Wireless communication system of digital ward

Abstract

The invention relates to the technical field of communication processing, in particular to a wireless communication system of a digital ward. The system comprises an acquisition module used for acquiring network usage flow, call times and call moments; the flow analysis module is used for determining a flow demand index according to the change of the network use flow; the call analysis module is used for obtaining a call prediction frequency according to the call frequency change; determining a call frequency index according to the call time interval, further determining a demand weight, and determining a call emergency index according to the call prediction frequency and the demand weight of the ward; the load distribution module is used for determining the network demand degree of the ward for network distribution in combination with the flow demand index and the call emergency index; and carrying out network load distribution in combination with the network demand degrees of all wards. According to the invention, different network loads can be reasonably distributed in combination with the network requirements in the ward, dynamic load adjustment is realized, and the stability effect of wireless communication of the whole digital ward is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication processing, and in particular to a wireless communication system for a digital ward. Background Art

[0002] Hospitals are now gradually introducing intelligent technologies and management methods to build digital wards, automatically collect and process clinical data, reduce the workload of medical staff, and provide patients with a more convenient, safe and comfortable medical environment. When using wireless communication technology to process clinical data in digital wards, it is necessary to achieve dynamic network load balancing. According to the current network load of the digital ward, the load distribution of each network area can be dynamically adjusted to ensure that the network point of each ward can bear the appropriate load and avoid the degradation of network performance caused by overloading of a certain access point.

[0003] In related technologies, a fixed network load is usually evenly distributed based on the number of beds and medical equipment. However, due to changes in the occupancy rate of patients in the ward and the patients' own network needs, network overload may occur, which in turn leads to unreasonable overall load distribution and poor wireless communication stability. Therefore, dynamic load adjustment is urgently needed. Summary of the invention

[0004] In order to solve the technical problem in the related art that the occupancy rate of patients in the ward and the changes in the network demand of the patients themselves may cause network overload, thereby causing unreasonable overall load distribution and poor wireless communication stability, the present invention provides a wireless communication system for a digital ward, and the technical solution adopted is as follows: The present invention proposes a wireless communication system for a digital ward, the system comprising: An acquisition module is used to obtain the network usage flow of each ward every day and the number of calls and call times in each ward; A traffic analysis module, used to perform time series decomposition according to the numerical changes of network usage traffic in the same ward every day, determine trend items and seasonal items, and determine the traffic demand index of the ward according to the fitting slope of the trend item and the peak change of the seasonal item; The call analysis module is used to predict the number of calls on the day according to the change of the number of calls on different days in the ward, and obtain the predicted number of calls; determine the call frequency index of the ward on the previous day according to the call time interval between two adjacent call times of the same ward on the previous day; classify the wards according to the call frequency index of all wards on the previous day to obtain mild frequent wards and severe frequent wards; assign different demand weights to mild frequent wards and severe frequent wards respectively, the demand weight of the mild frequent ward is less than the demand weight of the severe frequent ward, and determine the call urgency index of each ward according to the predicted number of calls and the demand weight of the ward; The load distribution module is used to determine the network demand degree of the ward for network allocation in combination with the flow demand index and the call urgency index; and to distribute the network load in combination with the network demand degrees of all wards.

[0005] Furthermore, the time series decomposition is performed according to the numerical changes of the network usage flow in the same ward every day to determine the trend item and the seasonal item, including: A two-dimensional coordinate system is constructed with time as the horizontal axis and the network usage flow value as the vertical axis. The coordinate points of the network usage flow for each day are determined, and the flow curve is obtained by connecting the coordinate points in time sequence. The flow curve is decomposed into time series based on the STL time series decomposition algorithm to determine the trend item and the seasonal item of the flow curve.

[0006] Furthermore, according to the fitting slope of the trend term and the peak change of the seasonal term, the flow demand index of the ward is determined, including: Performing least squares fitting on the trend term, and using the slope obtained after fitting as the fitting slope; Determine the peak data of the seasonal item, and use the absolute value of the difference between the two peak data closest to each other in time series as the peak change index corresponding to the two peak data; calculate the mean of all peak data and the mean of all peak change indexes, and use the product of the two means as the peak influence coefficient; The product of the peak impact coefficient and the fitting slope is calculated, and the maximum and minimum values ​​are normalized to obtain the flow demand index.

