Enterprise operation data detection and visual analysis system

Abstract

The invention relates to the field of enterprise operation data analysis, in particular to an enterprise operation data detection and visual analysis system, which comprises a data acquisition module used for acquiring basic operation data and determining a complete acquisition period and actual starting and ending time; the time sequence generation module is used for sorting, caching and storing various basic operation data; the thermodynamic diagram generation module is used for generating various enterprise operation data thermodynamic diagrams; the operation judgment module is used for obtaining an enterprise operation judgment result based on logic judgment; and the mark supplementing module is used for mapping the judgment result into a graphic symbol and marking the graphic symbol in the thermodynamic diagram. According to the invention, a manager can accurately grasp the operation state of an enterprise, the provided data processing flow can be combined with an enterprise management strategy in a closed-loop manner, the judgment of post configuration, production plans and incentive measures is assisted, and the overall operation efficiency and decision-making rapidness of the enterprise are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of enterprise operation data analysis, and particularly relates to an enterprise operation data detection and visualization analysis system. BACKGROUND

[0002] With the rapid development of big data and business intelligence technology, real-time detection and visualization analysis of enterprise operation data has become an important means for enterprises to improve operational efficiency and optimize resource allocation. Currently, the mainstream methods in this field mainly rely on various information management systems, presenting and analyzing key indicators such as sales, inventory, and production through data dashboards, statistical reports, and basic charts.

[0003] However, the existing systems still have several limitations in practical application.

[0004] Firstly, in terms of data collection and cycle division, most systems use natural months as fixed statistical cycles, failing to fully consider the widely used cycle rules in business operations. Since the start and end dates of natural months often do not align with complete weeks, directly aggregating data by month can lead to fragmented week data, making week-on-week analysis distorted and making it difficult to accurately capture weekly operational fluctuations, affecting the accuracy of cycle trend judgment.

[0005] Secondly, in terms of visualization analysis, existing technologies mainly focus on the presentation of single data, lacking deep integration and correlation analysis of multi-dimensional data. For example, sales, inventory, and personnel data are not placed in a unified view for linked analysis, making it difficult for managers to accurately grasp the internal relationships between data and overall operational status.

[0006] In addition, the analysis functions of most systems still remain at the stage of data description and simple alarm, lacking deep strategy analysis capability based on business rules, and unable to directly link data anomalies to management, resulting in a gap between analysis and decision-making.

[0007] Therefore, there is an urgent need for an analysis system that can accurately divide data cycles, support multi-dimensional data fusion visualization, and output directly actionable strategy suggestions through rule judgment, to make up for the shortcomings of existing technologies in the above aspects.

[0008] The Chinese patent publication No. CN113112176A discloses an enterprise carbon emission visualization early warning system based on big data, which comprises a carbon emission division module, an enterprise carbon emission information summary module and an enterprise carbon emission information comparison and detection module. The output end of the carbon emission division module is electrically connected with the input end of the enterprise carbon emission information summary module. Through the enterprise carbon emission information summary module, the total amount of carbon emission of the enterprise can be detected, and the total amount of fuel required for production can be effectively detected to analyze whether the fuel consumption matches the carbon emission amount. At the same time, the content of various pollutants in carbon emission can be detected. The enterprise carbon emission information comparison and detection module collects the information detected by the enterprise carbon emission information summary module and compares it with the standard information to effectively determine whether the carbon emission of the enterprise meets the standard. At the same time, it can detect and warn in time.

[0009] The existing enterprise operation data analysis technology is not accurate in dividing the cycle of enterprise operation data itself, the abnormal identification method is single, the visualization presentation is mainly used for displaying carbon emission information, and it is not closely combined with the enterprise operation decision logic, so there are obvious limitations in assisting management decision. SUMMARY

[0010] Therefore, the present application provides an enterprise operation data detection and visualization analysis system to overcome the problem of single abnormal identification method and visualization separation from decision logic by making decision judgment in accurate cycle division of enterprise operation data.

[0011] To achieve the above purpose, the present application provides an enterprise operation data detection and visualization analysis system, which comprises: A data acquisition module is used to acquire basic operation data in each collection period of the current month; A time series generation module is connected with the data acquisition module and is used to store the basic operation data of each collection point in the collection period after time sorting; A heat map generation module is connected with the time series generation module and is used to calculate the weekly average index based on the time series data of each complete collection period, convert it into the corresponding heat value and generate the heat map of various enterprise operation data of the current month; An operation judgment module is connected with the data acquisition module and the heat map generation module and is used to construct a global feature value set of the basic operation data, judge the supply and demand relationship corresponding to the global feature value set based on the supply and demand relationship judgment standard, and perform corresponding supply and demand threshold comparison judgment when the first supply and demand relationship or the third supply and demand relationship is obtained to obtain the enterprise operation judgment result; A label supplement module is connected with the heat map generation module and the operation judgment module respectively and is used to map the enterprise operation judgment result into a graphical symbol and supplement the label in the corresponding heat map.

