Indoor home layout design management system based on big data analysis technology

Abstract

The invention belongs to the technical field of home layout management, and particularly relates to an indoor home layout design management system based on a big data analysis technology, which comprises a user data collection and analysis module, a home product database, a home layout design module, a design preview adjustment module and an order management service module. According to the invention, a plurality of feasible home layout design schemes are automatically generated through the home layout design module based on the user portrait and the home product database, and the home layout design module updates the design schemes in real time according to the adjustment of the user; after a final home layout design scheme is determined, home product order information of a user is processed through the order management service module, intelligentization, individuation and high efficiency of home layout design are achieved, the design efficiency and the user satisfaction degree are improved, the background supervision difficulty degree is reasonably evaluated through the supervision comprehensive analysis early warning module, and the user experience is improved. And subsequent management is enhanced in time, and the system application public praise can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of home furnishing layout management, and in particular to an indoor home furnishing layout design management system based on big data analysis technology. Background Art

[0002] The traditional home layout design process often relies on the designer's personal experience and aesthetics, lacking scientificity and personalization. With the continuous development of science and technology, smart home management systems and big data technologies have been widely used in modern life.

[0003] A home simulation design system is disclosed in a Chinese invention patent with publication number CN105787230A. The invention provides customers with a simple and easy-to-use home simulation design method with high design accuracy and realistic simulation scenes, which is suitable for the trend of personalized, networked and convenient home simulation design.

[0004] However, in actual application, the above invention technical solution is difficult to combine user data collection, big data analysis, intelligent design, user interaction and home product order processing to further improve the efficiency and intelligence of indoor home layout design, and it is impossible to reasonably analyze and accurately judge the degree of difficulty of background supervision, and it is difficult to strengthen management in time and take corresponding optimization and improvement measures, which is not conducive to improving the reputation of system application;

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] The purpose of the present invention is to provide an interior home layout design management system based on big data analysis technology, which solves the problem that the existing technology is difficult to combine user data collection, big data analysis, intelligent design, user interaction and home product order processing to further improve the efficiency and intelligence of interior home layout design, and is unable to reasonably analyze and accurately judge the difficulty of background supervision, which is not conducive to improving the reputation of system application.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An indoor home layout design management system based on big data analysis technology, including a user data collection and analysis module, a home product database, a home layout design module, a design preview adjustment module, and an order management service module;

[0009] The user sends an indoor home layout design request through the user terminal. When the user data collection and analysis module receives the indoor home layout design request, it collects the user's basic information and user preferences, uses big data analysis technology to perform in-depth mining to form a user portrait, and sends the user portrait to the home layout design module;

[0010] The home product database stores detailed information on various types of home products. The home layout design module automatically generates multiple feasible home layout design solutions based on user portraits and the home product database using machine learning algorithms and optimization techniques.

[0011] The design preview adjustment module previews and displays the generated home layout design plan. The user adjusts the home layout by dragging and zooming. The home layout design module updates the design plan in real time according to the user's adjustment. After determining the final home layout design plan, the user places the corresponding home product order through the user terminal, and the order management service module is responsible for processing the user's home product order information.

[0012] Furthermore, it also includes a background supervision end, the background supervision end is connected to the supervision comprehensive analysis and early warning module in communication, the supervision comprehensive analysis and early warning module is used to set the detection period, obtain the processing evaluation symbol GX-1 or GX-2 and the operation evaluation symbol LP-1 or LP-2 of the detection period;

[0013] If GX-2∩LP-2 is obtained, a low-difficulty supervision signal is generated. In other cases, a high-difficulty supervision signal is generated, and the high-difficulty supervision signal or the low-difficulty supervision signal is sent to the background supervision end. When the background supervision end receives the high-difficulty supervision signal, it will issue a corresponding warning.