[0007] Furthermore, the method predicts the number of calls on the day according to the change of the number of calls on different days in the ward to obtain the predicted number of calls, including: The number of calls on different days is fitted based on the least square method, and the number of calls on the day is predicted according to the fitting results to obtain the predicted number of calls.

[0008] Further, determining the call frequency index of the ward on the previous day according to the call time interval between two adjacent call times of the same ward on the previous day includes: Calculate the average of the time intervals between all two adjacent call times in the same ward on the previous day to obtain the average call interval; The reciprocal of the calling interval mean is taken as the calling frequency index.

[0009] Furthermore, the wards are classified according to the call frequency index of all wards in the previous day to obtain mild frequent wards and severe frequent wards, including: The call frequency index is clustered based on the k-means algorithm, and the k value is set to 2, resulting in two clusters; The mean of all call frequency indicators in each cluster is calculated, wherein the beds corresponding to the clusters with larger mean values ​​of call frequency indicators are severe frequent wards, and the beds corresponding to the clusters with smaller mean values ​​of call frequency indicators are mild frequent wards.

[0010] Furthermore, the step of assigning different demand weights to the mild frequent wards and the severe frequent wards respectively includes: The demand weight of the mild frequent ward is set to 0.5, and the demand weight of the severe frequent ward is set to 1.

[0011] Further, the call urgency index of each ward is determined according to the predicted number of calls and the demand weight of the ward, including: The product of the predicted number of calls and the demand weight of the ward is calculated, and the maximum and minimum values ​​are normalized to obtain a call urgency index.

[0012] Further, combining the flow demand index and the call urgency index, determining the network demand degree of the ward for network allocation includes: The product of the flow demand index and the call urgency index is calculated to obtain the network demand degree.

[0013] Furthermore, the network load is distributed according to the network demand of all wards, including: Calculate the sum of the network demand levels of all wards as the total demand level; The ratio of the network demand level of any ward to the total demand level is taken as the load demand ratio; The product of the current allocable load and the load demand ratio of each ward is used as the load to be allocated for each ward; Load distribution is achieved based on the load to be distributed in all wards.

[0014] The present invention has the following beneficial effects: The present invention realizes the network load distribution of wireless communication through two dimensions: network usage flow and bed call situation; by obtaining the network usage flow of each ward every day and the number of calls and call time of the ward in each ward, a data basis for network load distribution analysis is provided; then, the numerical change of network usage flow is decomposed in time series, the trend item and the seasonal item are determined, and the flow demand index of the ward is determined according to the fitting slope of the trend item and the peak change of the seasonal item, and the flow demand index accurately represents the flow fluctuation and the demand degree of the flow body; then, according to the distribution and numerical change of the number of calls, a prediction analysis and weight assignment are performed to determine the call emergency index, and the call emergency represents the timeliness demand of the call; finally, the network demand degree of the ward for network allocation is determined by combining the flow demand index and the call emergency index; and the network load distribution is performed in combination with the network demand degree of all wards. Therefore, the present invention can realize the dynamic distribution of the network under the same total load, that is, in combination with the network demand of the ward itself, different network loads are reasonably distributed, so as to realize dynamic load adjustment and improve the stability effect of wireless communication of the overall digital ward. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A structural diagram of a wireless communication system for a digital ward provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation, structure, features and effects of a wireless communication system for a digital ward proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the wireless communication system for a digital ward provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a structure diagram of a wireless communication system of a digital ward provided by an embodiment of the present invention, the system comprising: The acquisition module 101 is used to acquire the network usage flow of each ward every day and the number of calls and call time in each ward.

[0021] The scenario of the embodiment of the present invention is a ward communication scenario. It should be noted that the daily network usage flow in each ward is the network traffic consumption of all personnel in each ward. Since the onset of symptoms is different in different periods, there will be relatively large differences in the number of patients in the ward and the use of medical resources, resulting in different network consumption in different departments and wards in different areas of the hospital. The flow data of wards in each area can be obtained by monitoring and analyzing network traffic using tools such as Wireshark and NetFlow Analyzer.