[0012] Further, the data acquisition module comprises a calling unit and a complete collection cycle number determination unit; The calling unit is configured to acquire basic business data including sales data, inventory data and personnel production data; The complete collection cycle number determination unit is connected with the calling unit and configured to determine a complete collection cycle number, corresponding number marks and actual start and end time of the current month.

[0013] Further, the complete collection cycle number determination unit comprises a time judgment subunit, a segment collection cycle division subunit and a collection point count value comparison subunit; The time judgment subunit is configured to acquire ideal start and end time of the current month, collection points in each collection cycle in the current month and natural day marks of the collection points in the collection cycle; The segment collection cycle division subunit is connected with the time judgment subunit and configured to judge collection point count values in the month in each collection cycle based on the natural day marks, and divide the collection cycle into a month-in segment collection cycle and a neighboring month segment collection cycle when the collection point count values in the month are not equal to a collection cycle standard count value; The collection point count value comparison subunit is connected with the time judgment subunit and the segment collection cycle division subunit and configured to acquire collection point count values in the month-in segment collection cycle and neighboring month collection point count values in the neighboring month segment collection cycle, determine a complete collection cycle number, corresponding number marks and actual start and end time of the current month based on a comparison result of the collection point count values in the month and the neighboring month.

[0014] Further, the time sequence generation module comprises a complete collection cycle number acquisition unit, a sorting unit and a temporary storage unit; The complete collection cycle number acquisition unit is configured to acquire number marks of each complete collection cycle and natural day marks of each collection point in the complete collection cycle; The sorting unit is connected with the complete collection cycle number acquisition unit and configured to obtain each type of basic business data after time sorting according to a complete collection cycle number mark sorting method based on time sequence and a collection point natural day mark sorting method based on time sequence; The temporary storage unit is connected with the sorting unit and configured to buffer the each type of basic business data after time sorting.

[0015] Further, the heat map generation module comprises a week average index conversion unit and a heat calculation unit; The week average index conversion unit is configured to calculate week average indexes of each complete collection period based on the various types of basic business data corresponding to each collection point in each complete collection period. The heat calculation unit is connected to the week average index conversion unit and configured to convert the week average indexes of each complete collection period into heat values corresponding to the week average indexes.

[0016] Further, the heat map generation module further includes a heat map generation unit. The heat map generation unit is connected to the heat calculation unit and configured to generate a heat map of the various types of business data of the current month according to the number of complete collection periods and corresponding numbered labels based on the heat values corresponding to the week average indexes.

[0017] Further, the business judgment module includes a feature construction unit. The feature construction unit is configured to calculate a global feature value set of the period feature values corresponding to each complete collection period according to the basic business data, including a total inventory amount relative baseline ratio, a total sales amount relative baseline ratio, a total personnel production amount relative baseline ratio, a monthly trend slope of a sales data sequence, a monthly fluctuation rate of a per capita production and sales rate sequence, and a monthly fluctuation rate of a stock consumption rate sequence.

[0018] Further, the business judgment module further includes a condition judgment unit. The condition judgment unit is connected to the feature construction unit and configured to receive the global feature value set, obtain a category of a supply and demand relationship according to a supply and demand relationship judgment standard, and perform a corresponding supply and demand threshold comparison judgment when the supply and demand relationship belongs to a first supply and demand relationship or a third supply and demand relationship, to obtain an enterprise business judgment result corresponding to a threshold comparison judgment result.

[0019] Further, the business judgment module further includes a reference strategy generation unit. The reference strategy generation unit is connected to the condition judgment unit and configured to obtain each reference strategy corresponding to the enterprise business judgment result according to a reference strategy reference table.

[0020] Further, the label supplement module includes a type mapping unit and a label unit. The type mapping unit is configured to map the enterprise business judgment result into a graphical symbol. The label unit is connected to the type mapping unit and configured to supplement the graphical symbol in the heat map name label of each heat map.