[0014] Furthermore, the supervision comprehensive analysis and early warning module is connected to the order processing evaluation module and the design operation analysis module in communication. The order processing evaluation module monitors and analyzes the processing process of the home product order during the detection period, assigns a processing evaluation symbol GX-1 or GX-2 through analysis, and sends the processing evaluation symbol GX-1 or GX-2 to the supervision comprehensive analysis and early warning module;

[0015] The design operation analysis module analyzes the design operation performance of the interior home layout design during the detection period, assigns an operation evaluation symbol LP-1 or LP-2 through the analysis, and sends the operation evaluation symbol LP-1 or LP-2 to the supervision comprehensive analysis and early warning module.

[0016] Furthermore, the specific analysis process of the order processing evaluation module includes:

[0017] When a user inquires about order information, the time when the user inquires is collected and marked as the first time, and the time when the customer service responds is collected and marked as the second time, and the interval between the first time and the second time is marked as the response time;

[0018] Obtain all response times during the detection period, mark the number of response times that exceed the corresponding preset response time threshold as a response abnormality, and calculate the average of all response times during the detection period to obtain a response performance value;

[0019] All customer service response texts during the test period are scanned to identify uncivilized language, and the total number of occurrences of uncivilized language in the customer service response process during the test period is collected and marked as the uncivilized response frequency;

[0020] The customer service management abnormality coefficient is obtained by numerically calculating the response abnormality value, response performance value and uncivilized response frequency, and the customer service management abnormality coefficient is numerically compared with the preset customer service management abnormality coefficient threshold. If the customer service management abnormality coefficient exceeds the preset customer service management abnormality coefficient threshold, the processing evaluation symbol GX-1 is assigned.

[0021] Furthermore, if the customer service management abnormality coefficient does not exceed the preset customer service management abnormality coefficient threshold, the cooperation non-optimal values ​​of all cooperating home product suppliers are obtained, and the cooperation non-optimal values ​​are numerically compared with the preset cooperation non-optimal threshold. If the cooperation non-optimal value exceeds the preset cooperation non-optimal threshold, the corresponding home product supplier is marked as a strict supervision object;

[0022] The percentage of home furnishing product suppliers marked as strict supervision objects during the inspection period is obtained and marked as the strict supervision value, and the cooperation non-optimal values ​​of all home furnishing product suppliers are averaged to obtain the cooperation abnormal value, and the cooperation non-optimal value with the largest value is marked as the cooperation abnormal value;

[0023] The supply management anomaly coefficient is obtained by numerically calculating the strict control numerical inspection value, the cooperation abnormal table value and the cooperation abnormal amplitude value, and the supply management anomaly coefficient is numerically compared with the preset supply management anomaly coefficient threshold. If the supply management anomaly coefficient exceeds the preset supply management anomaly coefficient threshold, the processing evaluation symbol GX-1 is assigned; if the supply management anomaly coefficient does not exceed the preset supply management anomaly coefficient threshold, the processing evaluation symbol GX-2 is assigned.

[0024] Furthermore, the order processing evaluation module is communicated with the partner detection and analysis module, which obtains all cooperating home furnishing product suppliers, marks the corresponding home furnishing product supplier as partner i, and i is a natural number greater than 1; analyzes the performance of the entire supply process of partner i during the detection period, obtains the cooperation non-optimal value of partner i through analysis, and sends the cooperation non-optimal value of partner i to the order processing evaluation module.

[0025] Furthermore, the specific analysis process of the partner detection and analysis module is as follows:

[0026] The number of household product orders involving partner i during the detection period is collected and marked as the order number inspection value, the number of times partner i is complained by users during the detection period is marked as the supply complaint detection value, and the number of times partner i fails to deliver the corresponding household products to the user's location within the corresponding specified time and the number of times the household products are damaged during the delivery process are marked as product delivery anomaly value and product delivery damage value, respectively. The cooperation non-optimal value of partner i is obtained by numerically calculating the order number inspection value, supply complaint detection value, product delivery anomaly value and product delivery damage value.