[0022] At the same time, due to digital management, each bed in the ward is controlled by an intelligent call system. The bedside care system can be used to determine whether there are patients in each bed. At the same time, it is matched with the ward bracelet call answering system. The patient's bedside transmitter is divided into two wireless buttons, one for the nurse and the other for the caregiver, to achieve point-to-point precise calling. The system will record the patients in the bed and the call situation. The present invention mainly records the number of calls and the time of each call for all beds in a ward, where the time of each call is used as the call time.

[0023] The traffic analysis module 102 is used to perform time series decomposition based on the numerical changes in the network usage traffic in the same ward every day, determine the trend item and the seasonal item, and determine the traffic demand index of the ward based on the fitting slope of the trend item and the peak change of the seasonal item.

[0024] Since the numerical fluctuations of network traffic usage are large and the overall fluctuation is sawtooth-shaped, feature decomposition is required to better analyze the characteristics of traffic fluctuations.

[0025] Furthermore, in some embodiments of the present invention, time series decomposition is performed based on the numerical changes in the network usage traffic in the same ward every day to determine trend items and seasonal items, including: constructing a two-dimensional coordinate system with time as the horizontal coordinate and the network usage traffic value as the vertical coordinate, determining the coordinate points of the network usage traffic for each day, and connecting the coordinate points in time series to obtain a traffic curve; performing time series decomposition on the traffic curve based on the STL time series decomposition algorithm to determine the trend item and seasonal item of the traffic curve.

[0026] In the embodiment of the present invention, the characteristic decomposition can be specifically an STL time series decomposition algorithm, which can decompose the flow curve into a trend term, a seasonal term and a residual term, wherein the trend term represents the overall numerical change trend, and the seasonal term represents the periodic change situation.

[0027] Therefore, in the embodiment of the present invention, the fitting slope of the trend term represents the overall trend characteristics, while the peak change of the season term represents the periodic peak impact characteristics. The flow demand of the ward can be analyzed by combining these two characteristics.

[0028] Furthermore, in some embodiments of the present invention, the flow demand index of the ward is determined based on the fitting slope of the trend item and the peak change of the seasonal item, including: performing least squares fitting on the trend item, and taking the slope obtained after fitting as the fitting slope; determining the peak data of the seasonal item, and taking the absolute value of the difference between the two peak data closest to each other in time series as the peak change index corresponding to the two peak data; calculating the mean of all peak data and the mean of all peak change indicators, and taking the product of the two means as the peak influence coefficient; calculating the product of the peak influence coefficient and the fitting slope, and normalizing the maximum and minimum values ​​to obtain the flow demand index.

[0029] Among them, the least squares method performs a straight line fit on the trend item to obtain a fitting straight line for the trend item, and the slope of the fitting straight line for the trend item is used as the fitting slope. The fitting slope represents the change in the overall flow demand. The larger the fitting slope value, the increased flow demand, and the smaller the fitting slope value, the decreased flow demand.

[0030] Among them, the seasonal item manifests as periodic fluctuations, that is, its fluctuations have peak values. The data value corresponding to the peak value is taken as the peak data. The absolute value of the difference between the two peak data closest to each other in time series represents the change of the two adjacent peak data in time series. The larger the value of the peak change index, the greater the flow fluctuation in the ward. Regardless of whether the flow usage suddenly increases or decreases, abnormal analysis is required. In other words, the larger the value of the peak change index, the more abnormal the overall flow fluctuation is.

[0031] Among them, the mean of all peak data represents the characteristics of the flow size, and the mean of all peak change indicators represents the abnormal characteristics of flow fluctuation. Therefore, the product of the two means is directly calculated to obtain the peak influence coefficient. The larger the value of the peak influence coefficient, the larger the overall flow peak and the greater the change. Therefore, the greater the overall demand for flow.