[0021] ​Compared with the prior art, the beneficial effects of the present application are that, by taking a natural day as a collection point, taking a week as a collection period to obtain sales data, inventory data and personnel production data, and based on the collection point count to determine the division of the month segment collection period and the adjacent month segment collection period, the complete collection period and the actual start and end time are determined, the accuracy of the enterprise operation data period division is realized, and the analysis deviation caused by data cross-period or omission is avoided; combined with the time sequence sorting and cache storage of the basic operation data in each complete collection period, the continuity and traceability of the data are ensured, and a reliable foundation is provided for subsequent analysis; by calculating the weekly average index of each collection point in each period and converting it into the corresponding heat value to generate a heat map, the enterprise operation data is intuitively presented, which is convenient for quickly discovering abnormal fluctuations and potential problems; further combined with the global feature value set construction and supply-demand threshold comparison, the dynamic identification of oversupply or undersupply state is realized, so that the abnormal operation state can be discovered in time; finally, by mapping the judgment result to a graphical symbol and labeling it in the heat map, the close combination of visual analysis and decision logic is realized, so that the managers can intuitively master various types of enterprise operation data state, optimize resource allocation, improve decision efficiency and management accuracy.

[0022] Further, by obtaining the sales data, inventory data and personnel production data in each collection period of the current month, and judging the collection point count in the month, the integrity of the collection period division is realized, and the influence of data loss on subsequent analysis is avoided.

[0023] Further, by comparing the collection point count values of the month segment collection period and the adjacent month segment collection period, the complete collection period and the corresponding number mark are accurately determined, the data period division is refined, and an accurate time division basis is provided for time sequence sorting and heat map generation. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 FIG. 1 is a structural schematic diagram of an enterprise operation data detection and visual analysis system according to an embodiment of the present application; Figure 2 FIG. 2 is a logic judgment diagram of a segment collection period division subunit according to an embodiment of the present application; Figure 3 FIG. 3 is a structural schematic diagram of a heat map generation module according to an embodiment of the present application; Figure 4 FIG. 4 is a logic judgment diagram of a condition judgment unit according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose and advantages of the present application clearer and more apparent, the present application will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0026] The preferred embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art will appreciate that the embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0027] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0028] In addition, it should be further noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0029] Please refer to Figure 1 The present application provides an enterprise operation data detection and visualization analysis system, which comprises: The data acquisition module is used to acquire the basic operation data in each collection period of the current month; The time sequence generation module is connected with the data acquisition module and is used to store the basic operation data of each collection point in the collection period after time sorting; The heat map generation module is connected with the time sequence generation module and is used to calculate the weekly average index based on the time sequence data of each complete collection period, convert it into the corresponding heat value, and generate the heat map of each type of enterprise operation data in the current month; The operation judgment module is connected with the data acquisition module and the heat map generation module, and is used to construct a global feature value set of the basic operation data, judge the supply and demand relationship corresponding to the global feature value set based on the supply and demand relationship judgment standard, and perform corresponding supply and demand threshold comparison judgment when the first supply and demand relationship or the third supply and demand relationship is obtained, to obtain the enterprise operation judgment result; The label supplement module is connected with the heat map generation module and the operation judgment module respectively, and is used to map the enterprise operation judgment result into a graphical symbol and supplement the label in the corresponding heat map.

[0030] In this embodiment, through the deployment of POS machines, RFID readers, industrial sensors and other data collection devices in enterprise sales terminals, warehouses and production sites, daily sales data, inventory change data and personnel production efficiency data are obtained. These devices transmit the collected data to the central server through the enterprise local area network; The server has a built-in data processing program. The data sorting program arranges the received period number and date mark in chronological order and temporarily stores the sorted data in the server memory.

[0031] By taking natural days as collection points and weeks as collection periods to obtain sales data, inventory data and personnel production data, and based on the count of collection points to determine the division of the month segment collection period and the adjacent month segment collection period, the complete collection period and the actual start and end time are determined, the accuracy of enterprise operation data period division is realized, and the analysis deviation caused by data cross-period or omission is avoided; combined with the sorting and cache storage of the basic operation data in each complete collection period according to the time sequence, the continuity and traceability of the data are ensured, providing a reliable foundation for subsequent analysis; by calculating the weekly average index of each collection point in each period and converting it into the corresponding heat value to generate a heat map, the enterprise operation data is intuitively presented, which is convenient for quickly discovering abnormal fluctuations and potential problems; further combined with the construction of global feature value set and the comparison of supply and demand threshold, the dynamic identification of oversupply or undersupply state is realized, so that the abnormal operation state can be discovered in time; finally, by mapping the judgment result to a graphical symbol and labeling it in the heat map, the close combination of visual analysis and decision logic is realized, so that managers can intuitively master various types of enterprise operation data state, optimize resource allocation, improve decision-making efficiency and management accuracy.

[0032] Specifically, the data acquisition module includes a calling unit and a complete collection period number determination unit; The calling unit is used to acquire basic operation data including sales data, inventory data and personnel production data. The complete collection period number determination unit is connected to the calling unit and is used to determine the number of complete collection periods and the corresponding number mark, as well as the actual start and end time of the current month.