[0027] Furthermore, the specific analysis process of the design and operation analysis module includes:

[0028] The number of times the system crashes and cannot operate normally during the detection period is obtained and marked as an abnormal operation inspection value, and the single duration of the system crash and cannot operate normally is marked as an abnormality coefficient, all abnormality coefficients during the detection period are summed up to obtain an abnormality total value, and the number of abnormality coefficients exceeding a preset abnormality coefficient threshold during the detection period is marked as an abnormality risk table value;

[0029] and collecting the real-time running number of the design task and marking it as the running congestion value, comparing the running congestion value with the preset running congestion threshold, if the running congestion value exceeds the preset running congestion threshold, judging that the system is in a non-smooth running state, obtaining the total time the system is in the non-smooth running state during the detection period and marking it as the non-smooth detection value;

[0030] The operation hazard value is obtained by numerically calculating the non-smooth detection value, abnormal operation inspection value, abnormal total condition value and abnormal risk table value, and the operation hazard value is numerically compared with the preset operation hazard threshold. If the operation hazard value exceeds the preset operation hazard threshold, the operation assessment symbol LP-1 is assigned.

[0031] Furthermore, if the operation hazard value does not exceed the preset operation hazard threshold, the design task corresponding to the home layout design request is monitored throughout the entire process, and the number of times the user adjusts the plan within the interval between the time when the request is issued and the time when the final plan is determined for the corresponding design task is collected and marked as the frequency modulation detection value, and the average of all frequency modulation detection values ​​within the detection period is calculated to obtain the frequency modulation performance value;

[0032] And the number of frequency modulation detection values ​​that exceed the preset frequency modulation detection threshold during the detection period is marked as frequency modulation anomaly values, and the operation evaluation value is obtained by numerically calculating the operation potential danger value, the frequency modulation performance value and the frequency modulation anomaly value. The operation evaluation value is numerically compared with the preset operation evaluation threshold. If the operation evaluation value exceeds the preset operation evaluation threshold, the operation evaluation symbol LP-1 is assigned; if the operation evaluation value does not exceed the preset operation evaluation threshold, the operation evaluation symbol LP-2 is assigned.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. In the present invention, the user information is collected and the user portrait is formed through the user data collection and analysis module. The home layout design module automatically generates multiple feasible home layout design schemes based on the user portrait and the home product database, and updates the design scheme in real time according to the user's adjustment. After the final home layout design scheme is determined, the user's home product order information is processed through the order management service module, so as to realize the intelligent, personalized and efficient home layout design, significantly shorten the home layout design cycle, and improve the design efficiency and user satisfaction;

[0035] 2. In the present invention, the processing process of the home furnishing product order during the detection period is monitored and analyzed through the order processing evaluation module to accurately feedback the customer service performance and the home furnishing product supply performance. The design operation analysis module analyzes the design operation performance of the indoor home furnishing layout design during the detection period. The comprehensive supervision analysis and early warning module accurately evaluates the difficulty of background supervision based on the analysis results of the order processing evaluation module and the design operation analysis module and issues early warning in time, so as to strengthen the follow-up management in time, which is conducive to improving the reputation of the system application. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0037] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0038] Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1: Figure 1 As shown, the indoor home layout design management system based on big data analysis technology proposed by the present invention includes a user data collection and analysis module, a home product database, a home layout design module, a design preview adjustment module, an order management service module and a background supervision terminal;

[0041] The user sends an interior home layout design request through the user terminal. When the user data collection and analysis module receives the interior home layout design request, it collects the user's basic information (including age, gender, occupation, family structure, living habits and other basic information) and user preferences (including color preferences, home style tendencies, functional requirements, etc.) through user questionnaires, social media behavior analysis and other means, and uses big data analysis technology to deeply mine this information to form a user portrait, and send the user portrait to the home layout design module to provide data support for subsequent design.

[0042] The home product database stores detailed information on various types of home products including furniture, decorations, and home appliances. The stored information includes the size, material, price, style, function and other attributes of the corresponding home products; the home layout design module is based on user portraits and the home product database, using machine learning algorithms and optimization techniques to automatically generate multiple feasible home layout design schemes. It should be noted that during the design process, multiple factors will be considered, such as space utilization, smoothness of traffic lines, and consistency of style, to ensure that the design scheme is both practical and beautiful.