[0032] Furthermore, the larger the value of the fitting slope, the more traffic demand increases, while the smaller the value of the fitting slope, the less traffic demand decreases; the larger the value of the peak impact coefficient, the larger the overall traffic peak and the greater the change, thus, the greater the overall demand for traffic. Directly calculate the product of the peak impact coefficient and the fitting slope, and normalize the maximum and minimum values ​​to obtain the traffic demand index.

[0033] Among them, the traffic demand index represents the degree of demand determined according to traffic changes. The larger the value of the traffic demand index is, the higher the traffic load required to achieve overall communication stability in the corresponding ward.

[0034] The call analysis module 103 is used to predict the number of calls on the day according to the changes in the number of calls on different days in the ward, and obtain the predicted number of calls; determine the call frequency index of the ward on the previous day according to the call time interval between two adjacent call times of the same ward on the previous day; classify the wards according to the call frequency index of all wards on the previous day to obtain mildly frequent wards and severely frequent wards; assign different demand weights to mildly frequent wards and severely frequent wards respectively, the demand weight of mildly frequent wards is less than the demand weight of severely frequent wards, and determine the call urgency index of each ward according to the predicted number of calls and the demand weight of the ward.

[0035] Among them, since the bed occupancy rate in the ward is uncertain, and the conditions of patients in different beds are uncertain, the frequency of care required by relevant nurses or care workers is uncertain. Therefore, it is impossible to directly combine multiple data to analyze the call situation. In the embodiment of the present invention, the number of calls for multiple days before the current day can be analyzed, so as to make a fitting prediction for the number of calls on the current day, which can serve as a highly reliable data reference.

[0036] In an embodiment of the present invention, the number of calls for the day can be predicted directly based on the changes in the number of calls on different days in the ward. Furthermore, in some embodiments of the present invention, the specific prediction method includes: fitting the number of calls on different days based on the least squares method, predicting the number of calls for the day based on the fitting results, and obtaining the predicted number of calls.

[0037] Among them, the least square method can analyze the overall value to obtain a more accurate and reliable call prediction number. This prediction method takes into account the overall change in the number of calls, making the prediction value more accurate and reliable.

[0038] After analyzing the number of calls, it is also necessary to predict the call frequency characteristics. It is understandable that since the ward is usually called during the day, there are also call needs at night, but the number is lower. Therefore, the call frequency characteristics analysis is directly performed based on the time interval between two adjacent calls. That is, based on the call time interval between two adjacent call times of the same ward on the previous day, the call frequency index of the ward on the previous day is determined.

[0039] Furthermore, in some embodiments of the present invention, a specific method for determining a call frequency index includes: calculating the average of the time intervals between all two adjacent call times in the same ward on the previous day to obtain a call interval average; and using the inverse of the call interval average as a call frequency index.

[0040] Since the smaller the mean value of the call interval is, the more frequent the overall calls are, therefore, in the embodiment of the present invention, the inverse of the mean value of the call interval is directly calculated as the call frequency index.

[0041] It can be understood that the larger the value of the call frequency index is, the more frequent the calls are, which means that the timeliness and reliability of communication are more required. Therefore, wards can be directly classified according to the call frequency index.

[0042] Furthermore, in some embodiments of the present invention, the wards are classified according to the call frequency index of all wards on the previous day to obtain mildly frequent wards and severely frequent wards, including: clustering the call frequency index based on the k-means algorithm, setting the k value to 2, and obtaining two clusters; calculating the mean of all call frequency indexes in each cluster, wherein the beds corresponding to the clusters with larger mean values ​​of the call frequency index are severely frequent wards, and the beds corresponding to the clusters with smaller mean values ​​of the call frequency index are mildly frequent wards.

[0043] Clustering is achieved by the k-means algorithm, and the k value is set to 2. Thus, all wards are divided into two categories directly based on the numerical value of the call frequency index, one of which is represented by a larger numerical value and the other is represented by a smaller numerical value. In the embodiment of the present invention, the beds corresponding to the clusters represented by the larger numerical values ​​are the severe frequent call wards, and the beds corresponding to the clusters represented by the smaller numerical values ​​are the mild frequent call wards.