[0033] In this embodiment, the data acquisition module includes a calling unit and a complete collection period number determination unit; the calling unit is used to acquire basic operation data, including sales data, inventory data and personnel production data; the time point corresponding to each basic operation data is the collection point, and the basic operation data collected by the collection point is various types of basic operation data collected on the same day. The complete collection cycle number determination unit is used to obtain ideal start and end time of the current month and natural day marks of each collection point, wherein the ideal start and end time is that the first day of the current month is the ideal start time and the last day is the ideal end time; Based on the comparison of the monthly collection point count value in each collection cycle and the collection cycle standard count value, the collection cycle is divided into monthly segment collection cycle and adjacent month segment collection cycle; The collection cycle is by default a unit cycle from Monday to Sunday; The complete collection cycle judgment basis is whether all collection points in the collection cycle fall within the current month; The collection cycle standard count value is 7, that is, each week should contain 7 collection points; The adjacent month segment collection cycle refers to the time period in which part of the collection points are scattered in the previous month or the next month in a weekly collection cycle; Based on the comparison result, the number of complete collection cycles is determined, and the numbering marks are generated in time sequence, such as the first complete collection cycle, the second complete collection cycle, etc.

[0034] By obtaining the sales data, inventory data and personnel production data in each collection cycle of the current month, and judging the monthly collection point count, the integrity of the collection cycle division is realized, and the influence of data loss on subsequent analysis is avoided.

[0035] Referring to Figure 2 As shown in the figure, it is a logic judgment diagram of the segment collection cycle division subunit of the embodiment of the present application; Specifically, the complete collection cycle number determination unit includes a time judgment subunit, a segment collection cycle division subunit and a collection point count value comparison subunit; The time judgment subunit is used to obtain the ideal start and end time of the current month, and the natural day marks of each collection cycle in the current month and the collection points in the collection cycle; The segment collection cycle division subunit is connected with the time judgment subunit, and is used to judge the monthly collection point count value in each collection cycle based on the natural day marks, and divide the collection cycle into monthly segment collection cycle and adjacent month segment collection cycle when the monthly collection point count value is not equal to the collection cycle standard count value; The collection point count value comparison subunit is connected with the time judgment subunit and the segment collection cycle division subunit, and is used to obtain the monthly collection point count value of the monthly segment collection cycle and the adjacent month collection point count value in the adjacent month segment collection cycle, determine the number of complete collection cycles and the corresponding numbering marks based on the comparison result of the monthly collection point count value and the adjacent month collection point count value, and the actual start and end time of the current month.

[0036] In this embodiment, the natural day marker refers to the date marker corresponding to each collection point; The intra-month collection point refers to the collection point corresponding to the collection period falling within the current month, that is, the natural day marker of the collection point belongs to the current month; The intra-month segment collection period refers to the time segment formed by all collection points belonging to the current month in one collection period; and the adjacent month segment collection period refers to the time segment formed by the remaining collection points falling in the adjacent month in the same collection period; In the intra-month segment collection period and the adjacent month segment collection period, each collection point has a corresponding collection point count value, that is, the number of collection points actually contained in the segment; The intra-month collection point count value is compared with the adjacent month collection point count value. If the intra-month collection point count value is greater than the adjacent month collection point count value, it is determined that the main collection points in the collection period are concentrated in the current month, and the collection period as a whole is attributed to the complete collection period of the current month. If the intra-month collection point count value is less than or equal to the adjacent month collection point count value, it is determined that the main collection points in the collection period are concentrated in the adjacent month, and the collection period as a whole is attributed to the adjacent month segment collection period.

[0037] By comparing the collection point count values of the intra-month segment collection period and the adjacent month segment collection period, the complete collection period and the corresponding number marker are determined, the data period division is refined, and accurate time division basis is provided for time series sorting and heat map generation.

[0038] Specifically, the time series generation module includes a complete collection period number acquisition unit, a sorting unit, and a temporary storage unit. The complete collection period number acquisition unit is configured to acquire the number marker of each complete collection period and the natural day marker of each collection point in the complete collection period. The sorting unit is connected with the complete collection period number acquisition unit and is configured to obtain various time-ordered basic business data in the actual start and end time of the current month according to a complete collection period number marker sorting method based on time sequence and a collection point natural day marker sorting method based on time sequence. The temporary storage unit is connected with the sorting unit and is configured to buffer the various time-ordered basic business data.