[0043] The design preview adjustment module previews and displays the generated home layout design plan. Users can adjust the home layout by dragging and zooming. The home layout design module updates the design plan in real time according to the user's adjustment, that is, it allows users to adjust the design plan according to their own needs. By providing intuitive design preview, plan adjustment tools and real-time feedback mechanism, it ensures that the final design result fully meets the user's expectations;

[0044] After determining the final home layout design plan, the user places the corresponding home product order through the user terminal. The order management service module is responsible for processing the user's home product order information, including product selection, quantity confirmation, price calculation, etc. It also provides follow-up services such as logistics distribution and installation guidance to ensure that the user can successfully complete the implementation of the home layout.

[0045] Furthermore, the background supervision terminal is connected to the supervision comprehensive analysis and early warning module in communication, and the supervision comprehensive analysis and early warning module is used to set the detection period, preferably, the detection period is forty days; obtain the processing evaluation symbol GX-1 or GX-2 and the operation evaluation symbol LP-1 or LP-2 of the detection period;

[0046] If GX-2∩LP-2 is obtained, it indicates that the management difficulty of the back-end supervisors during the detection period is relatively low, and a low-difficulty supervision signal is generated. The other cases (including GX-1∩LP-1, GX-1∩LP-2 and GX-2∩LP-1) indicate that the management difficulty of the back-end supervisors during the detection period is relatively high, and a high-difficulty supervision signal is generated;

[0047] And the high-difficulty supervision signal or the low-difficulty supervision signal will be sent to the background supervision end. When the background supervision end receives the high-difficulty supervision signal, it will issue a corresponding warning. It can comprehensively analyze and reasonably evaluate the management difficulty of the background supervision personnel during the detection period, and promptly remind the background supervision personnel to strengthen subsequent management, which is conducive to improving the reputation of the system application.

[0048] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the supervision comprehensive analysis and early warning module is connected to the order processing evaluation module in communication. The order processing evaluation module monitors and analyzes the processing process of the household product order during the detection period, assigns a processing evaluation symbol GX-1 or GX-2 through analysis, and sends the processing evaluation symbol GX-1 or GX-2 to the supervision comprehensive analysis and early warning module, which can not only reasonably analyze and accurately feedback the customer service performance and household product supply performance, but also provide information support for the analysis process of the supervision comprehensive analysis and early warning module to ensure the accuracy of its analysis results; the specific analysis process of the order processing evaluation module is as follows:

[0049] When a user inquires about order information, the time when the user inquires is collected and marked as the first time, and the time when the customer service responds is collected and marked as the second time, and the interval between the first time and the second time is marked as the response time; wherein, the larger the value of the response time, the slower the response to the corresponding inquiry;

[0050] Obtain all response times during the detection period, mark the number of response times that exceed the corresponding preset response time threshold as a response abnormality, and calculate the average of all response times during the detection period to obtain a response performance value;

[0051] All customer service response texts during the test period are scanned to identify uncivilized language, and the total number of occurrences of uncivilized language (mainly insulting and abusive words) in the customer service response process during the test period is collected and marked as the uncivilized response frequency;

[0052] By formula The abnormal response value TN, the response performance value TM and the uncivilized response frequency FW are numerically calculated to obtain the customer service management abnormality coefficient YW; wherein sq1, sq2 and sq3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the customer service management abnormality coefficient YW is, the worse the customer service attitude performance is overall, and the more customer supervision needs to be strengthened;

[0053] The customer service management abnormality coefficient YW is numerically compared with the preset customer service management abnormality coefficient threshold. If the customer service management abnormality coefficient YW exceeds the preset customer service management abnormality coefficient threshold, it indicates that the customer service's service attitude performance is generally poor and customer supervision needs to be strengthened in a timely manner, then the processing evaluation symbol GX-1 is assigned.

