed, and obtain the corresponding actual operation data set from the corresponding operation module; Determining multiple levels of weights affecting the enterprise operation data, and determining at least one actual operation data associated with each level of weight from the corresponding actual operation data set based on the priority of each level of weight; At least one first-level weight that directly affects the enterprise operation data is screened out from the multiple levels of weights, and the actual operation data corresponding to the first-level weight is analyzed to form a first-level analysis graph corresponding to the first-level weight.

2. The enterprise operation data analysis method according to claim 1, characterized in that: After the step of selecting at least one first-level weight that directly affects the enterprise operation data from the multiple-level weights, the method further includes: Filtering out a plurality of second-level weights that indirectly affect the enterprise operation data from the multi-level weights; The actual operating data corresponding to each second-level weight is analyzed respectively to form a plurality of second-level analysis graphs corresponding to each second-level weight.

3. The enterprise operation data analysis method according to claim 2, characterized in that: The priority of each of the second-level weights is lower than the priority of the first-level weights, and the priority of each of the second-level weights is arranged in order of priority; Wherein, after the step of respectively analyzing the actual operating data corresponding to each second-level weight to form a plurality of second-level analysis graphs corresponding to each second-level weight, the method further includes: According to the priority of each second-level weight, the corresponding actual operation data is analyzed in sequence to form a plurality of second-level analysis graphs corresponding in sequence to the priority order.

4. The enterprise operation data analysis method according to claim 2, characterized in that: After the step of analyzing the corresponding actual operation data in sequence according to the priority of each second-level weight to form a plurality of second-level analysis graphs corresponding in sequence to the priority order, the method further includes: The first-level analysis map is integrated with a plurality of the second-level analysis maps to form a global analysis map.

5. The enterprise operation data analysis method according to claim 1, characterized in that: In the step of determining the multi-level weights affecting the enterprise operation data, the method of determining the multi-level weights includes: Obtain historical operational data and train weighted learning models; The enterprise operation data to be analyzed is subjected to weight analysis from the weight learning model to form multi-level weights with a priority order.

6. The enterprise operation data analysis method according to claim 5, characterized in that: In the step of performing weight analysis on the enterprise operation data analyzed according to needs from the weight learning model to form a multi-level weight with priority order: The priority order of the priorities is determined based on at least one actual operation data set in each of the operation modules; Determine whether each of the actual operating data directly or indirectly affects the analysis graph, and define the core operating data directly affecting the analysis graph as a first weight; defining the core operating data that indirectly affects the analysis graph as a second weight; The actual operation data is determined based on the professional knowledge of each of the operation modules.

7. The enterprise operation data analysis method according to claim 1, characterized in that: After the step of analyzing the actual operating data corresponding to the first-level weights to form a first-level analysis graph corresponding to the first-level weights, the method further includes: Determining the operation type of the operation management platform; Using a big data analysis model to obtain other operation management platforms that are similar to or have the same operation type as the operation management platform; The first-level analysis map is compared with the adjacent / same other operation management platforms to form a comparison map between the operation management platform and the other operation type maps.

8. An enterprise operation data analysis system, characterized in that: The system is used to implement the enterprise operation data analysis method according to any one of claims 1 to 7, and the system includes: an acquisition module, configured to acquire actual operation data sets of a plurality of operation modules in the operation management platform, wherein the actual operation data sets include a plurality of actual operation data recorded in the operation modules; A determination module is used to determine the enterprise operation data that needs to be analyzed and obtain the corresponding actual operation data set from the corresponding operation module; The determination module is further configured to determine multiple levels of weights that influence the enterprise operation data; an association module, configured to determine at least one actual operation data associated with each level of the weight from the corresponding actual operation data set based on the priority of each level of the weight; The analysis module is used to screen out at least one first-level weight that directly affects the enterprise operation data from the multiple levels of weights, and analyze the actual operation data corresponding to the first-level weight to form a first-level analysis map corresponding to the first-level weight.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the enterprise operation data analysis method according to any one of claims 1 to 7 is implemented.

10. An enterprise operation data analysis device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for analyzing enterprise operation data according to any one of claims 1 to 7 is implemented.

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