[0039] In this embodiment, the complete collection period number marker sorting method based on time sequence is that the number markers of the complete collection periods are arranged in ascending order, and the smaller the number marker value, the earlier the corresponding complete collection period. The method for sorting the natural day markers of the collection points based on the time sequence is as follows: within the same complete collection cycle, the natural day markers of each collection point are arranged in ascending order of value. The natural day marker with a smaller value corresponds to an earlier natural day, and the natural day marker with a larger value corresponds to a later natural day, thus forming a strict day sequence arrangement. The temporary storage unit caches the sorted basic operating data. The cache location is a time series buffer that corresponds one-to-one with the natural day marker. The data structure of the buffer is consistent with the aforementioned natural day marker. All types of basic operating data can be read continuously in chronological order when called.

[0040] By sequentially processing the numbering markers obtained by the complete collection cycle numbering acquisition unit and the natural day markers of each collection point, the continuity and accuracy of basic operating data in the time dimension are ensured; providing a continuous and complete time series for subsequent data analysis and visualization.

[0041] Specifically, the heat map generation module includes a weekly average index conversion unit and a heat calculation unit; The weekly average indicator conversion unit is used to calculate the weekly average indicator for each complete collection period based on the various basic operating data corresponding to each collection point in each complete collection period. The thermal calculation unit is connected to the weekly average index conversion unit to convert the weekly average index of each complete collection cycle into the thermal value corresponding to each weekly average index.

[0042] In this embodiment, the weekly average index refers to the average value calculated by statistically analyzing the basic operating data corresponding to each collection point within a complete collection cycle, with Monday to Sunday as the unit cycle; Basic operating data includes sales data, inventory data, and personnel production data. The values ​​corresponding to each natural day are summed, and then divided by the number of collection points in that week to obtain the weekly average index. The conversion of weekly average indicators to thermal values ​​is done by linear mapping. The maximum and minimum values ​​of the weekly average indicators in the current month are determined. Then, the weekly average indicators of each complete collection cycle are proportionally converted according to their position between the maximum and minimum values ​​and mapped to the preset thermal display range, such as the color intensity range of 0 to 1 or 0 to 255. After conversion, the weekly average index with higher values ​​corresponds to cells with darker colors or higher display intensity in the heatmap, while the weekly average index with lower values ​​corresponds to cells with lighter colors or lower display intensity. In the process, different types of basic business data, such as sales data, inventory data, personnel production data, are respectively subjected to the same mapping processing to generate independent heat maps, and each heat map is marked with a complete acquisition cycle number and a heat map name.

[0043] By counting the basic business data of each acquisition point in each complete acquisition cycle and calculating the weekly average index, the weekly average index is mapped to the heat value, and the concentration and fluctuation of the sales data, inventory data and personnel production data in each weekly cycle are visually expressed.

[0044] Referring to Figure 3 Fig. 1 is a structural schematic diagram of a heat map generation module according to an embodiment of the present application; Specifically, the heat map generation module further comprises a heat map generation unit. The heat map generation unit is connected to the heat calculation unit, and is used to generate a heat map corresponding to each type of enterprise operation data in the current month based on the heat value corresponding to each weekly average index and the complete acquisition cycle number and corresponding marking.

[0045] In this embodiment, an independent heat map is generated for each type of enterprise operation data. After arranging the heat values corresponding to each complete acquisition cycle in order of the complete acquisition cycle number, the heat values are mapped to a color gradient and drawn on a two-dimensional chart using a visualization drawing tool, each row or column corresponds to a complete acquisition cycle, and each cell corresponds to the heat value of the period. After drawing, the complete acquisition cycle number and the heat map name are marked in the chart to form a visual heat map of each type of enterprise operation data in the current month.

[0046] By generating an independent heat map for each type of enterprise operation data, arranging the heat values corresponding to each complete acquisition cycle in order of the complete acquisition cycle number, and then mapping the heat values to a color gradient and drawing them on a two-dimensional chart using a visualization drawing tool, the parallel and intuitive visualization of multi-dimensional operation data is achieved.

[0047] Specifically, the operation judgment module comprises a feature construction unit. The feature construction unit is used to calculate a global feature value set of the period feature values corresponding to each complete acquisition cycle, including the total inventory relative baseline ratio, the total sales relative baseline ratio, the total personnel production relative baseline ratio, the monthly trend slope of the sales data sequence, the monthly volatility rate of the per capita production and sales rate sequence, and the monthly volatility rate of the inventory consumption rate sequence.