[0054] Furthermore, if the customer service management abnormality coefficient does not exceed the preset customer service management abnormality coefficient threshold, indicating that the customer service attitude performance is generally good, then the cooperation non-optimal values ​​of all cooperating home furnishing product suppliers are obtained, and the cooperation non-optimal values ​​are numerically compared with the preset cooperation non-optimal threshold. If the cooperation non-optimal value exceeds the preset cooperation non-optimal threshold, the corresponding home furnishing product supplier is marked as a strict supervision object;

[0055] The percentage of home furnishing product suppliers marked as strict supervision objects during the inspection period is obtained and marked as the strict supervision value, and the cooperation non-optimal values ​​of all home furnishing product suppliers are averaged to obtain the cooperation abnormal value, and the cooperation non-optimal value with the largest value is marked as the cooperation abnormal value;

[0056] The supply management abnormality coefficient QMi is obtained by numerically calculating the strict control value QSi, the cooperation abnormality table value QYi and the cooperation abnormality amplitude value QFi through the formula QMi=hu1*QSi+(hu2*QYi+hu3*QFi) / 2; wherein hu1, hu2, hu3 are preset proportional coefficients, hu1>hu2>hu3>0.237; and the larger the value of the supply management abnormality coefficient QMi is, the worse the overall performance of the home furnishing product supply during the detection period is;

[0057] The supply management abnormality coefficient QMi is numerically compared with the preset supply management abnormality coefficient threshold. If the supply management abnormality coefficient QMi exceeds the preset supply management abnormality coefficient threshold, indicating that the supply performance of home products during the detection period is generally poor, then the processing evaluation symbol GX-1 is assigned; if the supply management abnormality coefficient QMi does not exceed the preset supply management abnormality coefficient threshold, indicating that the supply performance of home products during the detection period is generally good, then the processing evaluation symbol GX-2 is assigned.

[0058] It should be noted that the order processing and evaluation module is in communication with the partner detection and analysis module, and the partner detection and analysis module obtains all cooperating home furnishing product suppliers and marks the corresponding home furnishing product supplier as partner i, where i is a natural number greater than 1;

[0059] The performance of the entire supply process of partner i during the detection period is analyzed to obtain the cooperation non-optimal value PXi of partner i, and the cooperation non-optimal value PXi of partner i is sent to the order processing evaluation module, which can not only reasonably evaluate the supply abnormality of each home product supplier during the detection period, but also provide data support for the analysis process of the order processing evaluation module to ensure the comprehensiveness of its analysis and the accuracy of the analysis results; the specific analysis process of the partner detection and analysis module is as follows:

[0060] The number of household product orders involved in the partner i during the detection period is collected and marked as the order number detection value, and the number of times the partner i is complained by users during the detection period is marked as the supply complaint detection value, and the number of times the partner i fails to deliver the corresponding household product to the user's location within the corresponding specified time and the number of times the household product is damaged during the delivery process during the detection period are marked as the product delivery error value and the product delivery damage value respectively;

[0061] By formula The order quantity inspection value SYi, the supply complaint inspection value HNi, the product transportation abnormality value WSi and the product transportation loss value QRi are numerically calculated to obtain the cooperation non-optimal value PXi of partner i; among them, b1, b2, b3, b4 are preset proportional coefficients whose values ​​are greater than zero, and the larger the value of the cooperation non-optimal value PXi, the worse the supply performance of partner i during the inspection period, and the more it is necessary to strengthen the supervision of partner i in the subsequent period.

[0062] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the supervision comprehensive analysis and early warning module is connected to the design operation analysis module in communication, and the design operation analysis module analyzes the design operation performance of the indoor home layout design during the detection period, and assigns the operation evaluation symbol LP-1 or LP-2 through analysis, and sends the operation evaluation symbol LP-1 or LP-2 to the supervision comprehensive analysis and early warning module, which can not only gradually and progressively accurately evaluate the system operation performance during the detection period, but also provide information support for the analysis process of the supervision comprehensive analysis and early warning module, and further ensure the accuracy of its analysis results; the specific analysis process of the design operation analysis module is as follows:

[0063] The number of times the system crashes and cannot operate normally during the detection period is obtained and marked as an abnormal operation inspection value, and the single duration of the system crash and cannot operate normally is marked as an abnormality coefficient, all abnormality coefficients during the detection period are summed up to obtain an abnormality total value, and the number of abnormality coefficients exceeding a preset abnormality coefficient threshold during the detection period is marked as an abnormality risk table value;