[0044] For the performance analysis of the specific numerical values ​​within the clusters, the mean is calculated directly, that is, by calculating the mean of all call frequency indicators in each cluster, the clusters with larger means correspond to the severe frequent wards, and the clusters with smaller means correspond to the mild frequent wards.

[0045] It is understandable that the severe frequent ward has a greater demand for network timeliness and the call is more urgent. In the embodiment of the present invention, different demand weights can be assigned to the severe frequent ward and the mild frequent ward, and the demand weight of the severe ward is greater than that of the mild ward. Therefore, in the embodiment of the present invention, the demand weight of the mild frequent ward is set to 0.5, and the demand weight of the severe frequent ward is set to 1.

[0046] Furthermore, in some embodiments of the present invention, a call urgency index of each ward is determined based on the predicted number of calls and the demand weight of the ward, including: calculating the product of the predicted number of calls and the demand weight of the ward, and normalizing the maximum and minimum values ​​to obtain the call urgency index.

[0047] In the embodiment of the present invention, the predicted number of calls represents the forecast for the next day, and is directly multiplied by the demand weight to obtain an analysis of the call urgency characteristics, that is, the larger the value of the call urgency index, the more predicted calls and the more frequent the calls.

[0048] The load distribution module 104 is used to determine the network demand degree of the ward for network allocation in combination with the flow demand index and the call urgency index; and to distribute the network load in combination with the network demand degrees of all wards.

[0049] In an embodiment of the present invention, communication network allocation can be performed for wards in combination with traffic demand and call demand. Traffic demand represents the overall communication volume demand, while call demand represents the timeliness demand of communication. Therefore, in an embodiment of the present invention, network allocation is performed for each ward in combination with traffic demand index and call urgency index.

[0050] Furthermore, in some embodiments of the present invention, the flow demand index and the call urgency index are combined to determine the network demand degree of the ward for network allocation, including: calculating the product of the flow demand index and the call urgency index to obtain the network demand degree.

[0051] The larger the values ​​of the flow demand index and the call urgency index are, the greater the demand for the network is. The product of the flow demand index and the call urgency index is directly calculated to obtain the network demand degree.

[0052] Furthermore, in some embodiments of the present invention, network load distribution is performed in combination with the network demand levels of all wards, including: calculating the sum of the network demand levels of all wards as the total demand level; taking the ratio of the network demand level of any ward to the total demand level as the load demand proportion; taking the product of the current allocable load and the load demand proportion of each ward as the load to be allocated in each ward; and achieving load distribution according to the load to be allocated of all wards.

[0053] By normalizing the load demand ratio, the load to be allocated in each ward is determined, and the load distribution in each ward is achieved based on the load to be allocated. This makes it easier to allocate more network load to wards with greater network demand and fluctuations, thereby improving the stability of wireless communication in the overall digital ward.

[0054] The present invention realizes the network load distribution of wireless communication through two dimensions: network usage flow and bed call situation; by obtaining the network usage flow of each ward every day and the number of calls and call time of the ward in each ward, a data basis for network load distribution analysis is provided; then, the numerical change of network usage flow is decomposed in time series, the trend item and the seasonal item are determined, and the flow demand index of the ward is determined according to the fitting slope of the trend item and the peak change of the seasonal item, and the flow demand index accurately represents the flow fluctuation and the demand degree of the flow body; then, according to the distribution and numerical change of the number of calls, a prediction analysis and weight assignment are performed to determine the call emergency index, and the call emergency represents the timeliness demand of the call; finally, the network demand degree of the ward for network allocation is determined by combining the flow demand index and the call emergency index; and the network load distribution is performed in combination with the network demand degree of all wards. Therefore, the present invention can realize the dynamic distribution of the network under the same total load, that is, in combination with the network demand of the ward itself, different network loads are reasonably distributed, so as to realize dynamic load adjustment and improve the stability effect of wireless communication of the overall digital ward.