[0048] In this embodiment, the global feature value set includes the following six parameters, which are all calculated based on the actual start and end time of the current month and the basic business data in each complete acquisition cycle. Total inventory relative to baseline ratio: the total inventory collected from all collection points in the month is accumulated to obtain the total inventory in the month, and a ratio is calculated with the median of the historical inventory in the same period, reflecting the deviation of the inventory level relative to the historical inventory; Total sales relative to baseline ratio: the total sales data of each week in the month is accumulated to obtain the total sales in the month, and a ratio is calculated with the median of the historical sales in the same period, reflecting the deviation of the sales level relative to the historical sales; Total personnel production relative to baseline ratio: the total personnel production data of each week in the month is accumulated to obtain the total personnel production in the month, and a ratio is calculated with the median of the historical personnel production in the same period, reflecting the deviation of the production capacity; Monthly trend slope of sales data sequence: the slope value is obtained by linear fitting of the sales data points of each week in the month in time sequence, for describing the upward or downward trend of sales data over time; Monthly volatility of per capita production and sales rate sequence: the standard deviation of the ratio of sales volume to total personnel production in each complete collection period in the month is calculated, for measuring the stability of production and sales efficiency; Monthly volatility of inventory consumption rate sequence: the standard deviation of the ratio of sales volume to total inventory in each complete collection period in the month is calculated, for measuring the stability of inventory consumption; The time range of the historical same period can be selected according to the historical data accumulation of the enterprise operation and the industry characteristics, and usually one to five years of data are selected; in the embodiment, the historical same period is selected as the data set of the corresponding months in the past three years; the median of the historical same period inventory, the median of the total sales and the median of the total personnel production are calculated based on the selected data set.

[0049] By constructing the global feature value set, the business operation state is stereoscopically and quantitatively extracted from multiple dimensions such as macro total, historical comparison, trend direction and volatility stability, the complex original data is converted into feature indexes with clear business significance, and the one-sidedness of single index analysis is avoided.

[0050] Referring to Figure 4 As shown in the figure, it is a logic judgment diagram of the condition judgment unit of the embodiment of the application; Specifically, the operation judgment module further includes a condition judgment unit; The condition judgment unit is connected with the feature construction unit to receive the global feature value set, obtain the category to which the supply and demand relationship belongs according to the supply and demand relationship judgment standard, and perform corresponding supply and demand threshold comparison judgment when the supply and demand relationship belongs to the first supply and demand relationship or the third supply and demand relationship, to obtain the enterprise operation judgment result corresponding to the threshold comparison judgment result.

[0051] In this embodiment, the supply-demand relationship is determined according to the total inventory quantity relative to the baseline, the total sales quantity relative to the baseline, and the total production quantity relative to the baseline in the global feature value set. The supply-demand relationship determination criteria are as follows: If the total inventory quantity relative to the baseline is greater than a first inventory threshold, and the total sales quantity relative to the baseline is less than a first sales threshold, it is determined that the first supply-demand relationship exists, i.e., supply exceeds demand. If the total inventory quantity relative to the baseline is less than a second inventory threshold, and the total sales quantity relative to the baseline is greater than a second sales threshold, it is determined that the third supply-demand relationship exists, i.e., demand exceeds supply. If it does not belong to the first supply-demand relationship or the third supply-demand relationship, it is determined that the second supply-demand relationship exists, i.e., supply and demand are balanced, and there is no subsequent comparison operation. The value ranges of the first inventory threshold, the first sales threshold, the second inventory threshold, and the second sales threshold can be adjusted within a preset interval according to the industry average level and the enterprise operation strategy. In this embodiment, the value of the first inventory threshold is 1.2, the value of the first sales threshold is 0.9, the value of the second inventory threshold is 0.8, and the value of the second sales threshold is 1.1. The enterprise operation determination results include four types of first enterprise operation determination results, second enterprise operation determination results, third enterprise operation determination results, and fourth enterprise operation determination results, which correspond to different operation states. If it is determined that the first supply-demand relationship exists, the supply-demand threshold corresponding to the first supply-demand relationship is compared with the monthly fluctuation rate of the inventory consumption rate sequence and the preset inventory turnover stability threshold. The monthly fluctuation rate of the inventory consumption rate sequence is compared with the preset inventory turnover stability threshold. If the inventory consumption rate fluctuation rate is greater than the inventory turnover stability threshold, it means that supply exceeds demand and the inventory turnover is unstable, and the first enterprise operation determination result is obtained, i.e., inventory accumulation and turnover disorder. If the inventory consumption rate fluctuation rate is less than or equal to the inventory turnover stability threshold, it means that supply exceeds demand but the inventory turnover is stable, and the second enterprise operation determination result is obtained, i.e., inventory accumulation. The value of the inventory turnover stability threshold can be determined by analyzing the stable state of the historical operation of the enterprise. In this embodiment, the value of the inventory turnover stability threshold is 0.15. If it is determined that the third supply-demand relationship exists, the supply-demand threshold corresponding to the first supply-demand relationship is compared with the monthly fluctuation rate of the production and sales rate per capita sequence and the preset production efficiency stability threshold. The monthly fluctuation rate of the production and sales rate per capita sequence is compared with the preset production efficiency stability threshold. If the fluctuation rate of the per capita production and sales rate is greater than the production efficiency stability threshold, at this time, the supply cannot meet the demand and the production efficiency fluctuates greatly, the third enterprise operation judgment result is obtained, that is, the demand is strong but the production efficiency is unstable; If the fluctuation rate of the per capita production and sales rate is less than or equal to the production efficiency stability threshold, at this time, the supply cannot meet the demand but the production capacity is stable, the fourth enterprise operation judgment result is obtained, that is, the stable production capacity is insufficient. The value of the production efficiency stability threshold can be determined by analyzing the fluctuation of the historical production efficiency of the enterprise. In this embodiment, the value of the production efficiency stability threshold is 0.1.