[0064] The real-time running number of the design tasks (i.e., the data value of the number of design tasks being processed at the same time) is collected and marked as the running congestion value, and the running congestion value is numerically compared with the preset running congestion threshold. If the running congestion value exceeds the preset running congestion threshold, it is judged that the system is in a non-smooth running state, and the total time of the system in the non-smooth running state during the detection period is obtained and marked as the non-smooth detection value;

[0065] By formula The non-smooth detection value RF, the abnormal operation inspection value RY, the abnormal total condition value RK and the abnormal risk table value RP are numerically calculated to obtain the operation hidden danger value RX, wherein eq1, eq2, eq3 and eq4 are preset proportional coefficients, and eq4>eq2>eq3>eq1>0; and the larger the value of the operation hidden danger value RX is, the greater the overall operation hidden danger of the system during the detection period is, and the more unfavorable it is to ensure the stable and efficient implementation of the indoor home layout design task;

[0066] The operation hazard value RX is numerically compared with the preset operation hazard threshold. If the operation hazard value RX exceeds the preset operation hazard threshold, it indicates that the system operation hazard during the detection period is generally large, which is not conducive to ensuring the stable and efficient implementation of the indoor home layout design task, and the operation evaluation symbol LP-1 is assigned.

[0067] Furthermore, if the operation risk value RX does not exceed the preset operation risk threshold, the design task corresponding to the home layout design request is monitored throughout the entire process, and the number of times the user adjusts the plan within the interval between the time when the request is issued and the time when the final plan is determined is collected and marked as the frequency modulation detection value, and all the frequency modulation detection values ​​within the detection period are averaged to obtain the frequency modulation performance value; and the frequency modulation detection value is numerically compared with the preset frequency modulation detection threshold, and the number of frequency modulation detection values ​​that exceed the preset frequency modulation detection threshold within the detection period is marked as the frequency modulation abnormal value;

[0068] The operation evaluation value HY is obtained by numerically calculating the operation hidden danger value RX, the frequency modulation performance value NP and the frequency modulation abnormality value GP through the formula HY=(uy1*RX+uy3*GP) / uy2+uy2*NP; wherein uy1, uy2 and uy3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the operation evaluation value HY is, the worse the overall system operation performance is during the detection period;

[0069] The operation evaluation value HY is numerically compared with the preset operation evaluation threshold. If the operation evaluation value HY exceeds the preset operation evaluation threshold, indicating that the system operation performance during the detection period is generally poor, then the operation evaluation symbol LP-1 is assigned; if the operation evaluation value HY does not exceed the preset operation evaluation threshold, indicating that the system operation performance during the detection period is generally good, then the operation evaluation symbol LP-2 is assigned.

[0070] The working principle of the present invention is as follows: when in use, the user's basic information and user preferences are collected through the user data collection and analysis module, and the big data analysis technology is used to deeply mine this information to form a user portrait. The home layout design module is based on the user portrait and the home product database, and uses machine learning algorithms and optimization technologies to automatically generate multiple feasible home layout design schemes. The design preview adjustment module previews and displays the generated home layout design scheme. The home layout design module updates the design scheme in real time according to the user's adjustment. After determining the final home layout design scheme, the user's home product order information is processed through the order management service module to realize the intelligent, personalized and efficient home layout design, significantly shorten the home layout design cycle, improve design efficiency and user satisfaction, and reasonably evaluate the management difficulty of the back-end supervisors during the detection period through the comprehensive supervision analysis and early warning module, so as to strengthen the follow-up management in time, which is conducive to improving the reputation of the system application.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An indoor home layout design management system based on big data analysis technology, characterized by: It includes user data collection and analysis module, home furnishing product database, home furnishing layout design module, design preview adjustment module and order management service module; The user sends an indoor home layout design request through the user terminal. When the user data collection and analysis module receives the indoor home layout design request, it uses big data analysis technology to perform in-depth mining to form a user portrait; The home layout design module automatically generates multiple feasible home layout design solutions based on user portraits and home product databases using machine learning algorithms and optimization techniques; The design preview adjustment module previews and displays the generated home layout design plan. The user adjusts the home layout by dragging and zooming. The home layout design module updates the design plan in real time based on the user's adjustments; the order management service module is responsible for processing the user's home product order information.