[0055] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A wireless communication system for a digital ward, characterized in that: The system comprises: An acquisition module is used to obtain the network usage flow of each ward every day and the number of calls and call times in each ward; A traffic analysis module, used to perform time series decomposition according to the numerical changes of network usage traffic in the same ward every day, determine trend items and seasonal items, and determine the traffic demand index of the ward according to the fitting slope of the trend item and the peak change of the seasonal item; The call analysis module is used to predict the number of calls on the day according to the change of the number of calls on different days in the ward, and obtain the predicted number of calls; determine the call frequency index of the ward on the previous day according to the call time interval between two adjacent call times of the same ward on the previous day; classify the wards according to the call frequency index of all wards on the previous day to obtain mild frequent wards and severe frequent wards; assign different demand weights to mild frequent wards and severe frequent wards respectively, the demand weight of the mild frequent ward is less than the demand weight of the severe frequent ward, and determine the call urgency index of each ward according to the predicted number of calls and the demand weight of the ward; The load distribution module is used to determine the network demand degree of the ward for network allocation in combination with the flow demand index and the call urgency index; and to distribute the network load in combination with the network demand degrees of all wards.

2. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: The time series decomposition is performed based on the numerical changes of the network usage flow in the same ward every day to determine the trend item and the seasonal item, including: A two-dimensional coordinate system is constructed with time as the horizontal axis and the network usage flow value as the vertical axis. The coordinate points of the network usage flow for each day are determined, and the flow curve is obtained by connecting the coordinate points in time sequence. The flow curve is decomposed into time series based on the STL time series decomposition algorithm to determine the trend item and the seasonal item of the flow curve.

3. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: According to the fitting slope of the trend term and the peak change of the seasonal term, the flow demand index of the ward is determined, including: Performing least squares fitting on the trend term, and using the slope obtained after fitting as the fitting slope; Determine the peak data of the seasonal item, and use the absolute value of the difference between the two peak data closest to each other in time series as the peak change index corresponding to the two peak data; calculate the mean of all peak data and the mean of all peak change indexes, and use the product of the two means as the peak influence coefficient; The product of the peak impact coefficient and the fitting slope is calculated, and the maximum and minimum values ​​are normalized to obtain the flow demand index.

4. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: The method predicts the number of calls on the day according to the change of the number of calls on different days in the ward to obtain the predicted number of calls, including: The number of calls on different days is fitted based on the least square method, and the number of calls on the day is predicted according to the fitting results to obtain the predicted number of calls.

5. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: Determining the call frequency index of the ward on the previous day according to the call time interval between two adjacent call times of the same ward on the previous day includes: Calculate the average of the time intervals between all two adjacent call times in the same ward on the previous day to obtain the average call interval; The reciprocal of the calling interval mean is taken as the calling frequency index.

6. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: The wards are classified according to the call frequency index of all wards on the previous day to obtain mild frequent wards and severe frequent wards, including: The call frequency index is clustered based on the k-means algorithm, and the k value is set to 2, resulting in two clusters; The mean of all call frequency indicators in each cluster is calculated, wherein the beds corresponding to the clusters with larger mean values ​​of call frequency indicators are severe frequent wards, and the beds corresponding to the clusters with smaller mean values ​​of call frequency indicators are mild frequent wards.

7. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: Different demand weights are assigned to the mild frequent wards and the severe frequent wards, including: The demand weight of the mild frequent ward is set to 0.5, and the demand weight of the severe frequent ward is set to 1.

8. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: The call urgency index of each ward is determined according to the predicted number of calls and the demand weight of the ward, including: The product of the predicted number of calls and the demand weight of the ward is calculated, and the maximum and minimum values ​​are normalized to obtain a call urgency index.

9. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: Determining the degree of network demand of the ward for network allocation in combination with the flow demand index and the call urgency index includes: The product of the flow demand index and the call urgency index is calculated to obtain the network demand degree.

10. A wireless communication system for a digital ward as claimed in claim 1, characterized in that: The network load is distributed according to the network demand of all wards, including: Calculate the sum of the network demand levels of all wards as the total demand level; The ratio of the network demand level of any ward to the total demand level is taken as the load demand ratio; The product of the current allocable load and the load demand ratio of each ward is used as the load to be allocated for each ward; Load distribution is achieved based on the load to be distributed in all wards.

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