[0052] By determining the supply and demand relationship category according to the global feature value set, and further executing the stability threshold comparison of the inventory consumption rate or the per capita production and sales rate under the first and third supply and demand relationship, the fine and deep attribution analysis of the two states of oversupply and undersupply is realized, and more operational decision support is provided for managers.

[0053] Specifically, the operation judgment module further comprises a reference strategy generation unit; The reference strategy generation unit is connected with the condition judgment unit, and is used to match the reference strategies according to the reference strategy matching table, and obtain the reference strategies corresponding to the enterprise operation judgment result.

[0054] In this embodiment, the reference strategy matching table is a pre-set database table, which stores a plurality of reference strategies corresponding to the enterprise operation judgment result, and when the second enterprise operation judgment result is obtained, a plurality of reference strategies are matched from the matching table; For example, when the second enterprise operation judgment result is obtained, three reference strategies of "starting a promotion plan", "adjusting production shifts" and "optimizing warehouse layout" are matched from the matching table; When the fourth enterprise operation judgment result is obtained, two reference strategies of "increasing production personnel" and "extending operation time" are matched from the matching table; The reference strategy generation unit takes the matched strategy list as the output reference strategy to assist the decision-making layer in decision-making.

[0055] By converting data into operable reference strategies, the excessive dependence on the personal experience of managers is effectively reduced, a standardized and scientific solution library is provided for typical management problems such as inventory backlog and insufficient production capacity, and the decision-making path is significantly shortened.

[0056] Specifically, the label supplement module comprises a type mapping unit and a label unit. The type mapping unit is used to map the enterprise operation judgment result into a graphical symbol; The marking unit is connected to the type mapping unit, and is used for marking the graphic symbol in the heat map name after marking the heat map in each heat map.

[0057] In the embodiment, the type mapping unit is built-in mapping rules, and each enterprise operation judgment result is converted into a unique graphic symbol. The marking unit receives the graphic symbol and the heat map image, and dynamically superimposes and renders the graphic symbol on the right side of the heat map title, and the original name together constitutes a complete legend. The first enterprise operation judgment result is marked as ●, the second enterprise operation judgment result is marked as ▲, the third enterprise operation judgment result is marked as ▼, and the fourth enterprise operation judgment result is marked as ■. For example, the first enterprise operation sales data heat map ●, the second enterprise operation inventory data heat map ▲, the third enterprise operation personnel production data heat map ▼, and the fourth enterprise operation financial data heat map ■.

[0058] By superimposing the corresponding graphic symbol at the end of the heat map title, the intuitive distinction of the enterprise operation judgment result is realized, the recognition efficiency and analysis accuracy of data visualization are improved, and the management personnel can quickly understand and make decisions.

[0059] Thus, the technical solutions of the present application have been described in connection with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0060] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A system for detecting and visualizing enterprise operational data, characterized in that, include: The data acquisition module is used to acquire basic operating data for each collection period in the current month; The time series generation module, connected to the data acquisition module, is used to store the basic operating data of each collection point within the collection period after sorting by time. The heat map generation module is connected to the time series generation module. It is used to calculate the weekly average index based on the time series data of each complete collection period, convert it into the corresponding heat value, and generate a heat map of various types of enterprise operating data for the current month. The business judgment module is connected to the data acquisition module and the heat map generation module. It is used to construct a global feature value set of basic business data, judge the supply and demand relationship corresponding to the global feature value set based on the supply and demand relationship judgment standard, and perform the corresponding supply and demand threshold comparison judgment when the first supply and demand relationship or the third supply and demand relationship is obtained, so as to obtain the business judgment result of the enterprise. The labeling supplement module is connected to the heatmap generation module and the business judgment module respectively, and is used to map the business judgment results of the enterprise into graphic symbols and supplement the labels in the corresponding heatmap.