2. The indoor home layout design management system based on big data analysis technology according to claim 1 is characterized in that: It also includes a background supervision terminal, which is connected to the supervision comprehensive analysis and early warning module in communication. The supervision comprehensive analysis and early warning module is used to set the detection period, obtain the processing evaluation symbol GX-1 or GX-2 and the operation evaluation symbol LP-1 or LP-2 of the detection period; if GX-2∩LP-2 is obtained, a low-difficulty supervision signal is generated, and a high-difficulty supervision signal is generated in other cases, and the high-difficulty supervision signal or the low-difficulty supervision signal is sent to the background supervision terminal.

3. The indoor home layout design management system based on big data analysis technology according to claim 2 is characterized in that: The supervision comprehensive analysis and early warning module is connected to the order processing evaluation module and the design operation analysis module in communication. The order processing evaluation module monitors and analyzes the processing process of the household product order during the detection period to assign a processing evaluation symbol GX-1 or GX-2, and sends the processing evaluation symbol GX-1 or GX-2 to the supervision comprehensive analysis and early warning module; The design operation analysis module analyzes the design operation performance of the interior home layout design during the detection period to assign an operation evaluation symbol LP-1 or LP-2, and sends the operation evaluation symbol LP-1 or LP-2 to the supervision comprehensive analysis and early warning module.

4. The indoor home layout design management system based on big data analysis technology according to claim 3 is characterized in that: The specific analysis process of the order processing evaluation module includes: The customer service management abnormality coefficient is obtained by numerically calculating the response abnormality value, response performance value and uncivilized response frequency. If the customer service management abnormality coefficient exceeds the preset customer service management abnormality coefficient threshold, the processing evaluation symbol GX-1 is assigned.

5. The indoor home layout design management system based on big data analysis technology according to claim 4 is characterized in that: If the customer service management abnormality coefficient does not exceed the preset customer service management abnormality coefficient threshold, the cooperation non-optimal values ​​of all cooperating home product suppliers are obtained, and the supply management abnormality coefficient is obtained by numerically calculating the strict control number inspection value, the cooperation abnormal table value and the cooperation abnormal amplitude value. If the supply management abnormality coefficient exceeds the preset supply management abnormality coefficient threshold, the processing evaluation symbol GX-1 is assigned; If the supply management abnormality coefficient does not exceed the preset supply management abnormality coefficient threshold, a processing evaluation symbol GX-2 is assigned.

6. The indoor home layout design management system based on big data analysis technology according to claim 4 is characterized in that: The order processing and evaluation module is connected to the partner detection and analysis module in communication. The partner detection and analysis module obtains all cooperating home furnishing product suppliers and marks the corresponding home furnishing product supplier as partner i, where i is a natural number greater than 1; The performance of the entire supply process of partner i during the detection period is analyzed, and the cooperation non-optimal value of partner i is obtained by numerically calculating the order number inspection value, supply complaint detection value, product transportation abnormality value and product transportation loss value, and the cooperation non-optimal value of partner i is sent to the order processing evaluation module.

7. The indoor home layout design management system based on big data analysis technology according to claim 3 is characterized in that: The specific analysis process of designing and operating the analysis module includes: The operation hazard value is obtained by numerically calculating the non-smooth detection value, abnormal operation inspection value, abnormal total condition value and abnormal risk table value. If the operation hazard value exceeds the preset operation hazard threshold, the operation assessment symbol LP-1 is assigned.

8. The indoor home layout design management system based on big data analysis technology according to claim 3 is characterized in that: If the operation hazard value does not exceed the preset operation hazard threshold, the operation evaluation value is obtained by numerically calculating the operation hazard value, the frequency regulation performance value and the frequency regulation abnormal value. If the operation evaluation value exceeds the preset operation evaluation threshold, the operation evaluation symbol LP-1 is assigned. If the operation evaluation value does not exceed the preset operation evaluation threshold, an operation evaluation symbol LP-2 is assigned.

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