2. The enterprise operation data detection and visualization analysis system according to claim 1, characterized in that, The data acquisition module includes a calling unit and a complete collection cycle number determination unit; The calling unit is used to obtain basic operational data, including sales data, inventory data, and personnel production data. The complete collection cycle number determination unit, connected to the calling unit, is used to determine the complete collection cycle number and its corresponding number marker, as well as the actual start and end times of the current month.

3. The enterprise operation data detection and visualization analysis system according to claim 2, characterized in that, The complete acquisition cycle number determination unit includes a time determination subunit, a segment acquisition cycle division subunit, and an acquisition point count value comparison subunit; The time determination subunit is used to obtain the ideal start and end times of the current month, and the natural day markers of the collection points in each collection cycle within the current month. The segment collection cycle division subunit is connected to the time determination subunit. It is used to determine the monthly collection point count value within each collection cycle based on the natural day mark. When the monthly collection point count value is not equal to the standard count value of the collection cycle, the collection cycle is divided into the monthly segment collection cycle and the adjacent monthly segment collection cycle. The sampling point count comparison subunit is connected to the time determination subunit and the segment sampling cycle division subunit. It is used to obtain the monthly sampling point count value of the monthly segment sampling cycle and the adjacent monthly sampling point count value of the adjacent monthly segment sampling cycle. Based on the comparison result of the monthly sampling point count value and the adjacent monthly sampling point count value, the number of complete sampling cycles and the corresponding number mark, as well as the actual start and end time of the current month are determined.

4. The enterprise operation data detection and visualization analysis system according to claim 1, characterized in that, The time series generation module includes a complete acquisition period number acquisition unit, a sorting unit, and a temporary storage unit; The complete collection cycle number acquisition unit is used to obtain the number mark of each complete collection cycle and the natural day mark of each collection point within the complete collection cycle; The sorting unit, connected to the complete collection cycle number acquisition unit, obtains basic operating data sorted by time for various types of data within the actual start and end time of the current month, based on the complete collection cycle number marking sorting method based on time sequence and the natural day marking sorting method based on time sequence of collection points. A temporary storage unit, connected to the sorting unit, is used to cache the various types of time-sorted basic operational data.

5. The enterprise operation data detection and visualization analysis system according to claim 1, characterized in that, The heat map generation module includes a weekly average index conversion unit and a heat calculation unit; The weekly average index conversion unit is used to calculate the weekly average index for each complete collection period based on the various basic operating data corresponding to each collection point in each complete collection period. The thermal calculation unit is connected to the weekly average index conversion unit to convert the weekly average index of each complete collection cycle into the thermal value corresponding to each weekly average index.

6. The enterprise operation data detection and visualization analysis system according to claim 5, characterized in that, The heat map generation module further includes a heat map generation unit; The heat map generation unit, connected to the heat calculation unit, is used to generate heat maps corresponding to various types of enterprise operating data for the current month based on the heat values ​​corresponding to the weekly average indicators, according to the number of complete collection cycles and the corresponding number markings.

7. The enterprise operation data detection and visualization analysis system according to claim 1, characterized in that, The business judgment module includes a feature construction unit; The feature construction unit is used to calculate a global feature set based on basic operating data, including the ratio of total inventory to baseline, the ratio of total sales to baseline, the ratio of total personnel production to baseline, the monthly trend slope of the sales data series, the monthly volatility of the per capita production and sales rate series, and the monthly volatility of the inventory consumption rate series.

8. The enterprise operation data detection and visualization analysis system according to claim 7, characterized in that, The operational judgment module also includes a condition judgment unit; The condition judgment unit, connected to the feature construction unit, is used to receive the global feature value set, obtain the category to which the supply and demand relationship belongs according to the supply and demand relationship judgment criteria, and perform the corresponding supply and demand threshold comparison judgment when the supply and demand relationship belongs to the first supply and demand relationship or the third supply and demand relationship, so as to obtain the enterprise operation judgment result corresponding to the threshold comparison judgment result.

9. The enterprise operation data detection and visualization analysis system according to claim 8, characterized in that, The operational judgment module also includes a reference strategy generation unit; A reference strategy generation unit, connected to the condition judgment unit, is used to generate a reference strategy based on a reference strategy lookup table. Obtain the reference strategies corresponding to the business judgment results of the enterprise.

10. The enterprise operation data detection and visualization analysis system according to claim 1, characterized in that, The tag supplementation module includes a type mapping unit and a tag unit; Type mapping unit, used to map the results of business judgments into graphic symbols; A marking unit, connected to the type mapping unit, is used to supplement the graphic symbols by marking them after the heatmap name in each heatmap.

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